From 6bf43fad88143110b10cd874c4e57460dbfb8f06 Mon Sep 17 00:00:00 2001
From: Jeff Soules <jsoules@flatironinstitute.org>
Date: Fri, 16 Jul 2021 14:20:37 -0400
Subject: [PATCH 1/3] Add infrastructure for better conversion of markdown to
 styled website text. Add Recordings page with live copy of API StudySet data.

---
 src/App.tsx                                   |  34 ++
 src/calc/jsonTypes.ts                         |   2 +-
 .../Recordings/RecordingPageContent.md        |  24 ++
 src/components/Recordings/RecordingsPage.tsx  | 376 ++++++++++++++++++
 src/components/util.ts                        |   2 +-
 src/sampleData/studysets-from-api.json        |   1 +
 6 files changed, 437 insertions(+), 2 deletions(-)
 create mode 100644 src/components/Recordings/RecordingPageContent.md
 create mode 100644 src/components/Recordings/RecordingsPage.tsx
 create mode 100644 src/sampleData/studysets-from-api.json

diff --git a/src/App.tsx b/src/App.tsx
index 6d20cb3..ef999a1 100644
--- a/src/App.tsx
+++ b/src/App.tsx
@@ -2,12 +2,14 @@ import React from "react"
 import { Card, Container } from "react-bootstrap"
 import { BrowserRouter } from "react-router-dom"
 import "./App.css"
+import { StudySet } from './calc/jsonTypes'
 import About from "./components/About/About"
 import Algorithms, { AlgorithmEntry } from "./components/Algorithms/Algorithms"
 // import ConfigPanelWrapper from "./components/ConfigPanelWrapper"
 import ExpandingHeatmapTable from "./components/ExpandingHeatmapTable"
 import Footer from './components/Footer/Footer'
 import Header from './components/Header/Header'
+import Recordings from './components/Recordings/RecordingsPage'
 import Preloader from "./components/Shared/Preloader"
 // import MetricsDescription from "./components/MetricsDescription/MetricsDescription"
 import "./index.css"
@@ -16,6 +18,7 @@ import {
     expandingHeatmapTableSampleHeader as header,
     expandingHeatmapTableSampleRows as tableRows
 } from "./sampleData/expandingHeatmapTableTestData"
+import studysetsjson from './sampleData/studysets-from-api.json'
 
 
 // import { basicConfig } from "./sampleData/HeatmapConfigTestData"
@@ -23,6 +26,35 @@ import {
 function App() {
   // const columnConfig = {...basicConfig, useColumnFormat: true}
   // const cpuConfig = {...basicConfig, format: 'cpu' as FormatType, showCPU: true }
+
+  // NOTE: this is for testing, the data will actually need to be pulled from an API
+  const studysets: StudySet[] = studysetsjson.map((s) => { return {
+        _id: s._id,
+        name: s.name,
+        description: s.description,
+        studies: s.studies.map((study) => {
+            return {
+                recordings: study.recordings.map((recording) => {return {
+                    _id: recording._id,
+                    name: recording.name,
+                    studyName: recording.studyName,
+                    studySetName: recording.studySetName,
+                    directory: recording.directory,
+                    firingsTrue: recording.firingsTrue,
+                    sampleRateHz: recording.sampleRateHz,
+                    numChannels: recording.numChannels,
+                    durationSec: recording.durationSec,
+                    numTrueUnits: recording.numTrueUnits,
+                    spikeSign: recording.spikeSign > 0 ? 1 : -1
+                }}),
+                _id: study._id,
+                name: study.name,
+                studySetName: study.studySetName
+            }
+        })
+      }
+    })
+
   return (
     <BrowserRouter>
         <div className="wrapper">
@@ -41,6 +73,8 @@ function App() {
             </div>
         </div>
         <div className="wrapper">
+            <hr />
+                <Recordings Sets={studysets} />
             {/* <div>
                 <hr />
                 <ConfigPanelWrapper />
diff --git a/src/calc/jsonTypes.ts b/src/calc/jsonTypes.ts
index ccd1ac5..29d9976 100644
--- a/src/calc/jsonTypes.ts
+++ b/src/calc/jsonTypes.ts
@@ -25,7 +25,7 @@ export interface StudySet {
     name: string,
     description: string,
     studies: Study[],
-    __v: number
+    __v?: number
 }
 
