Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
40,091
datasets available to search
ShareScore release 0.9.0
Dataset results
40,091 results for “records”
Figure 4b. from: New records of two endemic troglobitic and threatened arachnids (Amblypygi and Opiliones) from limestone caves of Minas Gerais state, southeast Brazil - Biodiversity Data Journal 3: e5260 (10 November 2015) https://doi.org/10.3897/BDJ.3.e5260
Figure 4b. - Charinuseleonorae, female from Lapa do Cipó cave.Figure 4a.Carapace, dorsal view, showing mid-portion of carapace without median eyesFigure 4b.Habitus, dorsal viewFigure 4c.Lateral view of right pedipalp showing spines. <br> Habitus, dorsal view
Understanding in vivo Models of Depression: A Systematic Review - Records of Full Search
<p>This Zenodo record outlines the full list of journal articles retrieved from the search string as well as a a subset of articles that have been screened by two independent human reviews and reconciled by a third independent screener. </p> <p> </p> <p>We carried out a search of 2 online databases for studies reporting animal models of depression. This search, carried out in May 2016, identified 70,365 unique publications (File: Depression-Dataset-SLIM-AllRecords.txt )</p> <p> </p> <p>Two independent investigators have, to date, screened 5749 of these publications for inclusion or exclusion and these publications form the dataset for this study (File: Updated-training-data.txt ). </p> <p>Several text-mining approaches will be developed for this depression literature search using the results from manual screening to train the machine, where machine-learning software set rules to automatically define each publication as included or excluded without the need for human screening. In this project, we seek to identify the best performing machine learning algorithm for this depression literature search. Performance is measured on sensitivity, specificity, and precision. </p> <p> </p> <p>Column Names in Datasets: </p> <p>Depression-Dataset-SLIM-AllRecords.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, SetNumber</p> <p>Depression-Dataset-SLIM-DevelopmentTrainingSet.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, Incl(1)/Excl(0)</p> <p>Updated-training-data.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, Incl(1)/Excl(0)</p> <p> </p>
I/Q Recording for 160 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 160 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
I/Q Recording for 80 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 80 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
Eclipse 2017 recording of lower 17m ham band from grid EM93sl
<p>Peter Kobak, K0BAK</p> <p>Wideband recording of the digital/CW portion of the 17 meter ham band,<br> from a location a few miles from the eclipse centerline in South Carolina:</p> <p>33.485145N, 80.475172W</p> <p>.wav file sample rate 48000Hz</p> <p>Recorded IQ from a Flex Radio model 1500, tuned to 18.094MHz</p> <p>PowerSDR panadapter indicated a range 18.068 to 18.111,<br> but Flex says the displayed bandwidth of the radio is about 32kHz</p> <p>Start time (approx): <br> Aug. 21, 2017,<br> 12:22:32 UTC<br> End time (approx):<br> 22:16:26 UTC</p> <p>Antenna:<br> 36-inch diameter "magnetic" loop.<br> Position was inside a third floor hotel room,<br> with the loop a few inches from the window.<br> </p> <p>See .txt file for more details</p>
I/Q Recording for 15 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 15 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
I/Q Recording for 10 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 10 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
I/Q Recording for 20 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 20 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
I/Q Recording for 6 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 6 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
I/Q Recording for 40 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 40 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
Wideband I/Q Recording at VU2PTT for 160m Ham Radio Band during August 21, 2017 Solar Eclipse in USA
