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Figure 6. A in On the manual morphology of Compsognathus longipes and its bearing on the diagnosis of Compsognathidae
Figure 6. A, left second digit, first phalanx ventral impression. B, right second digit first phalanx lateral impression.
Figure 3 in On the manual morphology of Compsognathus longipes and its bearing on the diagnosis of Compsognathidae
Figure 3. Left first digit, first phalanx shown in proximal oblique view. Arrowheads in drawing show articular facets, highlighted in grey.
Figure 2 in On the manual morphology of Compsognathus longipes and its bearing on the diagnosis of Compsognathidae
Figure 2. Right first digit, first and second (ungual) phalanges. Hash marks in drawing represent broken surface; stipples, obscured bone. Arrow shows flattened proximoventral extension of articular condyles typical of theropod I-1.
Figure 1 in On the manual morphology of Compsognathus longipes and its bearing on the diagnosis of Compsognathidae
Figure 1. Overview of the preserved manual material in the Munich holotype of Compsognathus longipes, drawing isolates manual elements with lines representing preserved bone and dotted lines representing element impressions (inset: metacarpal I from top of slab, shown in preserved orientation relative to other manual elements). Differing author's interpretations of element identity labelled as follows: 1, l. metacarpal I (this paper), mystery bone (Ostrom, 1978); 2, l. metacarpal II (this paper; von Huene, 1925), l. metacarpal I (Ostrom, 1978), l. metacarpal III (Gauthier et al., 1988); 3, l. metacarpal III (this paper; von Huene, 1925; Ostrom, 1978), l. metacarpal IV (Gauthier et al., 1988); 4, l. ph I-1 (this paper), l. metacarpal II (von Huene, 1925), l. metacarpal II (Ostrom, 1978; Gauthier et al., 1988); 5, l. ph I-2 (this paper), r. ph II-2 (Ostrom, 1978), r. ph II-3 (von Huene, 1925); 6, l. ph II-1 (this paper; von Huene, 1925), l. ph I-1 (Ostrom, 1978); 7, l. ph II-2 (this paper), l. ph I-1 (von Huene, 1925), l. ph II-1 (Ostrom, 1978); 8, l. ph II-3 (this paper), l. ph I-2 (von Huene, 1925), l. ph II-2 (Ostrom, 1978); 9, ph III-1?; 10, r. metacarpal II (this paper; von Huene, 1925; Ostrom, 1978), r. metacarpal III (Gauthier et al., 1988); 11, r. metacarpal III (this paper; von Huene, 1925; Ostrom, 1978); r. metacarpal IV (Gauthier et al., 1988); 12, r. ph I-1 (this paper; von Huene, 1925), r. mc I (Ostrom, 1978); 13, r. ph I-2 (this paper; von Huene, 1925; Ostrom, 1978); 14, r. ph II-1 (this paper; von Huene, 1925), r. ph I-1 (Ostrom, 1978); 15, r. ph II-2 (this paper; von Huene, 1925), r. ph II-1 (Ostrom, 1978); 16, r. ph II-3 (this paper), l. ph I-1 (Ostrom, 1978), l. ph II-3 (von Huene, 1925); 17, l. dc 1?.