 export type Nullable<T> = T extends null | undefined ? never: T
diff --git a/src/components/Recordings/RecordingPageContent.md b/src/components/Recordings/RecordingPageContent.md
new file mode 100644
index 0000000..4d14b66
--- /dev/null
+++ b/src/components/Recordings/RecordingPageContent.md
@@ -0,0 +1,24 @@
+# Recordings
+
+## Recording Organization
+
+Recordings are grouped into "studies." Each study contains a set of real or synthesized
+recordings sharing a common source (probe and brain region, or simulation code
+settings). It is appropriate to aggregate the statistics from all recordings within a
+particular study.
+
+In turn, studies are grouped into "study sets." Each study set is a collection of studies
+which have different parameters but share some common features.
+
+## Recording Types
+
+Our hosted recordings include many popular probe geometries and types, and fall
+into three cartegories:
+
+1. **Paired (*in vivo* or *in vitro*)** Recordings from various laboratories
+where an independent intra- or juxta-cellular probe providers reliable ground
+truth firing events, usually for one neuron per recording.
+1. **Simulated (*in silico*)** Recordings from neural simulator codes with
+various degrees of fidelity, with known firing events for large numbers of
+neurons.
+1. **Human-curated** We also host a small set of expert human-curated sorting results.
diff --git a/src/components/Recordings/RecordingsPage.tsx b/src/components/Recordings/RecordingsPage.tsx
new file mode 100644
index 0000000..574a45c
--- /dev/null
+++ b/src/components/Recordings/RecordingsPage.tsx
@@ -0,0 +1,376 @@
+import React, { Fragment, FunctionComponent, useEffect, useState } from "react"
+import { Col, Container, Row, Table } from "react-bootstrap"
+import ReactMarkdown from "react-markdown"
+import { Link } from "react-router-dom"
+import { Study, StudySet } from '../../calc/jsonTypes'
+import Preloader from "../Shared/Preloader"
+import Sidebar from "../Shared/Sidebar"
+import { Copy, getCanonicalSectionTag, parseMarkdownToContentCards, Section, toTitleCase } from "../util"
+import pageContent from './RecordingPageContent.md'
+
+
+type Studies = { Studies: Study[] }
+type StudySets = { Sets: StudySet[] }
+type EnhancedCopy = Copy & { Loading: boolean, StudySets: StudySets }
+
+const getCanonicalStudySetName = (rawName: string): string => (
+    toTitleCase(rawName.replace(/_/g, " ").toLowerCase())
+)
+
+const Recordings: FunctionComponent<StudySets> = (Props: StudySets) => {
+    const [copyMd, setCopyMd] = useState('loading...')
+    useEffect(() => {
+        fetch(pageContent)
+        .then((res) => res.text())
+        .then((text) => setCopyMd(text))
+    })
+    const loading = !(Props.Sets && Props.Sets.length > 0)
+    const content: Copy = parseMarkdownToContentCards(copyMd)
+    const studySetSidebarEntries: Section[] = Props.Sets.map((ss) => {
+        const name = getCanonicalStudySetName(ss.name)
+        return {
+            header: '',
+            content: '',
+            label: name,
+            tag: getCanonicalSectionTag(ss.name),
+        }
+    })
+
+    return (
+        <Container className="container-sidebar">
+            <Row noGutters>
+                <PageSidebar
+                    title={content.title}
+                    sections={[...content.sections, ...studySetSidebarEntries]} />
+                <PageCopy 
+                    title={content.title}
+                    sections={content.sections}
+                    Loading={loading}
+                    StudySets={Props} />
+            </Row>
+        </Container>
+    )
+}
+
+const PageSidebar: FunctionComponent<Copy> = (Props: Copy) => {
+    const sidebarItems = Props.sections.map(
+        (section) => {
+            return {
+                name: section.label,
+                value: section.tag
+            }
+        })
+    return (
+        <Col xl={2} md={3} sm={12} className="sidebar">
+            <Sidebar listItems={sidebarItems} listTitle={Props.title} />
+        </Col>
+    )
+}
+
+const PageCopy: FunctionComponent<EnhancedCopy> = (Props: EnhancedCopy) => {
+    return (
+        <Col xl={10} md={9} sm={12} className="page__body">
+            <Container className="container__heatmap">
+                <Row className="subcontainer justify-content-md-center">
+                    <Col lg={12} sm={12} xl={12}>
+                        <div className="intro">
+                            <p className="big">{Props.title}</p>
+                        </div>
+                    </Col>
+                </Row>
+                { Props.sections.map((section) => (<StaticCard {...section} />)) }
+                { Props.Loading 
+                    ? <PreloaderCard />
+                    : <div>
+                        { Props.StudySets.Sets.map((studyset) => (<StudySetCard {...studyset} />))}
+                      </div>
+                }
+                <div style={{ margin: "8rem 0" }} />
+            </Container>
+        </Col>
+    )
+}
+
+const StaticCard: FunctionComponent<Section> = (Props: Section) => {
+    return (
+        <Fragment>
+            <div className="finder" id={Props.tag} />
+            <Row className="subcontainer justify-content-md-center">
+                <Col lg={12} sm={12} xl={12}>
+                    <div className="card card__std">
+                        <div className="content">
+                            <div className="card__label">
+                                <p>
+                                    <strong>{Props.header}</strong>
+                                </p>
+                            </div>
+                            <div className="card__footer">
+                                <hr />
+                                <ReactMarkdown children={Props.content} />
+                            </div>
+                        </div>
+                    </div>
+                </Col>
+            </Row>
+        </Fragment>
+    )
+}
+
+const PreloaderCard: FunctionComponent = () => {
+    return (
+        <Row className="subcontainer justify-content-md-center">
+            <Col lg={12} sm={12} xl={12}>
+                <div className="card card__std">
+                    <div className="content">
+                        <Preloader />
+                    </div>
+                </div>
+            </Col>
+        </Row>        
+    )
+}
+
+const StudySetCard: FunctionComponent<StudySet> = (Props: StudySet) => {
+    return (
+        // hard-coded 'study-set-' is just arbitrary text--we're only
+        // trying to provide a unique key property value for React.
+        <div key={`study-set-${Props.name}`}>
+            <Row className="subcontainer justify-content-md-center">
+                <Col lg={12} sm={12} xl={12}>
+                    <div className="finder" id={getCanonicalSectionTag(Props.name)} />
+                    <div className="listcard listcard-recording">
+                        <div className="listcard-content">
+                            <div className="listcard-section">
+                                <Link to={`/studyset/${Props.name}`} className="listcard-title">
+                                    {Props.name}
+                                </Link>
+                            </div>
+                            <div className="listcard-section">
+                                <StudyTable Studies={Props.studies} />
+                            </div>
+                        </div>
+                    </div>
+                </Col>
+            </Row>
+        </div>
+    )
+}
+
+const StudyTable: FunctionComponent<Studies> = (Props: Studies) => {
+    return (
+        <Table striped bordered size="sm">
+            <thead>
+                <tr>
+                    <th key="col1">Study name</th>
+                    <th key="col2">Number of recordings</th>
+                    <th key="col3"> </th>
+                </tr>
+            </thead>
+            <tbody>
+                { Props.Studies.map((study) => (<StudyTableRow {...study} />)) }
+            </tbody>
+        </Table>
+    )
+}
+
+const StudyTableRow: FunctionComponent<Study> = (Props: Study) => {
+    return (
+        <tr key={`study-${Props.name}`}>
+            <td key="col1">{Props.name}</td>
+            <td key="col2">{Props.recordings.length}</td>
+            <td key="col3" className="listcard-link">
+                <Link to={`/study/${Props.name}`} className="listcard-env">
+                    View study details
+                </Link>
+            </td>
+        </tr>
+    )
+}
+
+// class OldRecordings extends Component {
+//   render() {
+//     let loading = isEmpty(this.props.studySets);
+//     let sidebarItems = [];
+//     if (this.props.studySets) {
+//       sidebarItems = this.props.studySets.map(item => ({
+//         name: toTitleCase(item.name.replace(/_/g, " ").toLowerCase()),
+//         value: item.name
+//       }));
+//     }
+//     sidebarItems.unshift(
+//       { name: "Recording Organization", value: "recordingorganization" },
+//       { name: "Recording Types", value: "recordingtypes" }
+//     );
+//     return (
+//       <Container className="container-sidebar">
+//         <Row noGutters>
+//           <Col xl={2} md={3} sm={12} className="sidebar">
+//             <Sidebar listItems={sidebarItems} listTitle={"Recordings"} />
+//           </Col>
+//           <Col xl={10} md={9} sm={12} className="page__body">
+//             <Container className="container__heatmap">
+//               <Row className="subcontainer justify-content-md-center">
+//                 <Col lg={12} sm={12} xl={12}>
+//                   <div className="intro">
+//                     <p className="big">Recordings</p>
+//                   </div>
+//                 </Col>
+//               </Row>
+//               <div className="finder" id="recordingorganization" />
+//               <Row className="subcontainer justify-content-md-center">
+//                 <Col lg={12} sm={12} xl={12}>
+//                   <div className="card card__std">
+//                     <div className="content">
+//                       <div className="card__label">
+//                         <p>
+//                           <strong>Recording Organization</strong>
+//                         </p>
+//                       </div>
+//                       <div className="card__footer">
+//                         <hr />
+//                         <p>
+//                           Recordings are grouped into "studies". Each study
+//                           contains a set of real or synthesized recordings
+//                           sharing a common source (probe and brain region, or
+//                           simulation code settings). It is appropriate to
+//                           aggregate the statistics from all recordings within a
+//                           particular study.
+//                         </p>
+//                         <p>
+//                           In turn, studies are grouped into "study sets". Each
+//                           study set is a collection of studies which have
+//                           different parameters but share some common features.
+//                         </p>
+//                       </div>
+//                     </div>
+//                   </div>
+//                 </Col>
+//               </Row>
+//               <div className="finder" id="recordingtypes" />
+//               <Row className="subcontainer justify-content-md-center">
+//                 <Col lg={12} sm={12} xl={12}>
+//                   <div className="card card__std">
+//                     <div className="content">
+//                       <div className="card__label">
+//                         <p>
+//                           <strong>Recording Types</strong>
+//                         </p>
+//                       </div>
+//                       <div className="card__footer">
+//                         <hr />
+//                         <p>
+//                           Our hosted recordings include many popular probe
+//                           geometries and types, and fall into three categories:
+//                         </p>
+//                         <div className="list__section">
+//                           <span className="list__heading">
+//                             1. Paired (<i>in vivo</i> or <i>in vitro</i>)
+//                           </span>
+//                           <span className="list__body">
+//                             Recordings from various laboratories where an
+//                             independent intra- or juxta-cellular probe provides
+//                             reliable ground truth firing events, usually for one
+//                             neuron per recording.
+//                           </span>
+//                         </div>
+//                         <div className="list__section">
+//                           <span className="list__heading">
+//                             2. Simulated (in silico)
+//                           </span>
+//                           <span className="list__body">
+//                             Recordings from neural simulator codes with various
+//                             degrees of fidelity, with known firing events for
+//                             large numbers of neurons.
+//                           </span>
+//                         </div>
+//                         <div className="list__section">
+//                           <span className="list__heading">
+//                             3. Human-curated
+//                           </span>
+//                           <span className="list__body">
+//                             We also host a small set of expert human-curated
+//                             sorting results.
+//                           </span>
+//                         </div>
+//                       </div>
+//                     </div>
+//                   </div>
+//                 </Col>
+//               </Row>
+//               {loading ? (
+//                 <Row className="subcontainer justify-content-md-center">
+//                   <Col lg={12} sm={12} xl={12}>
+//                     <div className="card card__std">
+//                       <div className="content">
+//                         <Preloader />
+//                       </div>
+//                     </div>
+//                   </Col>
+//                 </Row>
+//               ) : (
+//                 <div>
+//                   {this.props.studySets.map(studySet => (
+//                     <div key={`study-set-${studySet.name}`}>
+//                       <Row className="subcontainer justify-content-md-center">
+//                         <Col lg={12} sm={12} xl={12}>
+//                           <div className="finder" id={studySet.name} />
+//                           <div className="listcard listcard-recording">
+//                             <div className="listcard-content">
+//                               <div className="listcard-section">
+//                                 <Link
+//                                   to={`/studyset/${studySet.name}`}
+//                                   className="listcard-title"
+//                                 >
+//                                   {studySet.name}
+//                                 </Link>
+//                               </div>
+//                               <div className="listcard-section">
+//                                 <Table striped bordered size="sm">
+//                                   <thead>
+//                                     <tr>
+//                                       <th key="col1">Study name</th>
+//                                       <th key="col2">Number recordings</th>
+//                                       <th key="col3"> </th>
+//                                     </tr>
+//                                   </thead>
+//                                   <tbody>
+//                                     {studySet.studies.map(study => (
+//                                       <tr key={`study-${study.name}`}>
+//                                         <td key="col1">{study.name}</td>
+//                                         <td key="col2">
+//                                           {study.recordings.length}
+//                                         </td>
+//                                         <td
+//                                           key="col3"
+//                                           className="listcard-link"
+//                                         >
+//                                           <Link
+//                                             to={`/study/${study.name}`}
+//                                             className="listcard-env"
+//                                           >
+//                                             View study details
+//                                           </Link>
+//                                         </td>
+//                                       </tr>
+//                                     ))}
+//                                   </tbody>
+//                                 </Table>{" "}
+//                               </div>
+//                             </div>
+//                           </div>
+//                         </Col>
+//                       </Row>
+//                     </div>
+//                   ))}
+//                 </div>
+//               )}
+//               <div style={{ margin: "8rem 0" }} />
+//             </Container>