<p>Recorded by Prasad Rajagopal, VU2PTT in Bangalore, India</p> <p>Location: Latitude 12°58'24.8"N, Longitude 77°39'15.7"E</p> <p>Grid Locator: MK82tx</p> <p>Recorded using Skimmer Server & CWSL File</p> <p>Playback: Each time stamped I/Q file is a 15 minute slice and can be played back with HDSDR software</p> <p>Antenna was a full size 1/4 wave vertical on 40m (7 MHz)</p> <p>Receiver was a QS1R SDR capable of simultaneously recording 7 bands @ 192 kHz bandwidth </p>
Twenty four hour continuous tympanic temperature recordings in healthy volunteers and patients presented with Undifferentiated fever
<p>Twenty-four-hour continuous tympanic temperature recordings were obtained using high-accuracy tympanic probes placed at the auditory canal. Measurements were recorded at one-minute intervals over a 24-hour period, yielding 1,440 data points per subject and enabling high-resolution temporal profiling of body temperature.</p> <p>In Phase I, a total of 100 healthy adult volunteers were recruited to establish baseline circadian thermoregulatory patterns. These recordings were stratified by gender to examine physiological variability among individuals without fever.</p> <p>In Phase II, 184 adult patients presenting with undifferentiated fever of seven or more days’ duration were enrolled. Based on clinical examination and laboratory confirmation, the temperature profiles were analysed across a range of conditions, including tuberculosis, non-tubercular bacterial infections, dengue fever, malaria, leptospirosis, pyogenic sepsis, thyroiditis, malignancies, and non-infectious inflammatory diseases. This dual-phase dataset facilitated a comparative analysis between normative and pathological thermoregulatory patterns, supporting the development of classification models for diagnostic differentiation.</p>
Occurrence Record Dataset from "Depth Matters for Marine Biodiversity"
<p>This is the final occurrence record dataset produced for the manuscript "Depth Matters for Marine Biodiversity". Detailed methods for the creation of the dataset, below, have been excerpted from Appendix I: Extended Methods. Detailed citations for the occurrence datasets from which these data were derived can also be foud in Appedix I of the manuscript.</p> <p><span>We first assembled a list of all recognized species of fishes from the orders Scombiformes</span><span> (Betancur-R et al., 2017)</span><span>, Gadiformes, and Beloniformes by accessing FishBase</span><span> (Boettiger et al., 2012; Froese & Pauly, 2017)</span><span> and the Ocean Biodiversity Information System (OBIS; </span><span>OBIS, 2022; Provoost & Bosch, 2019)</span><span> through queries in R</span><span> (R Core Team, 2021)</span><span>. Species were considered Atlantic if their FishBase distribution or occurrence records on OBIS included any area within the Atlantic or Mediterranean major fishing regions as defined by the Food and Agriculture Organization of the United Nations (FAO Regions 21, 27, 31, 34, 37, 41, 47, and 48;</span><span> FAO, 2020)</span><span>. The database query script can be found on the project code repository (</span><a href="https://github.com/hannahlowens/3DFishRichness/blob/main/1_OccurrenceSearch.R"><span>https://github.com/hannahlowens/3DFishRichness/blob/main/1_OccurrenceSearch.R</span></a><span>). We then curated the list of names to resolve discrepancies in taxonomy and known distributions through comparison with the Eschmeyer Catalog of Fishes</span><span> (Eschmeyer & Fricke, 2015)</span><span> , accessed in September of 2020, as our ultimate taxonomic authority. The resulting list of species was then mapped onto the Global Biodiversity Information Facility’s backbone taxonomy</span><span> (Chamberlain et al., 2021; GBIF.org, 2020a)</span><span> to ensure taxonomic concurrence across databases (Supplementary Table 1). The final taxonomic list was used to download occurrence records from OBIS</span><span> (OBIS, 2022)</span><span> and GBIF</span><span> (GBIF.org, 2020b)</span><span> in R through <em>robis</em></span><span> (Provoost & Bosch, 2019)</span><span> and <em>occCite</em></span><span> (Owens et al., 2021)</span><span>. </span></p> <p><span><span> </span>For each species, duplicate points were removed from two- and three-dimensional species occurrence datasets separately, and inaccurate depth records were removed from 3D datasets (all records with and without depth information were retained for the 2D dataset). Depth records were based on the “depth” field in both the GBIF and OBIS datasets, which define the field as “depth below the surface in meters”. We chose this value over