Pollen Images for OLEAtool Manual Measurements Demonstration
<p>This dataset includes the following:</p> <p>Images [JPEG format] of <em>Olea</em> pollen (<em>Olea europaea</em> L.) used to collect Polar axis diameter (P) and Equatorial axis diameter (E) measurements using the Manual Measurements Module in OLEAtool (https://doi.org/10.5281/zenodo.7046771).</p> <p>Measurements (P and E metrics) collected from the included images using OLEAtool Manual Measurements Module [XLXS format].</p>
OSH Automated Documentation - Creating an Assembly Manual for a Vise Video
<p>A video that shows how an assembly manual of a simple vise is generated (semi) automatically from a CAD specification and a textual description.</p>
Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express
<p>Bow shock crossings at Venus identified manually from the ASPERA-4 and MAG instruments onboard Venus Express for the full mission from 2006 to 2014.</p> <p>A detailed description of the dataset can be found in the paper "Influence of solar wind variations on the shapes of Venus’ plasma boundaries based on Venus Express observations" by Signoles et al.</p> <p>The boundary crossings by Venus Express are determined from combining the measurements of both ion, electron and magnetic field measurements. The bow shock is identified from the sharp increase in the magnetic field magnitude, and the increase in electron and ion temperature. The ion composition boundary is identified from the decrease in magnetosheath protons and electrons, and the appearance of planetary heavy ions.</p> <p>The dataset contains 5193 identified bow shock crossings and 2679 identified ion composition boundary crossings.</p> <p>For more information on the dataset contact: M. Persson, moaperssonphd at gmail.com</p>
Dataset for Manually Coded Firm Founding Dates, CEO Duality, and CEO Photograph Prominence in Annual Reports
<p>In this dataset, we wish to make available some measures which we manually coded as part of our PhDs. It contains manually coded information on S&P1500 firms and their CEOs, specifically, the firm’s founding year, whether the CEO is simultaneously chairperson of the board (CEO duality), and the prominence of the CEO in photographs in the firm’s annual report. More information can be found in the attached documentation. Both authors contributed equally to the creation of this dataset.</p> <p>Since this is the first version of this dataset, we are happy about any feedback, corrections, or additions. Please reach out to us in case you spot any irregularities or have questions via mappels@mail.uni-mannheim.de or mkowalzi@staff.mail.uni-mannheim.de. </p> <p> </p> <p>The creation of this dataset was supported by the <a href="https://de.wikiversity.org/wiki/Wikiversity:Fellow-Programm_Freies_Wissen">Fellowship Freies Wissen</a> of the Wikimedia Foundation. More information on the project can be found <a href="https://de.wikiversity.org/wiki/Wikiversity:Fellow-Programm_Freies_Wissen/Einreichungen/Lowering_the_Barriers_for_Young_Strategy_Scholars:_An_Open-Access_Database_for_Manually_Computed_Variables_of_Strategy_Research">here</a>.</p>
Manually labeled Bird song dataset of 22 species from Xeno-canto to enhance deep learning acoustic classifiers with contextual information.
<p>Data accompanying the paper: Jeantet and Dufourq (2023). Empowering Deep Learning Acoustic Classifiers with Human-like Ability to Utilize Contextual Information for Wildlife Monitoring. <em>Ecological Informatics</em>. 77, 15749541, DOI: 10.1016/j.ecoinf.2023.102256</p> <p> </p> <p>Our investigation contributes to the field of deep learning and bioacoustics by highlighting the potential for improved classification performance through the incorporation of contextual information such as time and location.</p> <p>To test if spatial-temporal information can enhance deep learning classifier, we developed a subset dataset derived from Xeno-Canto that included location metadata as input alongside the spectrogram. We used this dataset with the primary purpose of creating a bird song classification task with species carefully selected to share similar vocal characteristics but from distinct geographical distributions. We only considered the recordings of category `A', corresponding to the best quality score in the database.</p> <p>The dataset contains songs of <strong>22 bird species</strong> from 5 families and genera differents. The recordings were downloaded from the Xeno-canto database in .wav format and each recording was <strong>manually annotated </strong>by labelling the start and stop time for every vocalisation occurrence using Sonic Visualiser. In total, database contained 6537 occurrences of bird songs of various length from <strong>967 file recordings</strong>. A precise description of the distribution by species and country can be found in the associated article.