+//           </Col>
+//         </Row>
+//       </Container>
+//     );
+//   }
+// }
+
+export default Recordings;
diff --git a/src/components/util.ts b/src/components/util.ts
index ca933ed..f0917b5 100644
--- a/src/components/util.ts
+++ b/src/components/util.ts
@@ -21,4 +21,4 @@ export const isEmpty = (obj: any): boolean => Object.entries(obj).length === 0
 //     link: (props: any) => {
 //         return `<a href=${props.href} target="_blank" rel="nofollow noreferrer noopener">${props.children}</a>`
 //     }
-// }
+// }
\ No newline at end of file
diff --git a/src/sampleData/studysets-from-api.json b/src/sampleData/studysets-from-api.json
new file mode 100644
index 0000000..4e8d3dd
--- /dev/null
+++ b/src/sampleData/studysets-from-api.json
@@ -0,0 +1 @@
+[{"_id":"5e795ef5d4f81c62f9a1aa45","name":"HYBRID_JANELIA","description":"# HYBRID_JANELIA\n- Hybrid recording with a sinusoidal probe drift\n- Drift was generated using 2D interpolation (Modified Akima)\n- Waveform templates were recorded using 5 um spaced electrodes, see [Kampff lab Ultradense Survey](http://www.kampff-lab.org/ultra-dense-survey), and later subsampled to match the Neuropixels electrode spacing\n- Background noise was generated using ~1/f random noise that matches experimental neural recordings.\n- Generated by `Kilosort2\\eMouse_drift` [code available on Github] (https://github.com/MouseLand/Kilosort2)\n- Two sine wave periods were present in the drift (20 um amplitude) for the full recording duration (1200 s).\n\n## Recording format\n- int16 raw format (channels x time order)\n- 1200s recording was divided to various length (600s, 1200s).\n- 64 channels were divided to various sub channels (4, 16, 32, 64) at various probe locations.\n\n\n## References\n- https://github.com/MouseLand/Kilosort2\n- https://github.com/MouseLand/Kilosort2/wiki/4.-eMouse-simulator-with-drift\n- [Kampff lab Ultradense Survey](http://www.kampff-lab.org/ultra-dense-survey)","studies":[{"recordings":[{"_id":"5e795ef5d4f81c62f9a1aa85","name":"rec_16c_1200s_11","studyName":"hybrid_drift_siprobe","studySetName":"HYBRID_JANELIA","directory":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_1200s_11","firingsTrue":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_1200s_11/firings_true.mda","sampleRateHz":30000,"numChannels":16,"durationSec":1200,"numTrueUnits":74,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa84","name":"rec_16c_1200s_21","studyName":"hybrid_drift_siprobe","studySetName":"HYBRID_JANELIA","directory":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_1200s_21","firingsTrue":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_1200s_21/firings_true.mda","sampleRateHz":30000,"numChannels":16,"durationSec":1200,"numTrueUnits":74,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa83","name":"rec_16c_1200s_31","studyName":"hybrid_drift_siprobe","studySetName":"HYBRID_JANELIA","directory":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_1200s_31","firingsTrue":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_1200s_31/firings_true.mda","sampleRateHz":30000,"numChannels":16,"durationSec":1200,"numTrueUnits":74,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa82","name":"rec_16c_600s_11","studyName":"hybrid_drift_siprobe","studySetName":"HYBRID_JANELIA","directory":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_11","firingsTrue":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_11/firings_true.mda","sampleRateHz":30000,"numChannels":16,"durationSec":600,"numTrueUnits":74,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa81","name":"rec_16c_600s_12","studyName":"hybrid_drift_siprobe","studySetName":"HYBRID_JANELIA","directory":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_12","firingsTrue":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_12/firings_true.mda","sampleRateHz":30000,"numChannels":16,"durationSec":600,"numTrueUnits":74,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa80","name":"rec_16c_600s_21","studyName":"hybrid_drift_siprobe","studySetName":"HYBRID_JANELIA","directory":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_21","firingsTrue":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_21/firings_true.mda","sampleRateHz":30000,"numChannels":16,"durationSec":600,"numTrueUnits":74,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa7f","name":"rec_16c_600s_22","studyName":"hybrid_drift_siprobe","studySetName":"HYBRID_JANELIA","directory":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_22","firingsTrue":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_22/firings_true.mda","sampleRateHz":30000,"numChannels":16,"durationSec":600,"numTrueUnits":74,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa7e","name":"rec_16c_600s_31","studyName":"hybrid_drift_siprobe","studySetName":"HYBRID_JANELIA","directory":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_31","firingsTrue":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_31/firings_true.mda","sampleRateHz":30000,"numChannels":16,"durationSec":600,"numTrueUnits":74,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa7d","name":"rec_16c_600s_32","studyName":"hybrid_drift_siprobe","studySetName":"HYBRID_JANELIA","directory":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_32","firingsTrue":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_16c_600s_32/firings_true.mda","sampleRateHz":30000,"numChannels":16,"durationSec":600,"numTrueUnits":74,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa7c","name":"rec_32c_1200s_11","studyName":"hybrid_drift_siprobe","studySetName":"HYBRID_JANELIA","directory":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_32c_1200s_11","firingsTrue":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_32c_1200s_11/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":1200,"numTrueUnits":74,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa7b","name":"rec_32c_1200s_21","studyName":"hybrid_drift_siprobe","studySetName":"HYBRID_JANELIA","directory":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_32c_1200s_21","firingsTrue":"sha1dir://615aa23efde8898aa89002613e20ad59dcde42f9.hybrid_janelia/drift_siprobe/rec_32c_1200s_21/fir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MANUAL_FRANKLAB\n\n- Tetrode manual sorting by three human sorters. Dataset prepared by Jason Chung from Loren Frank's lab.\n- Three human sorters sorted identical recordings, and the data is divided to three time durations (600s, 1200s, 2400s).\n- Prepared by James Jun (2018 Dec 21).\n\n## Studies contained\n- `manual_tetrode_2400s` (3 recordings)\n  - Each recording is 2400s long (full duration). \n- `manual_tetrode_1200s` (6 recordings)\n  - Each recording is split into two 1200s segments. \n- `manual_tetrode_600s` (12 recordings)\n  - Each recording is split into two 600s segments. \n\n\n\n## References\n- [Chung JE, Magland JF, Barnett AH, Tolosa VM, Tooker AC, Lee KY, Shah KG, Felix SH, Frank LM, Greengard LF. A fully automated approach to spike sorting. Neuron. 2017 Sep 13;95(6):1381-94](https://www.sciencedirect.com/science/article/pii/S0896627317307456)","studies":[{"recordings":[{"_id":"5e795ef5d4f81c62f9a1a9cc","name":"sorter1_1","studyName":"manual_tetrode_600s","studySetName":"MANUAL_FRANKLAB","directory":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter1_1","firingsTrue":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter1_1/firings_true.mda","sampleRateHz":30000,"numChannels":4,"durationSec":600,"numTrueUnits":11,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a9cb","name":"sorter1_2","studyName":"manual_tetrode_600s","studySetName":"MANUAL_FRANKLAB","directory":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter1_2","firingsTrue":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter1_2/firings_true.mda","sampleRateHz":30000,"numChannels":4,"durationSec":600,"numTrueUnits":11,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a9ca","name":"sorter1_3","studyName":"manual_tetrode_600s","studySetName":"MANUAL_FRANKLAB","directory":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter1_3","firingsTrue":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter1_3/firings_true.mda","sampleRateHz":30000,"numChannels":4,"durationSec":600,"numTrueUnits":11,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a9c9","name":"sorter1_4","studyName":"manual_tetrode_600s","studySetName":"MANUAL_FRANKLAB","directory":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter1_4","firingsTrue":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter1_4/firings_true.mda","sampleRateHz":30000,"numChannels":4,"durationSec":600,"numTrueUnits":11,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a9c8","name":"sorter2_1","studyName":"manual_tetrode_600s","studySetName":"MANUAL_FRANKLAB","directory":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter2_1","firingsTrue":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter2_1/firings_true.mda","sampleRateHz":30000,"numChannels":4,"durationSec":600,"numTrueUnits":8,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a9c7","name":"sorter2_2","studyName":"manual_tetrode_600s","studySetName":"MANUAL_FRANKLAB","directory":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter2_2","firingsTrue":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter2_2/firings_true.mda","sampleRateHz":30000,"numChannels":4,"durationSec":600,"numTrueUnits":8,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a9c6","name":"sorter2_3","studyName":"manual_tetrode_600s","studySetName":"MANUAL_FRANKLAB","directory":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter2_3","firingsTrue":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter2_3/firings_true.mda","sampleRateHz":30000,"numChannels":4,"durationSec":600,"numTrueUnits":8,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a9c5","name":"sorter2_4","studyName":"manual_tetrode_600s","studySetName":"MANUAL_FRANKLAB","directory":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter2_4","firingsTrue":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter2_4/firings_true.mda","sampleRateHz":30000,"numChannels":4,"durationSec":600,"numTrueUnits":8,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a9c4","name":"sorter3_1","studyName":"manual_tetrode_600s","studySetName":"MANUAL_FRANKLAB","directory":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter3_1","firingsTrue":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter3_1/firings_true.mda","sampleRateHz":30000,"numChannels":4,"durationSec":600,"numTrueUnits":7,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a9c3","name":"sorter3_2","studyName":"manual_tetrode_600s","studySetName":"MANUAL_FRANKLAB","directory":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter3_2","firingsTrue":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter3_2/firings_true.mda","sampleRateHz":30000,"numChannels":4,"durationSec":600,"numTrueUnits":7,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a9c2","name":"sorter3_3","studyName":"manual_tetrode_600s","studySetName":"MANUAL_FRANKLAB","directory":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter3_3","firingsTrue":"sha1dir://c0879a26f92e4c876cd608ca79192a84d4382868.manual_franklab/tetrode_600s/sorter3_3/firings_true.mda","sampleRateHz":30000,"numChannels":4,"durationSec":600,"numTrueUnits":7,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a9c1","name":"sorter3_4","studyName":"manual_tetrode_600s","studySetN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PAIRED_BOYDEN\n\nThis dataset was collected by Brian D. Allen from Ed Boyden's lab. The intracellular and extracellular voltages were recorded simultaneously.\nExtracellular voltages were recorded using 128 or 256 site silicon probes custom made at Boyden lab (9x9 um site dimension, 11x11 um site pitch).\n128-channel probe has 64x2 site grid pattern and 256-channel probe has 64x4 site grid pattern.\nBad or shorted sites are excluded based on the experimenter's criteria. The number of channels exported excludes the channels from bad sites. \nBursting spikes (ISI<20ms) are kept up to three successive spikes using the experimeter's burst creteria.\n\nFor more info, visit the publication website:\nhttps://www.physiology.org/doi/10.1152/jn.00650.2017\nAutomated in vivo patch clamp evaluation of extracellular multielectrode array spike recording capability\nBrian D. Allen, Caroline Moore-Kochlacs, Jacob Gold Bernstein, Justin Kinney, Jorg Scholvin, Luis Seoane, Chris Chronopoulos, Charlie Lamantia, Suhasa B Kodandaramaiah, Max Tegmark, and Edward S Boyden*\n\nThe raw data (.h5 format) was converted to .mda using ironclust v4.0.6 using `irc convert-h5-mda` command.\nhttps://github.com/jamesjun/ironclust\n\nA subset of the data was extracted -- 32 channels near the center of the activity, excluding the bad channels, as documented by the experimenters' annotation.\n\nThe recording conditions for each cell are listed below.\n\n```\nNeuron  Condition                       Filename        Target Layer    Mean uV (nonburst) on closest electrode Num chans\n--------------------------------------------------------------------------------------------------------------------------\n419_1   awake natural scene             419_7           V               256\n        awake gratings                  419_8           V       257     256\n513_2   awake natural scene             513_2_2         V               256\n        awake gratings                  513_2_3         V       84      256\n513_1   anesth. gratings (50pA stim)    513_1_1         II/III  52      256\n        anesth. gratings                513_1_2         II/III          256\n531_2   anesth. gratings                531_2_1         V               256\n        anesth. natural scene           531_2_2         V       91      256\n624_2   anesth. gratings                624_2_1         V               256\n        anesth. natural scene           624_2_2         V       53      256\n624_5   anesth. gratings                624_5_1         V               256\n        anesth. natural scene           624_5_2         V       68      256\n509_1   anesth. gratings                509_1_1         II/III          256\n        anesth. natural scene           509_1_2         II/III  61      256\n1103_1  anesth. gratings                1103_1_1        II/III  70      64\n915_8   anesth. gratings                915_8_1         V       101     128\n915_10  anesth. gratings                915_10_1        V       83      128\n915_17  anesth. gratings                915_17_1        II/III  57      128\n915_18  anesth. gratings                915_18_1        II/III  64      128\n```\n\n## References\nhttps://www.physiology.org/doi/10.1152/jn.00650.2017","studies":[{"recordings":[{"_id":"5e795ef5d4f81c62f9a1a7cf","name":"1103_1_1","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/1103_1_1","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/1103_1_1/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":387.11,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7ce","name":"419_1_7","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/419_1_7","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/419_1_7/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":494.46,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7cd","name":"419_1_8","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/419_1_8","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/419_1_8/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":490.3,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7cc","name":"509_1_1","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/509_1_1","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/509_1_1/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":486.66,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7cb","name":"509_1_2","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/509_1_2","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/509_1_2/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":497.05,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7ca","name":"513_1_1","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/513_1_1","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/513_1_1/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":482.33,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7c9","name":"513_1_2","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/513_1_2","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/513_1_2/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":482.09,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7c8","name":"513_2_2","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/513_2_2","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/513_2_2/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":482.02,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7c7","name":"513_2_3","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/513_2_3","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/513_2_3/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":483.9,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7c6","name":"531_2_1","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/531_2_1","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/531_2_1/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":604.03,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7c5","name":"531_2_2","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/531_2_2","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/531_2_2/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":487.6,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7c4","name":"624_2_1","studyName":"paired_boyden32c","studySetName":"PAIRED_BOYDEN","directory":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/624_2_1","firingsTrue":"sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/624_2_1/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":481.66,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a7c3","name":"624_2_2","studyNam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PAIRED_CRCNS_HC1\n- Data download from [crcns.org website](http://crcns.org/data-sets/hc/hc-1/about/?searchterm=download)\n- Raw recordings were curated by James Jun \n- Intracellular timing was determined by threshold crossing of a differentiated intracellular trace (10 SNR).