incorporating information from “minimumDepthInMeters” and “maximumDepthInMeters” because more records contained information from the “depth” field than either of the two other fields (although when these fields were both supplied, “depth” appears to have been often, but not always, derived by calculated the mean between minimum and maximum depth). We also initially included the “depthAccuracy” field from both datasets but did not ultimately use this field as it was not complete enough to be useful. Instead, we determined depth inaccuracy based on extreme statistical outliers (values greater than 2 or less than -2 when occurrence depths were centered and scaled), depths that exceeded bathymetry at occurrence coordinates, and occurrence depths far outside known depth ranges obtained from FishBase, Eschmeyer’s Catalog of Fishes, and/or congeneric depth ranges in the dataset. Once the resulting data were mapped and curated to remove records with putatively spurious coordinates, under-sampled regions and species were augmented with data from publicly available digital museum collection databases not served through OBIS or GBIF, as well as a literature search. Finally, for datasets with more than 20 points remaining after data curation, occurrence data were downsampled to the resolution of the environmental data; that is, to 1 point per 1 degree grid cell in the 2D dataset, and to one point per depth slice per 1 degree grid cell in the 3D dataset. </span></p> <p> </p> <p>References:</p> <p>Betancur-R, R., Wiley, E. O., Arratia, G., Acero, A., Bailly, N., Miya, M., Lecointre, G., & Ortí, G. (2017). Phylogenetic classification of bony fishes. <em>BMC Evolutionary Biology</em>, <em>17</em>(1), 162. <a href="https://doi.org/10.1186/s12862-017-0958-3">https://doi.org/10.1186/s12862-017-0958-3</a></p> <p>Boettiger, C., Lang, D. T., & Wainwright, P. C. (2012). rfishbase: exploring, manipulating and visualizing FishBase data from R. <em>Journal of Fish Biology</em>, <em>81</em>(6), 2030–2039. <a href="https://doi.org/10.1111/j.1095-8649.2012.03464.x">https://doi.org/10.1111/j.1095-8649.2012.03464.x</a></p> <p>Chamberlain, S., Barve, V., McGlinn, D., Oldoni, D., Desmet, P., Geffert, L., & Ram, K. (2021). <em>rgbif: Interface to the Global Biodiversity Information Facility API</em>. <a href="https://CRAN.R-project.org/package=rgbif">https://CRAN.R-project.org/package=rgbif</a></p> <p>Eschmeyer, & Fricke, W. N. &. (2015). Taxonomic checklist of fish species listed in the CITES Appendices and EC Regulation 338/97 (Elasmobranchii, Actinopteri, Coelacanthi, and Dipneusti, except the genus Hippocampus). <em>Catalog of Fishes, Electronic Version</em>. Accessed September, 2020. <a href="https://www.calacademy.org/scientists/projects/eschmeyers-catalog-of-fishes">https://www.calacademy.org/scientists/projects/eschmeyers-catalog-of-fishes</a></p> <p>FAO. (2020). <em>FAO Major Fishing Areas</em>. United Nations Fisheries and Aquaculture Division. <a href="https://www.fao.org/fishery/en/collection/area">https://www.fao.org/fishery/en/collection/area</a></p> <p>Froese, R., & Pauly, D. (2017). <em>FishBase</em>. Accessed September, 2022. www.fishbase.org</p> <p>GBIF.org. (2020a). <em>GBIF Backbone Taxonomy</em>. Accessed September, 2020. GBIF.org</p> <p>GBIF.org. (2020b). <em>GBIF Occurrence Download</em>. Accessed November, 2020. <a href="https://doi.org/10.15468">https://doi.org/10.15468</a></p> <p>OBIS. (2020). <em>Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO</em>. Accessed November, 2020. www.obis.org</p> <p>Owens, H. L., Merow, C., Maitner, B. S., Kass, J. M., Barve, V., & Guralnick, R. P. (2021). occCite: Tools for querying and managing large biodiversity occurrence datasets. <em>Ecography</em>, <em>44</em>(8), 1228–1235. <a href="https://doi.org/10.1111/ecog.05618">https://doi.org/10.1111/ecog.05618</a></p> <p>Provoost, P., & Bosch, S. (2019). <em>robis: R Client to access data from the OBIS API</em>. <a href="https://cran.r-project.org/package=robis">https://cran.r-project.org/package=robis</a></p> <p>R Core Team. (2021). <em>R: A Language and Environment for Statistical Computing</em>. <a href="https://www.R-project.org/">https://www.R-project.org/</a></p>
RPC-Net Dataset. Simultaneous HD-sEMG Recordings on the Forearm and angles of a 29-DOF Hand Kinematic Model
