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README.md

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-# RealtimeDLC_DataRepository
+# Data for Sehara et al., the real-time DeepLabCut project
 
-The data repository for Sehara et al., real-time DeepLabCut (Pose-Trigger) project.
+The data repository for Sehara et al., the real-time DeepLabCut (Pose-Trigger) project.
+
+## Datasets
+
+- [Raw videos](https://gin.g-node.org/larkumlab/RealtimeDLC_RawVideos): the raw videos acquired using [Pose-Trigger](https://github.com/gwappa/python-posetrigger). The files are converted to HDF5 files (instead of the original NumPy files).
+- [Spike2 recordings](https://gin.g-node.org/larkumlab/RealtimeDLC_Spike2Recordings): recordings of the frame and trigger signals during Pose-Trigger acquisition, using Spike2. The files are converted to HDF5 files (instead of the original `.smrx` files).
+- [DeepLabCut projects](https://gin.g-node.org/larkumlab/RealtimeDLC_DLCProjects): the [DeepLabCut (v2.1)](https://github.com/DeepLabCut/DeepLabCut/releases/tag/v2.0.8) projects used in the study.
+- [_Post hoc_ pose estimation](https://gin.g-node.org/larkumlab/RealtimeDLC_PostHocEstimations): the _post-hoc_ pose-estimation data to be compared with the real-time data. The files are in the HDF5 format (in the structure different from the "original" PyTables format that DeepLabCut generates).
+- [Performance profiling](https://gin.g-node.org/larkumlab/RealtimeDLC_PerformanceProfiling): the data and analytical procedures (and some figures) used to profile the speed and accuracy of Pose-Trigger.
+
+## License
+
+Copyright (c) 2020 Keisuke Sehara, Paul Zimmer-Harwood, Matthew E. Larkum, and Robert N.S. Sachdev, [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).

datacite.yml

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+# Metadata for DOI registration according to DataCite Metadata Schema 4.1.
+# For detailed schema description see https://doi.org/10.5438/0014
+
+## Required fields
+
+# The main researchers involved. Include digital identifier (e.g., ORCID)
+# if possible, including the prefix to indicate its type.
+authors:
+  -
+    firstname: "Keisuke"
+    lastname: "Sehara"
+    affiliation: "Institut für Biologie, Humboldt Universität zu Berlin, Berlin, 10117 Germany."
+    id: "ORCID:0000-0003-4368-8143"
+    role: "Project Administration" 
+    role: "Software"
+    role: "Investigation" 
+    role: "Formal analysis"
+    role: "Writing, original draft"
+    role: "Data curation"
+  - firstname: "Paul"
+    lastname: "Zimmer-Harwood"
+    affiliation: "Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, United Kingdom."
+    id: ""
+    role: "Investigation"
+    role: "Software"
+    role: "Writing, review and editing"
+  - firstname: "Julien"
+    lastname: "Colomb"
+    affiliation: "Institut für Biologie, Humboldt Universität zu Berlin, Berlin, 10117 Germany."
+    id: "ORCID: 0000-0002-3127-5520"
+    role: "Data curation"
+  -
+    firstname: "Matthew E."
+    lastname: "Larkum"
+    affiliation: "Institut für Biologie, Humboldt Universität zu Berlin, Berlin, 10117 Germany."
+    id: "ORCID:0000-0002-6627-0199"
+    role: "Funding acquisition"
+    role: "Supervision"
+    role: "Writing, review and editing"
+  -
+    firstname: "Robert N.S."
+    lastname: "Sachdev"
+    affiliation: "Institut für Biologie, Humboldt Universität zu Berlin, Berlin, 10117 Germany."
+    id: "ORCID:0000-0002-3127-5520"
+    role: "Supervision"
+    role: "Resources"
+    role: "Writing original draft"
+
+
+# A title to describe the published resource.
+title: "Data for Sehara et al., the real-time DeepLabCut project"
+
+# Additional information about the resource, e.g., a brief abstract.
+description: |
+  Computer vision approaches have made significant inroads into offline tracking of behavior and estimating animal poses. In particular, because of their versatility, deep-learning approaches have been gaining attention in behavioral tracking without any markers. Here we developed an approach using DeepLabCut for real-time estimation of movement. We trained a deep neural network offline with high-speed video data of a mouse whisking, then transferred the trained network to work with the same mouse, whisking in real-time. With this approach, we tracked the tips of three whiskers in an arc and converted positions into a TTL output within behavioral time scales, i.e 10.5 millisecond. With this approach it is possible to trigger output based on movement of individual whiskers, or on the distance between adjacent whiskers. Flexible closed-loop systems like the one we have deployed here can complement optogenetic approaches and can be used to directly manipulate the relationship between movement and neural activity. 
+
+# Lit of keywords the resource should be associated with.
+# Give as many keywords as possible, to make the resource findable.
+keywords:
+  - Neuroscience
+  - Behavioral tracking
+  - Closed-loop experiment system
+
+# License information for this resource. Please provide the license name and/or a link to the license.
+# Please add also a corresponding LICENSE file to the repository.
+license:
+  name: "Creative Commons 4.0 Attribution"
+  url: "https://creativecommons.org/licenses/by/4.0/"
+
+
+## Optional Fields
+
+# Funding information for this resource.
+# Separate funder name and grant number by comma.
+funding:
+  - "EU, EU.670118"
+  - "EU, EU.327654276"
+  - "EU, EU.720270"
+  - "EU, EU.785907"
+  - "EU, EU.945539"
+  - "DFG, DFG.250048060"
+  - "DFG, DFG.246731133"
+  - "DFG, DFG.267823436"
+
+# Related publications. reftype might be: IsSupplementTo, IsDescribedBy, IsReferencedBy.
+# Please provide digital identifier (e.g., DOI) if possible.
+# Add a prefix to the ID, separated by a colon, to indicate the source.
+# Supported sources are: DOI, arXiv, PMID
+references:
+  -
+    reftype: "IsSupplementTo"
+    id: ""
+    name: "Sehara K, Zimmer-Harwood P, Larkum ME, Sachdev RNS (2021) Real-time closed-loop feedback in behavioral time scales using DeepLabCut."
+
+# Resource type. Default is Dataset, other possible values are Software, DataPaper, Image, Text.
+resourcetype: "Dataset"
+