\n- Stimulus artifacts were manually removed \n\n## Electrodes\n- Mix of tetrodes or six-channel silicon probe (single shank was exported, edge electrodes arranged in a 'V' shape)\n\n## References\n- http://crcns.org/data-sets/hc/hc-1/about/?searchterm=download\n- [Henze et al, J. Neurophysiology 84, 390-400 (2000)](http://www.ncbi.nlm.nih.gov/pubmed/10899213)\n- [Harris et al, J. Neurophysiology 84, 401-414 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   # Silicon-juxtacellular hybrid probes\n    This studyset consists of 29 paired recordings from Silicon-juxtacellular hybrid probes.     Juxtacellular electrodes are pulled from 1 mm OD 0.7 mm ID borosilicate glass and have impedances of 8-10 MOhm     when filled with 130 mM potassium acetate. These juxtacellular electrodes are glued to 32 channel silicon probes     (A1-32-Poly3, Neuronexus) using light-cure dental acrylic. Using the inter-site spacing of the silicon probe as     a visual distance guide, the tip of the juxtacellular is fixed at a distance of ~20 micrometers from the nearest silicon     probe recording site. The closest site is selected as a site in the middle (top to bottom) of an outer column of     recording sites. The close distance between the juxtacellular tip and the recording sites results in both devices     recording the same neuron at high probability. \n    \n    #  Recording ground truth data in awake behaving mice\n    Mice are placed in the head-fixed treadmill apparatus and the protective silicon elastomer is removed from the craniotomy,     the juxtacellular-silicon hybrid prove is inserted 0.1 mm into the brain, and the craniotomy is covered with silicon fluid to     prevent drying and improve stability. The probe is advanced into the brain at ~1-2 micrometers until action potentials are     observed on the juxtacellular electrode, at which point movement is ceased and data collection begins. \n    Electrical signals from the juxtacellular electrode are recorded and amplified through an analog microelectrode amplifier     (Cygnus IR-183), then digitized via an Intan RHD2000 system. Signals from the silicon probe are amplified and digitized     through the same RHD2000 system, which results in hardware synchronization of the two signals. By converting the digital     signals in the RHD2000 to analog voltage and outputting them to a digital microprocessor (CED Power1401), we calculated online     the juxtacellular spike triggered average of each channel of the extracellular electrode, and discard recordings in which the     triggered average does not contain a spike waveform on any channel.\n    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PAIRED_KAMPFF\n\n## Paired juxtacellular and silicon probe recording\n- Recordings were contributed from Adam [Kampff lab](http://www.kampff-lab.org/validating-electrodes)\n- Prepared by J. James Jun, Dec 18, 2018.\n- Extracted subarray of 32 channels centered around the activity peak.\n- 12 cell pairs were taken from the Neuropixels probe recordings based on the highest SNR.\n- Intracellular timing was provided by the Kampff lab.\n\n## Silicon probes used\n- `2014_11_25_Pair_3_0`: Neuronexus poly32nn (25 um spacing, staggered three columns)\n  - https://drive.google.com/drive/folders/0B6paC2__-QYFNEt6VnZ0QVlkYlk\n- `2015_09_03_Pair_9_0A`: IMEC Neuroseeker probe (four columns, 20 um spacing). Subsampled half the electrodes (staggered four columns, neuropixels probe layout)\n  - https://drive.google.com/drive/folders/0B6paC2__-QYFTGtfd2NBb1d3ZlE\n- `2015_09_03_Pair_9_0B`: IMEC Neuroseeker probe, subsampled the other half of the electrodes\n  - https://drive.google.com/drive/folders/0B6paC2__-QYFTGtfd2NBb1d3ZlE\n- `c##`: Neuropixels probe (four columns staggered, 20 um vertical spacing, 28 um horizontal spacing). 12 cell pairs available.\n  - https://drive.google.com/drive/folders/13GCOuWN4QMW6vQmlNIolUrxPy-4Wv1BC\n\n## References\n- https://www.physiology.org/doi/full/10.1152/jn.00103.2016\n- https://www.biorxiv.org/content/10.1101/370080v1","studies":[{"recordings":[{"_id":"5e795ef5d4f81c62f9a1a853","name":"2014_11_25_Pair_3_0","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/2014_11_25_Pair_3_0","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/2014_11_25_Pair_3_0/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":557.22,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a852","name":"2015_09_03_Pair_9_0A","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/2015_09_03_Pair_9_0A","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/2015_09_03_Pair_9_0A/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":593.25,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a851","name":"2015_09_03_Pair_9_0B","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/2015_09_03_Pair_9_0B","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/2015_09_03_Pair_9_0B/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":593.25,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a850","name":"c14","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c14","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c14/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":1080.05,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a84f","name":"c16","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c16","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c16/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":675.03,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a84e","name":"c19","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c19","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c19/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":810.03,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a84d","name":"c21","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c21","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c21/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":1080.05,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a84c","name":"c24","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c24","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c24/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":810.03,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a84b","name":"c26","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c26","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c26/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":1215.05,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a84a","name":"c27","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c27","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c27/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":810.03,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a849","name":"c28","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c28","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c28/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":810.03,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a848","name":"c42","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c42","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c42/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":810.03,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a847","name":"c44","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c44","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c44/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":540.02,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a846","name":"c45","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c45","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c45/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":810.03,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a845","name":"c46","studyName":"paired_kampff","studySetName":"PAIRED_KAMPFF","directory":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c46","firingsTrue":"sha1dir://51570fce195942dcb9d6228880310e1f4ca1395b.paired_kampff/c46/firings_true.mda","sampleRateHz":30000,"numChannels":32,"durationSec":810.03,"numTrueUnits":1,"spikeSign":-1}],"_id":"5e795ef5d4f81c62f9a1a844","name":"paired_kampff","studySetName":"PAIRED_KAMPFF"}],"__v":0},{"_id":"5e795ef5d4f81c62f9a1a82f","name":"PAIRED_MEA64C_YGER","description":"# PAIRED_MEA64C_YGER\n\n## Dataset\n- An 8x8 sub-array was extracted from the original 16x16 MEA (252 channels, four corners excluded). \n- We used the ground-truth timing provided by the authors.\n\n## References\n* https://zenodo.org/record/1205233#.XMH886xKjCI\n* https://elifesciences.org/articles/34518","studies":[{"recordings":[{"_id":"5e795ef5d4f81c62f9a1a842","name":"20160415_patch2","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20160415_patch2","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20160415_patch2/firings_true.mda","sampleRateHz":20000,"numChannels":64,"durationSec":300,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a841","name":"20160426_patch3","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20160426_patch3","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20160426_patch3/firings_true.mda","sampleRateHz":20000,"numChannels":64,"durationSec":180,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a840","name":"20170621_patch1","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170621_patch1","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170621_patch1/firings_true.mda","sampleRateHz":20000,"numChannels":64,"durationSec":300,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a83f","name":"20170622_patch1","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170622_patch1","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170622_patch1/firings_true.mda","sampleRateHz":20000,"numChannels":64,"durationSec":300,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a83e","name":"20170622_patch2","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170622_patch2","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170622_patch2/firings_true.mda","sampleRateHz":20000,"numChannels":64,"durationSec":300,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a83d","name":"20170623_patch1","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170623_patch1","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170623_patch1/firings_true.mda","sampleRateHz":20000,"numChannels":64,"durationSec":300,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a83c","name":"20170627_patch1","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170627_patch1","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170627_patch1/firings_true.mda","sampleRateHz":20000,"numChannels":64,"durationSec":300,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a83b","name":"20170629_patch2","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170629_patch2","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170629_patch2/firings_true.mda","sampleRateHz":20000,"numChannels":64,"durationSec":300,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a83a","name":"20170629_patch3","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170629_patch3","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170629_patch3/firings_true.mda","sampleRateHz":20000,"numChannels":64,"durationSec":300,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a839","name":"20170630_patch1","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170630_patch1","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170630_patch1/firings_true.mda","sampleRateHz":20000,"numChannels":64,"durationSec":300,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a838","name":"20170706_patch1","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170706_patch1","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170706_patch1/firings_true.mda","sampleRateHz":20000,"numChannels":64,"durationSec":300,"numTrueUnits":1,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a837","name":"20170706_patch2","studyName":"paired_mea64c","studySetName":"PAIRED_MEA64C_YGER","directory":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/20170706_patch2","firingsTrue":"sha1dir://0ba09f6658e767d4e70055773805a8d939a9a4c6.paired_mea64c/201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PAIRED_MONOTRODE\n- Raw recordings were curated by James Jun\n- For each original paired recordings, four channels with the highest peak-to-peak voltages were extracted and saved to individual files.\n\n## Electrodes\n- Monotrode (single-channel electrode) by subsampling the paired intra/extracellular recordings collected by four labs\n  Adam Kampff (UCL), Ed Boyden (MIT), Gyuri Buszaki (NYU), Olivier Marre (CNRS)\n\n## References\n### Recordings from Gyuri Buzsaki's lab\n- http://crcns.org/data-sets/hc/hc-1/about/?searchterm=download\n- [Henze et al, J. Neurophysiology 84, 390-400 (2000)](http://www.ncbi.nlm.nih.gov/pubmed/10899213)\n- [Harris et al, J. Neurophysiology 84, 401-414 (2000)](http://www.ncbi.nlm.nih.gov/pubmed/10899214)\n\n### Recordings from Olivier Marre's lab \n- https://zenodo.org/record/1205233#.XMH886xKjCI\n- https://elifesciences.org/articles/34518\n\n### Recordings from Ed Boyden's lab\n- https://www.physiology.org/doi/10.1152/jn.00650.2017\n\n## Recordings from Adam Kampff's lab\n- https://www.physiology.org/doi/full/10.1152/jn.00103.2016\n- 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SYNTH_BIONET\n\n## Synthetic groundtruth generated using Bionet simulator\n- Bionet is a neuron-based network simulator\n- Original data (4 minutes) was generated using Bionet simulator (noiseless). \n- Data was concatenated 4-fold with random noise (10 microvolts RMS) to yield 16 minutes of data.\n\n## Detailed simulation method (originally described in Jun et al, 2017, bioRxiv)\nOur biophysical simulation randomly distributed ~710 neurons within a sensing volume of the probe (200×200×600 μm), and each neuron is constructed from real morphology of various cell types. Our simulation closely matches the real recordings in terms of the spatial spread of the spike waveforms, and the identity of all units and their spike timing information are precisely determined from their simulated intracellular voltage traces. Biophysically realistic network simulations were setup in order to generate simulated ground truth extracellular depth recordings datasets. The main engine used for these biophysical simulations was NEURON version 7.4 used with custom-written python (version 2.7) wrapper-algorithms that were used to define, setup and instantiate network models and simulations as well as save output.\n\nThe network model consists of two cell types, excitatory pyramidal neurons (2,560) and inhibitory basket cells (640). Pyramidal single-neuron models used in our network were published by Hay and co-workers as part of their study on models capturing a wide range of dendritic and perisomatic active properties. These single-neuron models were shown to capture a number of intracellular characteristics such as backpropagating action potentials, dendritic Calcium electrogenesis, etc. In addition, a later study showed that these computational models accurately capture the extracellular signature of neocortical pyramidal neurons. We adopted inhibitory basket cell models from Hu et al. (2010) who developed these models to capture a number of features such as backpropagation and dendritic Na/K ratio. Notably, pyramidal and basket cell models had active dendrites, a feature critical for capturing extracellular spiking activity. Each neuron received external excitatory synaptic input emulating AMPA synapses. The number and timing of these external synaptic inputs was set such as to reach specific spike frequency output.\n\nWith regards to calculating the simulated extracellular recordings, the so-called line source approximation was used to estimate the extracellular voltage at various locations assuming a perfectly homogeneous, isotropic and purely resistive (0.3 S/m) extracellular space. Specifically, the Neuropixels probe electrode location was adopted from layout specifications. Network simulations were performed at the San Diego supercomputer facility via the NSG (neuroscience gateway) portal. \n\n## Studies contained \n- `bionet_static`\n  - No drift was added. Electrode spacing: 60 channels spaced 20 um vertically and 28 um horizontally. \n  - Four electrode layouts used: two columns (two layouts) and four staggered columns (two layout)\n\n- `bionet_drift`\n  - Uniform drift was added by translating 16 micrometers over the duration of the recording (16 minutes).\n  - Drift was generated at 0.5 um (32 steps) resolution by interpolating 5 um spaced electrodes with a 2D Gaussian kernel.\n\n- `bionet_shuffle`\n  - Random jumping drift was added by dividing the drifting dataset to 32 time segments and shuffling in time.