</p> <p> </p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip". The name of each file follows the following nomenclature: Family_genus_species_country of recording_date of recording_ID Xenocanto_type of song.wav/svl. The meta-data information of each file can be find in the csv file provided (Xenocanto_metadata_qualityA_selection) based on the number of the ID Xeno-canto. The annotations can be viewed using the Sonic Visualiser software. The python codes to process these files and train neural networks can be found here : github</p> <p>The files were divided into a <strong>training folder</strong> and a<strong> validation folder</strong> to train and evaluate the efficiency of each method. For each species and country, we randomly selected 70% of the downloaded recordings for the training dataset and kept the remaining 30% for validation.</p> <p><strong>Process to select the species</strong> : We selected the ten most recorded families in the Passeriformes order, the most represented order in Xeno-canto database. From each of the ten families, we again sub-samples the ten most recorded genera. For each genus, we observed the countries of the recordings and the number of available recordings per species and countries. From these observations, we made a self-selection of genera containing species with similar songs but recorded in different regions, with enough recordings available by species and country to form a dataset . At the end, 5 genus were selected containing 22 species. We considered only recordings associated with bird songs, specifically, within Xeno-canto we selected the `song' type. To balance the number of recordings between species of the same genus, we reduced the number of recordings for the most represented species. Thus, for each genus we calculated the average of the number of records available per species and per country and limited the number of recordings for the species/country pairs that were in greater number to this value plus two.</p> <p> </p> <p> </p> <p> </p>
childTale-A: A corpus of eighty fairy tales from the 7th edition by the Brothers Grimm, manually annotated for textually encoded emotions
<p>The childTale-A corpus is a collection of eighty fairy tales, a core set of the Grimms’ Children's and Household Tales as introduced in Herrmann & Lüdtke (2023). Within the CHYLSA project, textually encoded emotions were annotated in each sentence in each of the eighty fairy tales. Annotations were collected for the dimensions <em>valence</em> and <em>arousal</em>, as well as for the six basic emotions <em>anger</em>, <em>disgust</em>, <em>fear</em>, <em>joy</em>, <em>sadness</em>, and <em>surprise</em>. Each fairy tale was annotated by two persons (for details see Hermann & Lüdtke, 2023).</p> <p>In detail, this dataset contains:</p> <ul> <li>instructions and texts used for annotation (zip-files): <ul> <li>all N=80 fairy tales as txt-files (with normalised orthography)</li> <li>instructions (in German) for the valence and arousal annotation as well as for the annotation of the six basic emotions</li> </ul> </li> <li>scripts for preparing and analysing annotations for textually encoded emotions: <ul> <li>Python scripts to prepare Excel files for annotation (including a script to separate texts into individual sentences)</li> <li>R-scripts for data preparation, calculation of inter-rater reliability and smoothing of the valence annotations (discrete cosine transformation (DCT) with length normalisation) <ul> </ul> </li> </ul> </li> <li>sentence-level data (for each sentence in each of the eighty fairy tales): <ul> <li>annotations for the dimensions valence and arousal (continuous values)</li> <li>categorisation of each sentence (as negative, neutral or positive) based on the continuous valence annotation</li> <li>annotations on the occurrence of the six basic emotions anger, disgust, fear, joy, sadness, and surprise</li> </ul> </li> <li>transformed and length-normalised valence annotations as basis for the <em>Emotional Arcs</em> (DCT and length normalisation results in one hundred data points for each fairy tale, saved in a separate data file)</li> <li>text-level data (for each of the eighty fairy tales): <ul> <li>general information, for example title in German and English, number of sentences, Kinder- und Hausmärchen-ID (KMH-ID), corpus ID</li> <li>results of the analysis of the annotated data with values for: <ul> <li><em>Average Valence</em> and <em>Average Arousal</em></li> <li>inter-rater reliability index (Krippendorff's alpha coefficient) for the valence and arousal annotations</li> <li>proportions of positive, negative and neutral sentences</li> <li><em>Emotion Potential</em> (percent of both positive and negative sentences)</li> <li><em>Valence Span</em>, <em>Arousal Span</em> and range of the <em>Emotional Arc</em></li> <li><em>Emotion Profile</em> (relative frequency for each of the six basic emotions <em>anger</em>, <em>disgust</em>, <em>fear</em>, <em>joy</em>, <em>sadness</em>, and <em>surprise)</em></li> <li>inter-rater reliability indices (Krippendorff's alpha coefficient and the percentage of agreement) for each of the six basic emotions</li> </ul> </li> </ul> </li> </ul> <p>Reference: <br> Herrmann, Berenike & Lüdtke, Jana (2023). A Fairy Tale Gold Standard. Annotation and Analysis of Emotions in the Children's and Household Tales by the Brothers Grimm.