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.
594
datasets available to search
ShareScore release 0.7.1
Dataset results
594 results for “REACH”
Deep Learning Reach-level Estimates of Mean River Depth at the Conterminous United States Spatial Scale
<p>Abstract: Estimates of riverine channel geometry play a vital role in the physical representation of stream networks in models used to predict flood and drought conditions, manage water resources, and increase our knowledge of fluvial conditions under a changing climate. A well established body of literature exists that explains the relationship between channel geometry parameters width, depth, and velocity to instantaneous river discharge using a log-log linear power-law regression. In this study, a state-of-the-art deep learning regression model is presented and compared against the power-law method to evaluate their ability to estimate cross-sectional mean river depth. Results reveal three key findings, the neural network: (1) decreases RMSE by 22% verse a CONUS scale power-law equation, (2) reduces prediction variance across Strahler stream orders, and (3) generally outperforms regional power-law equations with an average decrease in RMSE of 8.7% Lastly, a reach-level CONUS dataset of estimated mean river depth is delivered.</p> <p> </p> <p>The deep learning model was trained using the following features:</p> <ul> <li>AI - Mean aridity index of unit catchment - Trabucco and Zomer, 2019</li> <li>area - Upstream drainage area (km2) - P. Lin et al., 2020</li> <li>CLY - Mean clay content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>DOR - Stream segment degree of dam regulation (Scale 0. – 100.) - Grill et al., 2019</li> <li>Elev - Stream segment mean elevation - P. Lin et al., 2020</li> <li>K - Mean bedrock permeability of unit catchment surrounding stream segment - Huscroft et al., 2018</li> <li>LAI - Mean leaf area index of unit catchment - Zhu et al., 2013</li> <li>order - Strahler-Horton stream order - P. Lin et al., 2020</li> <li>P - Mean bedrock porosity of unit catchment - Huscroft et al., 2018</li> <li>QMEAN - Stream segment mean annual discharge (m3/s) - P. Lin et al., 2019</li> <li>Sin - Stream segment sinuosity - P. Lin et al., 2020</li> <li>Slp - Stream segment mean longitudinal slope - P. Lin et al., 2020</li> <li>SLT - Mean silt content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>SND - Mean sand content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>stream_wdth_va - Measured stream cross-sectional width (m) - Canova et al., 2016</li> <li>Urb - Mean urban fraction of unit catchment - Liu et al., 2018</li> </ul> <p>The deep learning model was trained using the following label:</p> <ul> <li>mean_depth_va - Measured stream mean depth (m) - Canova et al., 2016</li> </ul> <p>Predictions of mean depth were made by replacing stream_wdth_va from Canova et al., (2016) with bankfull width estimates (width_m) from P. Lin et al., (2020). Missing records from the P. Lin et al., (2020) dataset were excluded when making predictions, thus there are missing reaches in the dataset.</p>
Meta-analysis on necessary investment shifts to reach net zero pathways in Europe
<p>This is the code and the data necessary to reproduce the six main figures and the t-test presented in the supplementary information of the publication "Meta-analysis on necessary investment shifts to reach net zero pathways in Europe". DOI: 10.1038/s41558-022-01549-5</p>
A cropland cover dataset for the middle and lower reaches of the Yellow River over the past millennium
<p>We developed a new gridding allocation model for croplands with unequal weight factors and reconstructed 58 time-point cropland cover maps at a 10-km resolution for the past millennium in the middle and lower reaches of the Yellow River in China. The cropland dataset can be used to model past climate change, estimate carbon emissions, assess human-activity-induced ecological effects, and improve global historical land use scenarios.</p>
Dataset from University of Idaho 2004, master's thesis [Littoral ecology of epilithic algae in the Rocky Reach Pool, Mid-Columbia River (Washington State) - The effects of reservoir fluctuations.]