\n\n## References\n- [Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction](https://www.biorxiv.org/content/10.1101/101030v2)\n- [BioNet: A Python interface to NEURON for modeling large-scale networks](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201630)","studies":[{"recordings":[{"_id":"5e795ef5d4f81c62f9a1a95a","name":"static_8x_A_2A","studyName":"synth_bionet_static","studySetName":"SYNTH_BIONET","directory":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_A_2A","firingsTrue":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_A_2A/firings_true.mda","sampleRateHz":20000,"numChannels":60,"durationSec":960,"numTrueUnits":708,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a959","name":"static_8x_A_2B","studyName":"synth_bionet_static","studySetName":"SYNTH_BIONET","directory":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_A_2B","firingsTrue":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_A_2B/firings_true.mda","sampleRateHz":20000,"numChannels":60,"durationSec":960,"numTrueUnits":708,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a958","name":"static_8x_A_4A","studyName":"synth_bionet_static","studySetName":"SYNTH_BIONET","directory":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_A_4A","firingsTrue":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_A_4A/firings_true.mda","sampleRateHz":20000,"numChannels":60,"durationSec":960,"numTrueUnits":708,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a957","name":"static_8x_A_4B","studyName":"synth_bionet_static","studySetName":"SYNTH_BIONET","directory":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_A_4B","firingsTrue":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_A_4B/firings_true.mda","sampleRateHz":20000,"numChannels":60,"durationSec":960,"numTrueUnits":708,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a956","name":"static_8x_B_2A","studyName":"synth_bionet_static","studySetName":"SYNTH_BIONET","directory":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_B_2A","firingsTrue":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_B_2A/firings_true.mda","sampleRateHz":20000,"numChannels":60,"durationSec":960,"numTrueUnits":708,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a955","name":"static_8x_B_2B","studyName":"synth_bionet_static","studySetName":"SYNTH_BIONET","directory":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_B_2B","firingsTrue":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_B_2B/firings_true.mda","sampleRateHz":20000,"numChannels":60,"durationSec":960,"numTrueUnits":708,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a954","name":"static_8x_B_4A","studyName":"synth_bionet_static","studySetName":"SYNTH_BIONET","directory":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_B_4A","firingsTrue":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_B_4A/firings_true.mda","sampleRateHz":20000,"numChannels":60,"durationSec":960,"numTrueUnits":708,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a953","name":"static_8x_B_4B","studyName":"synth_bionet_static","studySetName":"SYNTH_BIONET","directory":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_B_4B","firingsTrue":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_B_4B/firings_true.mda","sampleRateHz":20000,"numChannels":60,"durationSec":960,"numTrueUnits":708,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a952","name":"static_8x_C_2A","studyName":"synth_bionet_static","studySetName":"SYNTH_BIONET","directory":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_C_2A","firingsTrue":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_C_2A/firings_true.mda","sampleRateHz":20000,"numChannels":60,"durationSec":960,"numTrueUnits":708,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a951","name":"static_8x_C_2B","studyName":"synth_bionet_static","studySetName":"SYNTH_BIONET","directory":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_C_2B","firingsTrue":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_C_2B/firings_true.mda","sampleRateHz":20000,"numChannels":60,"durationSec":960,"numTrueUnits":708,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a950","name":"static_8x_C_4A","studyName":"synth_bionet_static","studySetName":"SYNTH_BIONET","directory":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_C_4A","firingsTrue":"sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_C_4A/firings_true.mda","sampleRateHz":20000,"numChannels":60,"durationSec":960,"numTrueUnits":70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SYNTH_MAGLAND\n\n## Synthetic groundtruth generated by spikeforest\n- Data generated by James Jun on Oct 19, 2018 based on Jeremy Magland's code\n- Waveform templates are generated using parameteric curves\n- Spike timing was randomly generated from the Poisson distribution and white noise is added\n- [Code is available on Github](https://github.com/magland/spikeforest/tree/master/working/gen_synth_datasets/8trode) (TODO: need to update this link)\n  - run `python working/gen_synth_datasets/8trode/driver.py`\n  - commit#: eaf42d7a76dfd31b4150b752815d8efffdc51908\n- Required packages\n  - [spikeinterface](https://github.com/colehurwitz31/spikeinterface)\n  - [mountainlab_pytools](https://github.com/magland/mountainlab_pytools)\n\n## Studies contained this dataset\n- Directory names follow a format `datasets_noise#_K#_C#` where\n- `noise#`: noise value added (SD of white noise, 10 or 20)\n- `K#`: number of units simulated (10 or 20)\n- `C#`: number of electrodes (4 or 8 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SYNTH_MEAREC_NEURONEXUS\n\n## Recording\n- Synthetic groundtruth generated by SpikeForest using spike waveform templates generated by MEAREC (neuron-based simulator)\n- Recordings generated by James Jun on Dec 4, 2018\n- code is available on Github\n  - run `https://github.com/magland/spikeforest/blob/master/gen_synth_datasets_mearec/gen_synth_datasets_neuronexus.ipynb`\n  - Python notebook is modified from `gen_synth_datasets.ipynb` written by Jeremy Magland\n  - commit#: 6691a1b4701696fa6b9aab5294f34a53ebb9d65e\n  - Spike waveform templates are generated by Alessio Buccino using MEAREC\n  - Templates location\n    - https://kbucket.flatironinstitute.org/15734439d8cf/download/groundtruth/mearec_synth/neuronexus/templates_30_Neuronexus-32.h5\n- required packages\n  - spikeforest: https://github.com/magland/spikeforest\n\n## Electrode\n- Neuronexus A32\n  - single column, 25 um spacing, 32 sites\n\n## Directory naming convension: datasets_noise#_K#_C#\n- noise#: noise value added (SD of white noise)\n- K#: number of units simulated\n- C#: number of electrodes (arranged linearly and separated by a 1 distance 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SYNTH_MEAREC_TETRODE\n\n## Recording\n- Synthetic groundtruth generated by SpikeForest using spike waveform templates generated by MEAREC (neuron-based simulator)\n- Recordings generated by James Jun on Dec 4, 2018\n- code is available on Github\n  - run `https://github.com/magland/spikeforest/blob/master/gen_synth_datasets_mearec/gen_synth_datasets.ipynb`\n  - Python notebook is modified from `gen_synth_datasets.ipynb` written by Jeremy Magland\n  - commit#: 6691a1b4701696fa6b9aab5294f34a53ebb9d65e\n  - Spike waveform templates are generated by Alessio Buccino using MEAREC\n  - Templates location\n    - https://kbucket.flatironinstitute.org/15734439d8cf/download/groundtruth/mearec_synth/tetrode/templates_tetrode.h5\n- required packages\n  - spikeforest: https://github.com/magland/spikeforest\n\n## Electrode\n- Tetrode, linearly spaced\n\n## Directory naming convension: datasets_noise#_K#_C#\n- noise#: noise value added (SD of white noise)\n- K#: number of units simulated\n- C#: number of electrodes (arranged linearly and separated by a 1 distance 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SYNTH_MONOTRODE\n## Description\n*From the author description*:\nSimulated dataset used in Pedreira et al., J Neurosci Methods, 2012\n\nThis dataset comprises 95 simulations of 10 minutes-long extracellular recordings. Details of the simulation approach can be found in the paper. Simulations included different number of single units, from 2 to 20. For each case, 5 different simulations were performed.\n\nThe data associated with each simulation is stored in the variable data located in the file ‘simulation_sim-num.mat’, where sim-num represents the simulation number (from1 to 95). The sampling rate is 24 KHz. \n\nAll the information regarding to the ground truth of the recording is stored in 3 cell arrays located in the file ‘ground_truth.mat’. The details associated to the simulation sim-num are stored in the sim-num-th row of each cell array. Particularly, it includes details of the waveform, class (single unit label or multiunit) and time in the recording, corresponding to each of the spikes included in the simulation.\n\nVariable su_waveforms{sim-num} stores the waveforms of each single unit used in the simulation. Each waveform has 316 points and it is sampled at 96 KHz (the original sampling rate used to create the neural recording before downsampling it to 24Khz).\n\nVariable spike_classes{sim-num} is a vector where the i-th component represents the class associated to the i-th spike in the simulation. It takes values between 0 and tot_SU (the total number of single units included in the simulation, which can be from 2 to 20). Class 0 is associated to the multiunit activity. Each single unit is associated to a waveform stored in su_waveforms{sim-num}.\n\nVariable spike_first_sample{sim-num} is a vector where the i-th component represents the first sample in the recording where the i-th spike in the simulation was inserted (which is associated to the waveform from the class number in the i-th component of spike_classes{sim-num}).\n\n## Recording format\n- single channel\n- float32 format (1 channel x time order)\n- 600s recording duration\n- Negative-going polarity (sign was flipped from the original recording supplied by the authors)\n\n\n## References\n- Pedreira C, Martinez J, Ison MJ, Quian Quiroga R. How many neurons can we see with current spike sorting algorithms? J  Neurosci  Methods 211: 58–65, 2012. doi: 10.1016/j.jneumeth.2012.07.010\n- [Recordings with ground-truth are available here](https://www135.lamp.le.ac.uk/hgr3/)","studies":[{"recordings":[{"_id":"5e795ef5d4f81c62f9a1a932","name":"simulation_13","studyName":"neurocube_sim2_2K10","studySetName":"SYNTH_MONOTRODE","directory":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_13","firingsTrue":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_13/firings_true.mda","sampleRateHz":24000,"numChannels":1,"durationSec":600,"numTrueUnits":15,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a931","name":"simulation_14","studyName":"neurocube_sim2_2K10","studySetName":"SYNTH_MONOTRODE","directory":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_14","firingsTrue":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_14/firings_true.mda","sampleRateHz":24000,"numChannels":1,"durationSec":600,"numTrueUnits":11,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a930","name":"simulation_15","studyName":"neurocube_sim2_2K10","studySetName":"SYNTH_MONOTRODE","directory":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_15","firingsTrue":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_15/firings_true.mda","sampleRateHz":24000,"numChannels":1,"durationSec":600,"numTrueUnits":17,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a92f","name":"simulation_16","studyName":"neurocube_sim2_2K10","studySetName":"SYNTH_MONOTRODE","directory":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_16","firingsTrue":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_16/firings_true.mda","sampleRateHz":24000,"numChannels":1,"durationSec":600,"numTrueUnits":2,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a92e","name":"simulation_17","studyName":"neurocube_sim2_2K10","studySetName":"SYNTH_MONOTRODE","directory":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_17","firingsTrue":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_17/firings_true.mda","sampleRateHz":24000,"numChannels":1,"durationSec":600,"numTrueUnits":19,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a92d","name":"simulation_18","studyName":"neurocube_sim2_2K10","studySetName":"SYNTH_MONOTRODE","directory":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_18","firingsTrue":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_18/firings_true.mda","sampleRateHz":24000,"numChannels":1,"durationSec":600,"numTrueUnits":20,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a92c","name":"simulation_21","studyName":"neurocube_sim2_2K10","studySetName":"SYNTH_MONOTRODE","directory":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_21","firingsTrue":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_21/firings_true.mda","sampleRateHz":24000,"numChannels":1,"durationSec":600,"numTrueUnits":3,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a92b","name":"simulation_22","studyName":"neurocube_sim2_2K10","studySetName":"SYNTH_MONOTRODE","directory":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_22","firingsTrue":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_22/firings_true.mda","sampleRateHz":24000,"numChannels":1,"durationSec":600,"numTrueUnits":9,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a92a","name":"simulation_24","studyName":"neurocube_sim2_2K10","studySetName":"SYNTH_MONOTRODE","directory":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_24","firingsTrue":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_24/firings_true.mda","sampleRateHz":24000,"numChannels":1,"durationSec":600,"numTrueUnits":9,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a929","name":"simulation_27","studyName":"neurocube_sim2_2K10","studySetName":"SYNTH_MONOTRODE","directory":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_27","firingsTrue":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_27/firings_true.mda","sampleRateHz":24000,"numChannels":1,"durationSec":600,"numTrueUnits":14,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a928","name":"simulation_29","studyName":"neurocube_sim2_2K10","studySetName":"SYNTH_MONOTRODE","directory":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_29","firingsTrue":"sha1dir://d857ca61ee229a341ff44ebaa7e299b4a401c066.synth_monotrode/sim2_2K10/simulation_29/firings_true.mda","sampleRateHz":24000,"numChannels":1,"durationSec":600,"numTrueUnits":6,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1a927","name":"simulation_30","studyName":"neur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SYNTH_VISAPY\n\n## Synthetic groundtruth generated by ViSAPy\n\n- Data converted to the mda format by James Jun on Feb 19, 2019.\n- Data provided by Peter Lee from Paninski lab (used to test YASS).\n- \"ViSAPy\" is a NEURON-based simulator developed by Espen Hagen from Einevoll's group.\n  - https://github.com/espenhgn/ViSAPy\n\n## Recording format\n\n- The directory contains six recordings (set1..6)\n- Each recording contains 30 channels, 300 sec duration\n- Data is stored in float32 format\n- Unit is in millivolts, and spikes have negative polarity\n- Each recording contains 16 ground truth units\n\n## References\n\nHagen, Espen, et al. \"ViSAPy: a Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms.\" Journal of neuroscience methods 245 (2015): 182-204.\n\nLee, Jin Hyung, et al. \"Yass: Yet another spike sorter.\" Advances in Neural Information Processing Systems. 2017.","studies":[{"recordings":[{"_id":"5e795ef5d4f81c62f9a1aa44","name":"set1","studyName":"mea_c30","studySetName":"SYNTH_VISAPY","directory":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set1","firingsTrue":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set1/firings_true.mda","sampleRateHz":32000,"numChannels":30,"durationSec":300,"numTrueUnits":16,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa43","name":"set2","studyName":"mea_c30","studySetName":"SYNTH_VISAPY","directory":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set2","firingsTrue":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set2/firings_true.mda","sampleRateHz":32000,"numChannels":30,"durationSec":300,"numTrueUnits":16,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa42","name":"set3","studyName":"mea_c30","studySetName":"SYNTH_VISAPY","directory":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set3","firingsTrue":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set3/firings_true.mda","sampleRateHz":32000,"numChannels":30,"durationSec":300,"numTrueUnits":16,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa41","name":"set4","studyName":"mea_c30","studySetName":"SYNTH_VISAPY","directory":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set4","firingsTrue":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set4/firings_true.mda","sampleRateHz":32000,"numChannels":30,"durationSec":300,"numTrueUnits":16,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa40","name":"set5","studyName":"mea_c30","studySetName":"SYNTH_VISAPY","directory":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set5","firingsTrue":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set5/firings_true.mda","sampleRateHz":32000,"numChannels":30,"durationSec":300,"numTrueUnits":16,"spikeSign":-1},{"_id":"5e795ef5d4f81c62f9a1aa3f","name":"set6","studyName":"mea_c30","studySetName":"SYNTH_VISAPY","directory":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set6","firingsTrue":"sha1dir://ed0fe4de4ef2c54b7c9de420c87f9df200721b24.synth_visapy/mea_c30/set6/firings_true.mda","sampleRateHz":32000,"numChannels":30,"durationSec":300,"numTrueUnits":16,"spikeSign":-1}],"_id":"5e795ef5d4f81c62f9a1aa3e","name":"mea_c30","studySetName":"SYNTH_VISAPY"}],"__v":0}]
\ No newline at end of file