<em> </em>Zeitschrift für digitale Geisteswissenschaften (ZfdG). DOI: 10.17175/2023_005.</p> <p> </p> <p>DFG Schwerpunktprogramm SPP 2207 “Computational Literary Studies“<br> Online:</p> <ol> <li><a href="https://gepris.dfg.de/gepris/projekt/402743989">https://gepris.dfg.de/gepris/projekt/402743989</a></li> <li><a href="https://dfg-spp-cls.github.io/">https://dfg-spp-cls.github.io<em>/</em></a></li> </ol> <p>Teilprojekt: „CHYLSA - Children’s and Youth Literature Sentiment Analysis“</p> <p>Online:</p> <ol> <li><a href="https://gepris.dfg.de/gepris/projekt/424250469">https://gepris.dfg.de/gepris/projekt/424250469</a></li> <li><a href="https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-CHYLSA/">https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-CHYLSA/</a></li> </ol>
Manual classification of dataset and software mention contexts as used, created or shared
<p>This dataset consists of two JSON files containing:</p> <p>- software-contexts.json: 49195 sentences from the SoMeSci and Softcite datasets with 3162 software mentions and with added manual indication if the mentioned software is used, created and/or shared,</p> <p>- dataset-software-extra-contexts.json: an additional set of 481 sentences with dataset or software mentions and with added manual indication if the mentioned software is used, created and/or shared.</p> <p>Softcite dataset: <a href="https://zenodo.org/record/7995565">https://zenodo.org/record/7995565</a></p> <p>SoMeSci dataset: <a href="https://zenodo.org/record/4968738">https://zenodo.org/record/4968738</a></p>
List of manuscripts containing John Chrysostom's Homilies and the relevant manual transcriptions
<p>This dataset consists of a <strong>list of all manuscripts</strong> (in the form of a .csv file) used as data in experiments with HTR training via Transkribus. The manuscripts are dated between the 10th-14th centuries and transmit John Chrysostom’s <em>Homilies on St. Paul’s Epistles to Titus</em>. Homilies 1 and 5 were exploited for the training process. In addition, <strong>19 XML source files</strong> are provided in the <strong>TEI standards</strong> format, which contains a sample of the manual transcription used as ground truth data for training HTR models.</p> <p>Specifically, the <strong>sample_dataset_chrysostomus_ad-titum.csv</strong><strong> </strong>file includes the following columns:</p> <ul> <li><strong>Sigla:</strong> a capital letter used in critical editions to refer to a specific manuscript in an abbreviated form.</li> <li><strong>Manuscripts: </strong>the name of each manuscript, containing the library and the catalogue number assigned to it.</li> <li><strong>Folia: </strong>the folia (i.e., pages) of each manuscript used in the experiments. A different sequence of folia from the same manuscript is recorded in a separate row of this file.</li> <li><strong>Ground truth data sample [file_name]: </strong>the file name of the TEI/XML files that correspond to each manuscript.</li> <li><strong>Image files: </strong>most digital reproductions of manuscripts are under some degree of copyright protection. So, instead of the image files, in this column, one can find a link to the relevant library's digital archive (if applicable).</li> </ul> <p><strong>**IMPORTANT NOTE:</strong> Version 1 contained an erroneous file. Please use only version 1.2.</p>
Colombia. Justicia. Drogas. Municipal. Erradicacion Manual 1994 2022
<p>Colombia. Justicia. Drogas. Municipal. Erradicacion Manual 1994 2022</p>
Codes and catalogs for: Parametric testing of EQTransformer's performance against a high-quality, manually-picked catalog for reliable and accurate seismic phase picking