(Abstract from thesis) Epilithic algae, water column physical/chemical properties, and sediments were examined in the impounded Mid-Columbia River including the Rocky Reach Reservoir. Primary objectives included determination of the effects reservoir drawdown has on epilithic algae and potential nutrient enrichment via sediment. Epilithic algae were analyzed by pigment concentration, gravimetrically, and species composition. Reservoir elevation fluctuated at higher rates at tailrace sites (0.41-0.25 m/hr) compared to the forebay site (0.06-0.08 m/hr). Littoral exposure times were also greater at tailrace sites (mean of 8 hrs compared to 0 hr at the forebay site). Mean epilithic algae monochromatic chlorophyll a over all sampling periods at mainstem sites was 76.7 ± 4.8 mg/m2 (95 % C.I.). Epilithic algae monochromatic chlorophyll a in the zone of water fluctuation (0-1 m) was less at Wells tailrace (38.8 mg/m2) compared to Rocky Reach forebay (141.3 mg/m2) during summer, 2000 and 2001. Mean epilithic biofilm ash-free oven-dry weight over all sampling periods at mainstem sites was 25.6 ± 1.5 g/m2 (95 % C.I.). Mean autotrophic index across all mainstem locations was 439 indicating a large heterotrophic component within the epilithic biofilms. Epilithic algae communities were dominated by diatoms (50.2 %) and cyanobacteria (35.9 %), with some green algae (13.8 %). Canonical correlation analysis indicated that temperature, depth, site, and the water elevation change rate were important controllers of epilithic algae chlorophyll pigments. Mean textural characteristics of dredged sediment were 51.1 % sand, 43.2 % silt, and 5.7 % clay. Mean organic matter content in this sediment was 4.1 %. The mean seston sedimentation rate across mainstem locations was 11.3 g m-2 d-1 and organic matter comprised 14.7 % of the material collected from the water column.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from February 2016 through December 2016
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from February through December 2016. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2017 through December 2017
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from January 2017 through December 2017. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2018 through December 2018
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from January 2018 through December 2018. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
Total numbers and species of insects taken from rock scrubbings during the summer of 1984-1988, 1993-1994, 1996-1998, in the Kuparuk River experimental reach near Toolik Field Station, North Slope Alaska..
A rock-scrubbing technique was used to collect bottom samples at several different stations with three replicates at each station in the Kuparuk River. The stations are measured relative to the 1984 phosphorus dripper. Only July sampling dates are included in this file (ACG). The samples were preserved in ethanol then picked, sorted, counted, and measured in Duluth using a NIKON MICRO-PLAN II digitizing pad.
Data from Potential source areas for atmospheric lead reaching Ny-Ålesund from 2010 to 2018
<p>date reports the sampling data in YYYY-MM-DD format and volume the sampling volume in m3.<br> pb_sign is = for Pb concentrarion data above limit of quantification (LoQ) and < for data below LoQ.<br> pb_val is numeric and it is the measured Pb concentration or LoQ in pg/m3.<br> pb is text and it is the measured Pb concentration or <LoQ in pg/m3.<br> al_ef is the enrichment factor (EF) EF(Pb/Al)c in comparison to the upper continental crust (UCC, Wedepohl 1995).<br> pb20x20y is the value measured for 20xPb / 20yPb isotope ratio.<br> u20x20y is the 95-confidence level uncertainty for the measured 20xPb / 20yPb isotope ratio value.<br> Missing values are reported as NA.<br> Wedepohl 1995: Wedepohl, K.H., 1995. The composition of the continental crust. Geochim. Cosmochim. Acta 58A, 959–960. https://doi.org/10.1180/minmag.1994.58A.2.234</p>
Data + Analyses: "Gaze-dependent Coding of Somatosensory Reach Targets after Effector Movement: Testing the Impact of Online Information, Movement Timing, and Target Distance"