From 6cae3af7bbe29001385f4f8a1d20ab68105ca6f0 Mon Sep 17 00:00:00 2001
From: Jeff Soules <jsoules@flatironinstitute.org>
Date: Fri, 16 Jul 2021 16:46:37 -0400
Subject: [PATCH 2/3] Rebase recordings page and run off shared components from
 update to About page.

---
 src/components/Recordings/RecordingsPage.tsx | 285 ++-----------------
 src/components/Shared/MarkdownHandling.tsx   |   8 +-
 2 files changed, 26 insertions(+), 267 deletions(-)

diff --git a/src/components/Recordings/RecordingsPage.tsx b/src/components/Recordings/RecordingsPage.tsx
index 574a45c..5bce094 100644
--- a/src/components/Recordings/RecordingsPage.tsx
+++ b/src/components/Recordings/RecordingsPage.tsx
@@ -1,17 +1,14 @@
 import React, { Fragment, FunctionComponent, useEffect, useState } from "react"
 import { Col, Container, Row, Table } from "react-bootstrap"
-import ReactMarkdown from "react-markdown"
 import { Link } from "react-router-dom"
 import { Study, StudySet } from '../../calc/jsonTypes'
+import { Copy, getCanonicalSectionTag, PageCopy, PageSidebar, parseMarkdownToContentCards, Section } from "../Shared/MarkdownHandling"
 import Preloader from "../Shared/Preloader"
-import Sidebar from "../Shared/Sidebar"
-import { Copy, getCanonicalSectionTag, parseMarkdownToContentCards, Section, toTitleCase } from "../util"
+import { toTitleCase } from "../util"
 import pageContent from './RecordingPageContent.md'
 