<p>The dataset in this repository comprises data acquired during the doctoral research project of Giovanni Rolandino at the Nuffield Department of Surgical Sciences, University of Oxford. Five sub-datasets make up the repository:</p> <p>DS1: Simultaneous acquisition of high-density surface electromyography (HD-sEMG) signals from the forearm and hand position kinematics. Data were recorded from 12 healthy subjects while they cycled through 16 hand poses. HD-sEMG was acquired with traditional gel electrode arrays.</p> <p>DS2: A similar protocol to DS1 was followed, but the HD-sEMG was acquired using a novel dry-electrode array. This dataset includes 16 subjects. Whereas DS1 included data from a single session for each subject, DS2 includes two sessions, acquired hours to days apart; these sessions are identified as s1 and s2.</p> <p>DS3: This subset consists of two parts. DS3.a repeats the protocol used in DS2 with 4 subjects, introducing repositioning between trials. DS3.b includes the results of the real-time assessment of RPC-Net, a shallow neural network trained with data from DS3.a to estimate hand position from HD-sEMG activity. DS3.b contains the real-time output recorded during prompt-matching tasks and the corresponding targets.</p> <p>DS4: This subset includes data related to the assessment of RFC-Net, a shallow neural network designed to estimate hand position from neck muscle activation. Experiments were performed on 8 healthy participants and 8 participants with tetraplegia. DS4.a includes the data used for training the network, while DS4.b includes data from the testing phase of the algorithm. DS4.b.s1 includes results from a cursor control task, and DS4.b.s2 includes results from a virtual hand control task.</p> <p>AD1: Additional data related to the electrical validation of the dry-electrode array.</p> <p>Code for processing the data in this repository is available on Dropbox:<br>https://www.dropbox.com/scl/fo/nkvbse7evo0k8ou1utn7i/AMDh_MOZQJ6gCwDXGPadmZ0?rlkey=ynoix3anpc81v24hogn3fymb4&st=xrqx07q2&dl=0</p> <p>For additional information, readers are referred to the original papers detailing acquisition protocols and processing procedures:</p> <p>1) G. Rolandino, M. Gagliardi, T. Martins, G. L. Cerone, B. Andrews, J. J. FitzGerald. Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation. IEEE Transactions on Biomedical Engineering, 71(5):1617-1627, May 2024. doi:10.1109/TBME.2023.3346192.</p> <p>2) G. Rolandino, C. Zangrandi, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. HDE-Array: Development and Validation of a New Dry Electrode Array Design to Acquire HD-sEMG for Hand Position Estimation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32:4004-4013, 2024. doi:10.1109/TNSRE.2024.3490796.</p> <p>3) G. Rolandino, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Performance of a ML-Based 3-DoF Kinematic Model in Estimating Hand Position from High-Density EMG. Presented at IFESS Conference, Bath, UK, September 2024.</p> <p>4) G. Rolandino, L. Lion, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Artificial Neural Networks for HD-sEMG-Based Hand Position Estimation: Addressing Inter- and Intra-Subject Variability. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>5) G. Rolandino, G. Parisi, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Real-Time Hand Kinematic Estimation with HD-sEMG and Artificial Neural Networks: Feasibility and Effects of Multi-Subject Training and Visual Feedback. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>6) G. Rolandino, V. Taboni Lisboa, T. Vieira, A. Cliquet Jr., B. Andrews, J. J. FitzGerald. HD-sEMG-Based Control Using Neck Muscles and Shallow Neural Networks: Assessing Performance in Rehabilitation-Oriented Tasks. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>This dataset benefited from the support of all listed authors and arose from collaborations between the Oxford Neural Interfacing Group; LISiN (Politecnico di Torino, Turin, Italy); the Department of Orthopedics, Rheumatology and Traumatology (University of Campinas, SP, Brazil); and the Oxford Robotics Institute (University of Oxford, Oxford, UK). Part of this work was funded by the John Fell Oxford University Press Research Fund.</p> <p>The corresponding author is available for questions or clarification at g.rolandino@protonmail.com.</p>
Lookout Fire Time-Lapse Video Taken from the Roswell Communication Tower for the Period of Record Aug. 10 - Oct. 23, 2023.