<p><strong>Codes and Catalogs for:</strong> "Parametric Testing of EQTransformer's Performance Against a High-Quality, Manually-Picked Catalog for Reliable and Accurate Seismic Phase Picking."</p> <p><strong>Codes:</strong></p> <ol> <li><strong>overlap_check.py:</strong> This script tests the overlap parameter of EQTransformer to help minimize detection inconsistencies.</li> <li><strong>picks_comparison_other_networks.py:</strong> This code evaluates the probability threshold of EQTransformer by obtaining the time differences between picks from a catalog and EQTransformer.</li> <li><strong>test_seisbench_eq_eqt.py:</strong> A comparative analysis between the native EQTransformer and its implementation in SeisBench.</li> </ol> <p><strong>Catalogs:</strong></p> <ol> <li><strong>picks_differences_all_years_0.01_mag_cat.csv:</strong> This catalog presents pick differences for the central Alpine Fault using the SAMBA network and manual picks from Michailos et al. (2019).</li> <li><strong>sed_picks.csv:</strong> A catalog that showcases pick differences derived from data obtained from the Swiss Seismological Service (SED).</li> </ol> <p><strong>Note:</strong> Versions <1.0 represent pre-acceptence files and should not be used.</p>
Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions - Dataset and Results
<p>This dataset contains the replication package for the paper: Federico Formica, Tony Fan, and Claudio Menghi. 2023. "Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions".</p> <p>The dataset contains:</p> <ul> <li>The models used in the evaluation section.</li> <li>All the results obtained by running Athena-S on the models.</li> <li>The scripts that automatically analyze the results file and produce the tables and figures used in the paper.</li> </ul>
EDDi Paper Manual - Introduction
<p><strong>EDDi Print Manual - An Epidemic of Haemolytic Uraemic Syndrome in Hamburg</strong></p> <p><em><strong>Summary:</strong></em><br> Building on a problem-based self-study approach, <strong>EDDi</strong> participants will be introduced to the principles and practices of infectious disease epidemiology as well as concepts related to the investigation of disease outbreaks, with particular focus on:</p> <ol> <li>Describing infectious disease events and evaluating local outbreak scenarios</li> <li>Learning about basic methods of epidemiological outbreak investigation, study designs, and tools of epidemiology (e.g., epidemiological curves, outbreak maps, technical terms)</li> <li>Interpreting and recognizing associations between outbreak events and relevant influencing factors by applying basic techniques of infectious disease epidemiology (especially descriptive methods)</li> <li>Making informed decisions in outbreak investigation based on epidemiological evidence</li> <li>Extra: Getting to know the structure of the German disease surveillance and reporting system as well as background information on the Hanseatic city of Hamburg<br> </li> </ol> <p><strong><em>Preparation of the Table Top Exercise</em></strong></p> <p><em>Resources</em><br> <a href="https://zenodo.org/record/8362813">1 EDDi Case Study: Description of the case scenario including tasks</a><br> <a href="https://zenodo.org/record/8363360">2 EDDi Investigation Notebook: Reference materials relevant to the case study</a><br> <a href="https://zenodo.org/record/8363447">3 EDDi Solution Guide: Guide for facilitators including solutions</a></p> <p><em>Approximate time (classroom teaching set-up)</em><br> Approx. 2.5-3 hours (estimate) = approx. four course units á 45 minutes;<br> the course units may be spread over two sessions á 1.5 hours</p> <p><em>Welcome Tutorial and Epidemiological Cheat Sheet</em><br> As the Table Top Exercise requires a basic understanding of infectious disease epidemiology concepts and terminology, refreshing some background knowledge through the <a href="https://zenodo.org/record/8365637"><strong><em>EDDi Welcome Tutorial</em></strong></a> is recommended, either individually or in small groups. There is a total of six educational videos to prepare for the different tasks of the exercise. The tutorial can be given to students as preparatory homework and could be further supplemented with additional questions. In addition, students may be advised to familiarize themselves with the <strong><a href="https://zenodo.org/record/8363589"><em>Epidemiology Cheat Sheet</em></a> </strong>and bring a copy.</p> <p><em>Materials needed</em><br> Although extensive, we advise printing the <a href="https://zenodo.org/record/8362813"><em><strong>EDDi Case Study</strong></em></a> and <a href="https://zenodo.org/record/8363360"><em><strong>EDDi Investigation Notebook</strong></em></a> (one set per group). The printouts may be reused provided that all tasks are solved by the students on an extra sheet. A calculator is needed for some of the tasks.</p> <p><em>Teaching Facilitators</em><br> To facilitate the exercise, we recommend having one facilitator (e.g., lecturer, tutor, experienced student) per working group who can assist with any problems. For each facilitator, we recommend providing one <a href="https://zenodo.org/record/8363447"><em><strong>EDDi Solution Guide</strong></em></a>. If this is not possible, one solution guide can be given to each group to assist with any questions or problems.