-
 type Studies = { Studies: Study[] }
 type StudySets = { Sets: StudySet[] }
-type EnhancedCopy = Copy & { Loading: boolean, StudySets: StudySets }
 
 const getCanonicalStudySetName = (rawName: string): string => (
     toTitleCase(rawName.replace(/_/g, " ").toLowerCase())
@@ -24,98 +21,40 @@ const Recordings: FunctionComponent<StudySets> = (Props: StudySets) => {
         .then((res) => res.text())
         .then((text) => setCopyMd(text))
     })
-    const loading = !(Props.Sets && Props.Sets.length > 0)
     const content: Copy = parseMarkdownToContentCards(copyMd)
     const studySetSidebarEntries: Section[] = Props.Sets.map((ss) => {
         const name = getCanonicalStudySetName(ss.name)
         return {
-            header: '',
-            content: '',
-            label: name,
-            tag: getCanonicalSectionTag(ss.name),
+            Header: '',
+            Content: '',
+            Label: name,
+            Tag: getCanonicalSectionTag(ss.name),
         }
     })
 
+    const apiDataNotYetLoaded = (!Props.Sets || Props.Sets.length < 1)
+    const studySetDynamicContent = apiDataNotYetLoaded
+        ? < PreloaderCard />
+        : <Fragment>
+                {Props.Sets.map((studyset) => (<StudySetCard {...studyset} />)) }
+          </Fragment>
+
     return (
         <Container className="container-sidebar">
             <Row noGutters>
                 <PageSidebar
-                    title={content.title}
-                    sections={[...content.sections, ...studySetSidebarEntries]} />
+                    Title={content.Title}
+                    Sections={[...content.Sections, ...studySetSidebarEntries]} />
                 <PageCopy 
-                    title={content.title}
-                    sections={content.sections}
-                    Loading={loading}
-                    StudySets={Props} />
+                    Copy={content}
+                    ContentHook={() => undefined}
+                    AdditionalContent={studySetDynamicContent}
+                />
             </Row>
         </Container>
     )
 }
 
-const PageSidebar: FunctionComponent<Copy> = (Props: Copy) => {
-    const sidebarItems = Props.sections.map(
-        (section) => {
-            return {
-                name: section.label,
-                value: section.tag
-            }
-        })
-    return (
-        <Col xl={2} md={3} sm={12} className="sidebar">
-            <Sidebar listItems={sidebarItems} listTitle={Props.title} />
-        </Col>
-    )
-}
-
-const PageCopy: FunctionComponent<EnhancedCopy> = (Props: EnhancedCopy) => {
-    return (
-        <Col xl={10} md={9} sm={12} className="page__body">
-            <Container className="container__heatmap">
-                <Row className="subcontainer justify-content-md-center">
-                    <Col lg={12} sm={12} xl={12}>
-                        <div className="intro">
-                            <p className="big">{Props.title}</p>
-                        </div>
-                    </Col>
-                </Row>
-                { Props.sections.map((section) => (<StaticCard {...section} />)) }
-                { Props.Loading 
-                    ? <PreloaderCard />
-                    : <div>
-                        { Props.StudySets.Sets.map((studyset) => (<StudySetCard {...studyset} />))}
-                      </div>
-                }
-                <div style={{ margin: "8rem 0" }} />
-            </Container>
-        </Col>
-    )
-}
-
-const StaticCard: FunctionComponent<Section> = (Props: Section) => {
-    return (
-        <Fragment>
-            <div className="finder" id={Props.tag} />
-            <Row className="subcontainer justify-content-md-center">
-                <Col lg={12} sm={12} xl={12}>
-                    <div className="card card__std">
-                        <div className="content">
-                            <div className="card__label">
-                                <p>
-                                    <strong>{Props.header}</strong>
-                                </p>
-                            </div>
-                            <div className="card__footer">
-                                <hr />
-                                <ReactMarkdown children={Props.content} />
-                            </div>
-                        </div>
-                    </div>
-                </Col>
-            </Row>
-        </Fragment>
-    )
-}
-
 const PreloaderCard: FunctionComponent = () => {
     return (
         <Row className="subcontainer justify-content-md-center">
@@ -187,190 +126,4 @@ const StudyTableRow: FunctionComponent<Study> = (Props: Study) => {
     )
 }
 