<p>This upload contains the experiment scripts (written in Presentation), data, and analyses (performed with MATLAB and SPSS) underlying the publication<strong> </strong>by Mueller & Fiehler (2017). <em>PloS one</em>. doi:<strong>10.1371/journal.pone.0180782</strong></p>
REACH_OpenCall_ Applicants_Open_Database
<p>This anonymised database covers Open Calls 1, 2 and 3 of REACH Incubator. </p>
Teach to Reach 8: Expériences partagées
<p>Cette publication présente les expériences <strong>en français</strong> partagées par les participants de Teach to Reach 8 en amont de l'événement en direct du 16 juin 2023. <a href="https://www.learning.foundation/teachtoreach-fr">En savoir plus sur Teach to Reach</a>… <a href="http://hopin.com/events/teachtoreach8">Voir le programme de Teach to Reach 8</a>…</p> <p><strong>Échantillon</strong> : 16 835 personnes inscrites à Teach to Reach 8 ont été invitées à partager leur expérience de la vaccination par le biais d’un questionnaire en ligne en juin 2023.</p> <p><strong>Contenu</strong> : Au 12 juin 2023, les réponses suivantes ont été reçues par domaine thématique. Pour chaque thème, les expériences sont présentées dans l’ordre dans lequel elles ont été reçues.</p> <table> <tbody> <tr> <td> <p>Thème</p> </td> <td> <p>Contributions (anglais)</p> </td> <td> <p>Contributions (français)</p> </td> <td> <p>Total des contributions</p> </td> </tr> <tr> <td> <p>Pourquoi travaillez-vous pour la santé ?</p> </td> <td> <p>228</p> </td> <td> <p>251</p> </td> <td> <p>479</p> </td> </tr> <tr> <td> <p>La vaccination dans un contexte humanitaire</p> </td> <td> <p>142</p> </td> <td> <p>129</p> </td> <td> <p>271</p> </td> </tr> <tr> <td> <p>Expériences de la vaccination contre le papillomavirus (HPV)</p> </td> <td> <p>103</p> </td> <td> <p>107</p> </td> <td> <p>210</p> </td> </tr> <tr> <td> <p>Utilisation du vaccin oral contre le choléra (OCV) lors des flambées de choléra </p> </td> <td> <p>30</p> </td> <td> <p>78</p> </td> <td> <p>108</p> </td> </tr> <tr> <td> <p>L’avenir du travail dans le domaine de la santé</p> </td> <td> <p>58</p> </td> <td> <p>26</p> </td> <td> <p>84</p> </td> </tr> <tr> <td> <p>Comment votre travail a-t-il été affecté par la pandémie de COVID-19 ?</p> </td> <td> <p>24</p> </td> <td> <p>18</p> </td> <td> <p>42</p> </td> </tr> <tr> <td> <p>Comment le partage d’expérience est-il utile à votre travail quotidien ?</p> </td> <td> <p>21</p> </td> <td> <p>19</p> </td> <td> <p>40</p> </td> </tr> <tr> <td> <p>Qu’est-ce qui vous aidera à établir et à maintenir la confiance avec les populations que vous servez ? </p> </td> <td> <p>23</p> </td> <td> <p>13</p> </td> <td> <p>36</p> </td> </tr> <tr> <td> <p>Comment les technologies digitales se sont-elles intégrées dans votre travail quotidien ?</p> </td> <td> <p>17</p> </td> <td> <p>18</p> </td> <td> <p>35</p> </td> </tr> </tbody> </table> <p><strong>Langues originales</strong> : Anglais et français. Cette publication ne présente que les expériences partagées en français.</p> <p><strong>Formats disponibles</strong> : Récits qualitatifs avec données démographiques structurées et informations de consentement (Excel).</p> <p><strong>Limites connues</strong> : Les études de cas et les récits des contributeurs sont autodéclarés et ne sont pas vérifiés par la Fondation.</p> <p><strong>Autres considérations sur les données présentées</strong> : Les récits présentés sont ceux de professionnels de la vaccination qui ont fait le choix de partager leur expérience personnelle. Les données sont autodéclarées et ne sont pas vérifiées par la Fondation. Les expériences sont relus pour leur cohérence interne et légèrement éditées. L’inclusion d’expériences et de commentaires dans ce recueil n’implique aucune recommandation de la part de la Fondation ou de ses partenaires. La Fondation n’approuve aucune stratégie, approche ou réflexion particulière partagée par les contributeurs, et déconseille explicitement de tirer des conclusions de cas spécifiques qui pourraient ne pas être généralisables. Les lecteurs sont seuls responsables de l’évaluation des implications éthiques, juridiques et pratiques de l’utilisation de ce expériences partagées, et