<p>A lightning strike fire started on Saturday, August 5, within the H.J. Andrews Experimental Forest, between the base of Lookout cliff and the ridge dividing Lookout and Mack Creek drainages. As of today, August 7, the fire is 2.5 acres. Two helicopters are traveling between the Blue River reservoir and the fire carrying water. The plan is to keep knocking back the fire until ground crews can get a line around it and contain it. It is burning in steep terrain, in old growth with dense understory, which is making it a challenge for experienced ground crews. In addition to the helicopters, a hotshot crew has been assigned to the fire, most likely starting August 8.</p><p>These images were taken from remote based StarDot and NetCam IP cameras positioned 13 meters up the Roswell Communication tower located in the north east side of the HJ Andrews Experimental Forest. The HJAHQCAM video was recorded from a StarDot camera positioned on top of the HJ Andrews main office building. Andrews Forest LTER collected and manged these data in real-time and compiled a final time-lapse video of each camera using a multi-threaded python program.</p><p>Additional Resources:<br><a href="https://andrewsforest.oregonstate.edu/about/news-events/lookout-fire-updates-2023">Andrews Forest LTER</a><br><a href="https://www.youtube.com/@AndrewsForest/playlists">Andrews Forest Youtube</a><br><a href="https://inciweb.wildfire.gov/incident-information/orwif-lookout-fire">InciWeb</a></p><p><strong>This material is based upon work supported by the H.J. Andrews Experimental Forest and Long Term Ecological Research (LTER) program under the NSF grant LTER8 DEB-2025755.</strong></p>
Catalogue of Arctic Toothed Whale Click Spectra and Spectrograms from SoundTrap Recordings
<p>This supplementary file accompanies a research article focused on beluga and narwhal echolocation click classification. This document provides an overview of the data included in all beluga and narwhal acoustic events (each 1 hour) from SoundTrap recordings collected at the Kong Oscar and Fisher Islands mooring sites. There are two figures for each event: (1) concatenated click spectrogram, and (2) mean power spectrum for all detections within the event. Summary statistics are provided for each event showing the number of detections and the one-third octave level (TOL) ratio in decibels (dB) between selected TOL bands. Visual inspection of the enclosed figures demonstrates clear differences between beluga and narwhal click spectra.</p>
Fig.ç2.C olor variations of caudal ns of Upeneus guttatus from Kagoshima, Japan. A, KAUM–I. 7819, 57.8 mm SL; B, KAUM–I. 11885, 80.5 mm SL; C, KAUM–I. 24423, 84.3 mm SL; D, KAUM–I. 13067, 119.2 mm SL. in First Records of the Two-tone Goatfish, Upeneus guttatus, from Japan, and Comparisons with U. japonicus (Perciformes: Mullidae)
Fig.ç2.C olor variations of caudal ns of Upeneus guttatus from Kagoshima, Japan. A, KAUM–I. 7819, 57.8 mm SL; B, KAUM–I. 11885, 80.5 mm SL; C, KAUM–I. 24423, 84.3 mm SL; D, KAUM–I. 13067, 119.2 mm SL.
Fig.ç1.C olor photographs of fresh specimens of (A–B) Upeneus guttatus and (C–D) U. japonicus. A–B: KAUM–I. 11011, 114.1 mm SL, Kagoshima, Japan; C–D: KAUM–I. 9212, 90.9 mm SL, Kagoshima, Japan. in First Records of the Two-tone Goatfish, Upeneus guttatus, from Japan, and Comparisons with U. japonicus (Perciformes: Mullidae)
Fig.ç1.C olor photographs of fresh specimens of (A–B) Upeneus guttatus and (C–D) U. japonicus. A–B: KAUM–I. 11011, 114.1 mm SL, Kagoshima, Japan; C–D: KAUM–I. 9212, 90.9 mm SL, Kagoshima, Japan.
Fig.ç3.R elationships of (A) barbel length and (B) pectoral- n length to standard length in Upeneus guttatus (closed symbols: stars from Japan, squares from Indo–West Paci c) and U. japonicus (open circles). in First Records of the Two-tone Goatfish, Upeneus guttatus, from Japan, and Comparisons with U. japonicus (Perciformes: Mullidae)
Fig.ç3.R elationships of (A) barbel length and (B) pectoral- n length to standard length in Upeneus guttatus (closed symbols: stars from Japan, squares from Indo–West Paci c) and U. japonicus (open circles).
Figs. 1-10. Amphientulus sinensis, new species. 1 in First Record Of The Genus Amphientulus Tuxen, 1981 (Protura: Berberentulidae) From China, With Description Of A New Species
Figs. 1-10. Amphientulus sinensis, new species. 1, labial palpus; 2, pseudoculus; 3, mesonotum; 4, metanotum; 5, anterior part of head, dorsal view; 6, urotergite VI; 7, filamento di sostegno; 8, head chaetotaxy; 9, urosternite VI; 10, abdominal legs II, III. Scale bar: Figs. 1-2 = 5 µm; Figs. 3-10 = 10µm.
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.