</p> <p><em>Workflow</em><br> #1 Make sure you have completed all preparations and all printouts are available.<br> #2 Before starting the exercise, explain the overall outline of the exercise (learning objectives, resources needed, time to complete the exercise) and assign the course to working groups (3-5 individuals and, if possible, one facilitator per group). Each group shall be provided with one <a href="https://zenodo.org/record/8362813"><em><strong>EDDi Case Study</strong></em></a> and one <a href="https://zenodo.org/record/8363360"><em><strong>EDDi Investigation Notebook</strong></em></a> printout. The <a href="https://zenodo.org/record/8362813"><em><strong>Case Study</strong></em></a> provides the main storyline and tasks and points to important reference materials in the <a href="https://zenodo.org/record/8363360"><em><strong>Investigation Notebook</strong></em></a>. The groups will then work on the exercise independently.<br> #3 During the exercise, assistance can be sought through the facilitators or the <a href="https://zenodo.org/record/8363447"><em><strong>Solution Guide</strong></em></a>. Make sure to keep an eye on the time!<br> #4 The exercise should be closed with a final discussion and exchange of findings and experiences (approx. 15-20 minutes). To learn more about the real outbreak event, further reading materials can be found in the<em> </em><a href="https://zenodo.org/record/8363360"><em><strong>Investigation Notebook</strong></em></a> and shared with the class.</p> <p><br> <em>Extra: EDDi Artwork Table Top Exercise</em><br> Artwork by Annabell Koenen-Rindfrey, licensed under CC BY 4.0 <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>.<br> The artwork was developed using Affinity Designer 1.9.9 (iPad version).<br> Epidemiological trends by Juliane Boenecke, licensed under CC BY 4.0 <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>.<br> Graphs were developed using software R Studio (Version 1.0.136),<br> packages rgdal, sp, ggplot2, ggsave, readr and base.</p> <p>If you would like to revise, rewrite, or build a new narrative upon this case study, all materials are available under an open license.</p>
Structural Fat Grafting for Craniofacial Trauma Using Manual Technique for Processing Fat Graft Material
ClinicalTrials.gov study NCT02267187. IPD Sharing: YES. Countries: 1. Publications: 1.
ReviewR: A light-weight and extensible tool for manual review of clinical records
Open the record for dataset details and reuse information.
Dataset for worker activity recognition and efficiency estimation during manual harvesting
Open the record for dataset details and reuse information.
Manual annotation of individual ovenbirds in acoustic recordings
This dataset includes acoustic recordings and annotations of those recordings for individually identified Ovenbird songs (Seiurus aurocapilla) created from annotating randomly selected passive acoustic monitoring recordings, largely collected in Alberta, CA. Every Ovenbird song within each recording is tagged in time and frequency. Each Ovenbird tag is labeled with individual identity a number corresponding to each individual’s order of appearance in the recording. If no Ovenbirds were not detected in a given recording, the recording was labeled at approximately 2 seconds into the recording with a three-second long tag containing species label “NONE.” Recordings contain other species’ songs, but no other species are tagged. This dataset was annotated via the WildTrax interface (https://wildtrax.ca/) and can also be found at https://portal.wildtrax.ca/home/aru-tasks.html?sensorId=ARU&projectId=623. These recordings were intended to be used for studies of individual ID, but can be used for a variety of other applications, including count data, song rate and vocal activity, and migratory phenology. Because each individual Ovenbird is numbered, these data can be also be transformed into count data or used to estimate individual Ovenbirds’ song rates. Data were randomly selected with respect to recording time, recording date, and recording location. Because recording time was randomly selected, the dataset can be used to estimate Ovenbird vocal activity over the course of a day. Because recordings were selected from a date range that includes dates before Ovenbirds are likely to be present in Alberta, the dataset can be used to estimate phenology of Ovenbird migration. This dataset is intended as an example open access labeled dataset to encourage development in the field of acoustic individual identification (Knight et al. In Revision for Trends in Ecology and Evolution).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.