en particulier de la nécessité d’adapter la pratique d’un contexte à l’autre. Les opinions et les déclarations exprimées dans cette publication sont celles des auteurs et ne reflètent pas nécessairement la position officielle de leurs ministères de la santé respectifs ou d’autres employeurs. Bien que les auteurs fassent part de leur affiliation, ils participent à titre personnel et leurs contributions ne doivent pas être considérées comme représentant les opinions ou l’approbation des organisations auxquelles ils sont affiliés. Toutes les contributions ont reçu l’autorisation de l’auteur pour être utilisées par la Fondation à des fins de communication, de plaidoyer, de renforcement des capacités et de recherche.</p> <p><strong>Teach to Reach 8, c’est quoi ?</strong></p> <ul> <li>16 835 acteurs de terrain de la vaccination et des soins de santé primaires (SSP) utilisent l’apprentissage par les pairs pour relever leurs défis locaux en participant à Teach to Reach, un réseau international de partage d’expérience</li> <li>Un grand événement en direct s’est tenu le vendredi 16 juin 2023 et s’est concentré sur le réseautage individuel entre les participants.</li> <li>La Cérémonie d’ouverture a été assuré par le collectif Femmes de la vaccination, qui a partagé son expérience de l’introduction du vaccin contre le papillomavirus pour aider à prévenir 340 000 décès dus au cancer du col de l’utérus.</li> </ul> <p>Écoutez les voix des Femmes de la vaccination expliquer pourquoi le vaccin contre le papillomavirus est important: <a href="https://www.youtube.com/watch?v=fxqqejT1vf4">https://www.youtube.com/watch?v=fxqqejT1vf4</a></p> <p>Nous avons vécu un grand moment d’émotion et de partage vendredi. Cependant, ce moment d’apprentissage en direct n’est qu’une étape de Teach to Reach.</p> <ul> <li>Avant l’événement, les participants avaient déjà partagé leurs expériences sur une série de questions sélectionnées par la communauté.</li> <li>Tous les participants à Teach to Reach 8 reçoivent ce recueil complet des expériences partagées en français avant l’événement.</li> <li>Les participants utilisent maintenant les deux semaines à venir pour partager leur apprentissage et leurs idées.</li> </ul> <p>Ces idées seront également restituées à la communauté dans le prochain rapport «Écouter pour apprendre» de Teach to Reach 8.</p> <ul> <li>À tout moment, les participants peuvent partager d’autres réflexions, idées et pratiques par l’intermédiaire de La Double Boucle, notre bulletin d’information sur les perspectives. Pour en savoir plus sur La Double Boucle <a href="https://www.learning.foundation/loop">https://www.learning.foundation/loop</a></li> <li>Suivez ce lien pour consulter le rapport Teach to Reach 7 insights <a href="https://doi.org/10.5281/zenodo.7851783">https://doi.org/10.5281/zenodo.7851783</a></li> </ul> <p>Les participants se joindront également à «Idées en direct», des échanges-éclairs sur ce que nous apprenons ensemble—et sur la manière dont nous l’utilisons pour faire la différence.</p> <ul> <li>Teach to Reach fait partie du parcours de l’apprentissage à l’action de la Fondation Apprendre Genève en soutien au Mouvement pour la vaccination à l’horizon 2030 (IA2030).</li> <li>Contrairement aux conférences présentielles coûteuses, Teach to Reach est ouvert à tous, et il n’y a pas de limite supérieure au nombre de participants.</li> </ul> <p>Pour en savoir plus sur la valeur supérieure, l’efficacité et l’impact des réseaux digitaux pour impulser le changement dans la santé globale, <a href="https://redasadki-me.translate.goog/2023/06/12/digital-bridges-cannot-cross-analog-gates/?_x_tr_sl=en&_x_tr_tl=fr&_x_tr_hl=en&_x_tr_pto=wapp">lisez cet article de blog</a></p>
REACH_ DataProviderSamples_DB_v3
<p><span></span><span><span></span><span><span>Database with information on </span><span>data samples published by Data Providers on </span><span>the </span><span>three open calls of </span><span>REACH incubator </span><span>project</span><span>. </span></span><span></span></span><span> </span></p>
Supplementary material for the article "Reaching Meaning through Language: What can Children Tell Us about Distributivity?"