-// class OldRecordings extends Component {
-//   render() {
-//     let loading = isEmpty(this.props.studySets);
-//     let sidebarItems = [];
-//     if (this.props.studySets) {
-//       sidebarItems = this.props.studySets.map(item => ({
-//         name: toTitleCase(item.name.replace(/_/g, " ").toLowerCase()),
-//         value: item.name
-//       }));
-//     }
-//     sidebarItems.unshift(
-//       { name: "Recording Organization", value: "recordingorganization" },
-//       { name: "Recording Types", value: "recordingtypes" }
-//     );
-//     return (
-//       <Container className="container-sidebar">
-//         <Row noGutters>
-//           <Col xl={2} md={3} sm={12} className="sidebar">
-//             <Sidebar listItems={sidebarItems} listTitle={"Recordings"} />
-//           </Col>
-//           <Col xl={10} md={9} sm={12} className="page__body">
-//             <Container className="container__heatmap">
-//               <Row className="subcontainer justify-content-md-center">
-//                 <Col lg={12} sm={12} xl={12}>
-//                   <div className="intro">
-//                     <p className="big">Recordings</p>
-//                   </div>
-//                 </Col>
-//               </Row>
-//               <div className="finder" id="recordingorganization" />
-//               <Row className="subcontainer justify-content-md-center">
-//                 <Col lg={12} sm={12} xl={12}>
-//                   <div className="card card__std">
-//                     <div className="content">
-//                       <div className="card__label">
-//                         <p>
-//                           <strong>Recording Organization</strong>
-//                         </p>
-//                       </div>
-//                       <div className="card__footer">
-//                         <hr />
-//                         <p>
-//                           Recordings are grouped into "studies". Each study
-//                           contains a set of real or synthesized recordings
-//                           sharing a common source (probe and brain region, or
-//                           simulation code settings). It is appropriate to
-//                           aggregate the statistics from all recordings within a
-//                           particular study.
-//                         </p>
-//                         <p>
-//                           In turn, studies are grouped into "study sets". Each
-//                           study set is a collection of studies which have
-//                           different parameters but share some common features.
-//                         </p>
-//                       </div>
-//                     </div>
-//                   </div>
-//                 </Col>
-//               </Row>
-//               <div className="finder" id="recordingtypes" />
-//               <Row className="subcontainer justify-content-md-center">
-//                 <Col lg={12} sm={12} xl={12}>
-//                   <div className="card card__std">
-//                     <div className="content">
-//                       <div className="card__label">
-//                         <p>
-//                           <strong>Recording Types</strong>
-//                         </p>
-//                       </div>
-//                       <div className="card__footer">
-//                         <hr />
-//                         <p>
-//                           Our hosted recordings include many popular probe
-//                           geometries and types, and fall into three categories:
-//                         </p>
-//                         <div className="list__section">
-//                           <span className="list__heading">
-//                             1. Paired (<i>in vivo</i> or <i>in vitro</i>)
-//                           </span>
-//                           <span className="list__body">
-//                             Recordings from various laboratories where an
-//                             independent intra- or juxta-cellular probe provides
-//                             reliable ground truth firing events, usually for one
-//                             neuron per recording.
-//                           </span>
-//                         </div>
-//                         <div className="list__section">
-//                           <span className="list__heading">
-//                             2. Simulated (in silico)
-//                           </span>
-//                           <span className="list__body">
-//                             Recordings from neural simulator codes with various
-//                             degrees of fidelity, with known firing events for
-//                             large numbers of neurons.
-//                           </span>
-//                         </div>
-//                         <div className="list__section">
-//                           <span className="list__heading">
-//                             3. Human-curated
-//                           </span>
-//                           <span className="list__body">
-//                             We also host a small set of expert human-curated
-//                             sorting results.
-//                           </span>
-//                         </div>
-//                       </div>
-//                     </div>
-//                   </div>
-//                 </Col>
-//               </Row>
-//               {loading ? (
-//                 <Row className="subcontainer justify-content-md-center">
-//                   <Col lg={12} sm={12} xl={12}>
-//                     <div className="card card__std">
-//                       <div className="content">
-//                         <Preloader />
-//                       </div>
-//                     </div>
-//                   </Col>
-//                 </Row>
-//               ) : (
-//                 <div>
-//                   {this.props.studySets.map(studySet => (
-//                     <div key={`study-set-${studySet.name}`}>
-//                       <Row className="subcontainer justify-content-md-center">
-//                         <Col lg={12} sm={12} xl={12}>
-//                           <div className="finder" id={studySet.name} />
-//                           <div className="listcard listcard-recording">
-//                             <div className="listcard-content">
-//                               <div className="listcard-section">
-//                                 <Link
-//                                   to={`/studyset/${studySet.name}`}
-//                                   className="listcard-title"
-//                                 >
-//                                   {studySet.name}
-//                                 </Link>
-//                               </div>
-//                               <div className="listcard-section">
-//                                 <Table striped bordered size="sm">
-//                                   <thead>
-//                                     <tr>
-//                                       <th key="col1">Study name</th>
-//                                       <th key="col2">Number recordings</th>
-//                                       <th key="col3"> </th>
-//                                     </tr>
-//                                   </thead>
-//                                   <tbody>
-//                                     {studySet.studies.map(study => (
-//                                       <tr key={`study-${study.name}`}>
-//                                         <td key="col1">{study.name}</td>
-//                                         <td key="col2">
-//                                           {study.recordings.length}
-//                                         </td>
-//                                         <td
-//                                           key="col3"
-//                                           className="listcard-link"
-//                                         >
-//                                           <Link
-//                                             to={`/study/${study.name}`}
-//                                             className="listcard-env"
-//                                           >
-//                                             View study details
-//                                           </Link>
-//                                         </td>
-//                                       </tr>
-//                                     ))}
-//                                   </tbody>
-//                                 </Table>{" "}
-//                               </div>
-//                             </div>
-//                           </div>
-//                         </Col>
-//                       </Row>
-//                     </div>
-//                   ))}
-//                 </div>
-//               )}
-//               <div style={{ margin: "8rem 0" }} />
-//             </Container>
-//           </Col>
-//         </Row>
-//       </Container>
-//     );
-//   }
-// }
-
 export default Recordings;
diff --git a/src/components/Shared/MarkdownHandling.tsx b/src/components/Shared/MarkdownHandling.tsx
index 12b20e0..044e905 100644
--- a/src/components/Shared/MarkdownHandling.tsx
+++ b/src/components/Shared/MarkdownHandling.tsx
@@ -36,7 +36,11 @@ export type ContentHook = (Section: Section) => JSX.Element | undefined
 // if you just want to render the Markdown-based cards differently, use a ContentHook
 // function that can render the appropriate changes from the Section object (perhaps
 // by looking at the Flavor field, for example).
-export type CopyHook = { Copy: Copy, ContentHook: ContentHook, AdditionalContent?: JSX.Element }
+export type CopyHook = {
+    Copy: Copy,
+    ContentHook: ContentHook,
+    AdditionalContent?: JSX.Element
+}
 
 export type SectionHook = { Section: Section, ContentHook: ContentHook }
 
@@ -131,6 +135,8 @@ export const PageCopy: FunctionComponent<CopyHook> = (Props: CopyHook) => {
                 </Row>
                 { Props.Copy.Sections.map(item => PageCard({Section: item, ContentHook: Props.ContentHook})) }
                 { Props.AdditionalContent }
+                {/* Handles overflow from the bottom margin: */}
+                <div style={{ margin: "8rem 0" }} />
             </Container>
         </Col>
     )

From 8d9d209f2cf105ab00466f86714f8a059e5d0153 Mon Sep 17 00:00:00 2001
From: Jeff Soules <jsoules@flatironinstitute.org>
Date: Fri, 16 Jul 2021 16:54:25 -0400
Subject: [PATCH 3/3] Clarify how the parsing works.

---
 src/components/Shared/MarkdownHandling.tsx | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/components/Shared/MarkdownHandling.tsx b/src/components/Shared/MarkdownHandling.tsx
index 044e905..4b58dba 100644
--- a/src/components/Shared/MarkdownHandling.tsx
+++ b/src/components/Shared/MarkdownHandling.tsx
@@ -83,9 +83,11 @@ export const parseMarkdownToContentCards = (rawMd: string): Copy => {
     // the header and the second element is the (Markdown) section content.
 
     // So finally let's build some Sections. We'll use any parenthesized content
-    // in the section header as the sidebar label; if none, we'll use the exact header.
+    // in the section header as the sidebar label--so e.g. ## My Heading (Sidebar Label)
+    // will appear as "My Heading" in the main page body and "Sidebar Label" in the sidebar.
+    // If the section in parens is omitted, we'll use the same heading for both.
     // The corresponding item ID for the tag system should be the sidebar label,
-    // normalized to lowercase and with spaces removed.
+    // lower-cased & with spaces removed.
     pairs.forEach((pair) => {
         if (pair.length > 2 || !pair[0] || !pair[1]){
             console.log(`Error in pair: ${pair}. Skipping...`)