<div> <div>This data set includes the supplementary material for the article "Reaching Meaning through Language: What can Children Tell Us about Distributivity?". All content is documented in the README.md file.</div> <br> <div><strong>Abstract: </strong>Sentences with a plural subject receive a distributive reading if the predicate refers to the atomic members or a collective one if it relates to the whole group. Previous accounts suggest that the distributive representation includes an additional semantic operator, and comprehension experiments show that adults interpret an ambiguous sentence as collective. However, children accept distributive readings more often, questioning their presumed greater difficulty. The current study investigates these interpretations in a novel way through a production study. Italian adults and preschoolers described distributive and collective pictures. We found that adults produced more distributive expressions, in line with semantic theories and psycholinguistic findings. Children were not fully sensitive to the need to express markers disambiguating the two readings. However, when they recognised the difference between pictures, they produced more collective markers, different from adults. We discussed our results at the intersection of language acquisition, semantic theories, and cognitive development.</div> </div>
AZtec projects reach the data size limit
<p>Ten Ti-6Al-4V samples were mounted on a multi-sample stage for EBSD on a Thermo Fisher Apreo SEM equipped with an Oxford Instruments' Symmetry 2 detector at the University of Manchester.</p> <p>In project <em>multi-sample_1</em>, AZtec reported a saving error when scanning the fifth sample and stopped with 5646 frames saved (.oip~4GB). It is able to montage and export the maps, but any edit on the .oip file cannot be saved.</p> <p>In project <em>multi-sample_2,</em> we restarted the scan on the rest of the samples and completed with 5601 frames. The .oip is 3.97GB, which almost reaches the size limit. No error was reported during the scanning, and the .oip file is still editable. </p>
Opinion dynamics in social network under competition: the role of influencing factors in consensus reaching
<p>The profitability of opinion and the finiteness of individual attention have already spawned the extensive competition for individual preferences on social networks. It's quite necessary to investigate the opinion dynamics over social networks in a competitive environment. To this point, this paper develops a novel social network DeGroot model based on competition game (DGCG) to characterize the opinion evolution in a competitive opinion dynamics. Based on the DGCG model, we obtain equilibrium results in the stable state of opinion evolution. Consecutively, we analyze what role relevant factors play in the final consensus and competitive outcomes, including the resource ratio of both contestants, initial opinions and network structure. Theoretical analyses and simulation experiments show that these factors can significantly sway the consensus and even reverse competition outcomes.</p>
The percentage of above-ground biomass carbon carrying capacity reached
<p>This dataset is the percentage of above-ground biomass carbon carrying capacity reached in the eight provinces of southern China from 2002 to 2017 at the resolution of 500m x 500m, with the urban and water areas, cropland, and the southeast margin of the Tibet Plateau masked. The dataset takes values ranging from 0%-100%. 0% represents the highest carbon sequestration potential, while 100% represents carbon sequestration has reached saturation. The dataset can locate areas where vegetation has not yet reached its full potential, which is significant for the implementation and adjustment of ecological engineering. The dataset is publicly available.</p>
Dataset of reaching measures of auditory peripersonal space
<p>Data from a auditory reaching experiment in participants with active, guided and no training.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 2. Two Possible Progress Scenarios for How to Reach Towards Machines and Systems with Human- Level Cognitive Skills
<p>Having identified the need for novel methods for machine recognition, situation assessment, and decision making in order to advance further in different automation domains, an important question is by what means can we reach such sophisticated mechanisms. The long-term goal in<br> mind is to construct machines and systems showing performances comparable to or even beyond human skill levels. In a guest talk at the Vienna University of Technology in 2008, Prof. Etienne Barnard, an expert in the field of Artificial Intelligence, made an interesting “conceptual suggestion” for two possible progress scenarios to reach this goal which could be summarized as depicted in Figure 2.</p>
Data from: Does losing reduce the tendency to engage with rivals to reach mates? An experimental test
<p>Male-male contests for access to females or breeding resources is critical in determining male reproductive success. Larger males and those with more effective weaponry are more likely to win fights. However, even after controlling for such predictors of fighting ability, studies have reported a winner-loser effect: previous winners are more likely to win subsequent contests, while losers often suffer repeated defeats. While the effect of winning-losing is well-documented for the outcome of future fights, its effect on other behaviors (e.g., mating) remains poorly investigated. Here, we test whether a winning versus losing experience influenced subsequent behaviors of male mosquitofish (<em>Gambusia holbrooki</em>) towards rivals and potential mates. We housed focal males with either a smaller or larger opponent for 24 hours to manipulate their fighting experience to become winners or losers, respectively. The focal males then underwent tests that required them to enter and swim through a narrow corridor to reach females, bypassing a cylinder that contained either a larger rival male (competitive scenario), a juvenile or was empty (non-competitive scenarios). The tests were repeated after one week. Winners were more likely to leave the start area and to reach the females, but only when a larger rival was presented, indicating higher levels of risk-taking behavior in aggressive interactions. This winner-loser effect persisted for at least one week. We suggest that male mosquitofish adjust their assessment of their own and/or their rival's fighting ability following contests in ways whose detection by researchers depends on the social context.</p>
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.