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.
91,407
datasets available to search
ShareScore release 0.9.0
Dataset results
91,407 results for “Effects With / Effects Of”
Data underlying the article "Effect of Submergence on the Lateral Exchange between Groyne Fields and their adjacent Main Channel"
<p>The paper addresses the identification of the mechanisms that dominate the flow around a series of obstacles placed at the sidewall of a channel, representing fluvial groynes. Accordingly, 2D velocity fields were measured through Particle Image Velocimetry in a horizontal plane spanning the area between groynes and an adjacent portion of the main channel. Four experimental cases were performed, addressing two groyne separations and two submergence conditions (emerged and submerged). For the submerged case, the ratio water depth to groyne height was 1.3. Groyne separations were characterized according to the corresponding width-to-length ratio of the groyne field (lambda = W/L = 1 and 2). A complete description of the experimental conditions, objectives and outcomes can be found in the article.</p> <p>The following data is included:</p> <ol> <li>Meanfields_[case].csv: spanwise and streamwise components of the velocity field, velocity magnitude and uv component of the Reynolds stress tensor averaged over time.</li> <li>Reynoldsstressesprofiles_[case].csv: uv component of the Reynolds stress tensor averaged over time at selected transverse profiles.</li> <li>PSD_[case].csv: power spectral densities computed from fluctuating velocity series extracted at selected locations.</li> <li>Autoccorrelation_[case].csv: normalized transverse autocorrelation functions computed from fluctuating velocity series extracted at selected locations.</li> <li>PODenergycontribution.csv: energy contribution from the first 20 POD modes computed for the case studies.</li> <li>PODtemporalcoefficients.csv: temporal coefficients obtained from the first two modes for the case studies.</li> <li>PODspectra_[case].csv: spectra of the temporal coefficients corresponding to modes 1 to 4 for the cases in study.</li> <li>PODspatialmodes_[case].csv: first two spatial modes computed for the case studies.</li> </ol>
Effects of secondary ice processes on a stratocumulus to cumulus transition during a cold-air outbreak
<p>Dataset of model simulations discussed in paper "Effects of secondary ice processes on a stratocumulus to cumulus transition during a cold-air outbreak", <a href="https://doi.org/10.1016/j.atmosres.2022.106302">https://doi.org/10.1016/j.atmosres.2022.106302</a></p>
Effects of audio-motor training on spatial representations in long-term late blindness
<p>Datasets for behavioural data:</p> <p>-<em>Auditory horizontal localization task</em></p> <p>- <em>Auditory vertical localization task</em></p> <p>- <em>Position matching task</em></p> <p>- <em>Proprioceptive midline task</em></p> <p>Dataset for EEG data:</p> <p>- Spatial bisection task: mean ERP amplitude for 50-90ms timew window for each trial, separately for condition, session and roi</p> <p> </p>
Diallel analysis reveals Mx1-dependent and Mx1-independent effects on response to influenza A virus in mice
<p>Data and analysis files for diallel analysis of weight loss in 8-12 week old male and female mice (n=1,043), mock treated or infected with influenza A virus (H1N1, PR8) across 4 days post-infection, as well as founder haplotype effect analysis at Mx1 for pre-CC and CC-RIX.</p>
Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome
<p>Demultiplexed fastq files used for sequencing analysis, processed taxonomy read data for all samples and controls, and relevant environmental metadata as described in "Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome".</p>
Soil C models for evaluating the effect of cover crops
<p>Excel implementation and dataset for three C cycling models: </p> <ul> <li>Yasso20: monthly simulation version</li> <li>SOMIC 1.0: excel implementation with both simple time step and Euler-Heng iteration</li> <li>Single pool simulation model</li> <li>In addition input files for DNDC.Can.9.5.8 for one of the farms. </li> </ul> <p>There are three versions of the SOMIC model: </p> <ol> <li>The Zip file contains the operational version of the files for the cover crop experiment example and an </li> <li>_Euler-Heng version is an alternative version of the numerical simulation, which can be unstable (feel free to improve, do not use for simulation as such)</li> <li>The _MC version is a Monte Carlo uncertainty analysis implementation for one farm. (Using Simulacion 4.0 <a href="https://ucema.edu.ar/~jvarela/index_eng.htm">https://ucema.edu.ar/~jvarela/index_eng.htm</a>) </li> </ol> <p>The models are applied to an cover crop experiment, where four farms tested cover crops for 5 years. The corresponding article is submitted to Soil Use and Management. The C input estimation zip is used to translate recorded yield and cover crop NDVI data to time series of C inputs used for the models. </p> <ul> <li>The Yasso20 model implementation is based on Yasso20 model code: <a href="https://github.com/YASSOmodel/Yasso20/tree/main">https://github.com/YASSOmodel/Yasso20/tree/main</a></li> <li>The SOMIC model implementation is based on: <a href="https://github.com/domwoolf/somic1">https://github.com/domwoolf/somic1</a></li> <li>The single pool model is as described in: <a href="https://doi.org/10.1016/j.still.2021.105204">https://doi.org/10.1016/j.still.2021.105204</a></li> <li>The DNDC.Can model version can be downloaded from: <a href="https://github.com/BrianBGrant/DNDCv.CAN">https://github.com/BrianBGrant/DNDCv.CAN</a></li> </ul> <p>All the models are capable of simulating time series of soil C development over time, as influenced by C inputs, starting SOC and soil temperature and moisture. They are presented here for the purpose of further model development and comparison, not for making accurate forecasts. </p> <p> </p> <p></p> <p></p> <p></p>
Supplementary run files for the paper "Learning Effective Representations for Retrieval using Self-Distillation with Adaptive Relevance Margins"
<p>TREC-Format run files of all trained models as supplementary material for the paper "Learning Effective Representations for Retrieval using Self-Distillation with Adaptive Relevance Margins".</p> <p>File naming follows the schema: <code>{model}-{loss variant}-{in-batch usage}-{dataset}.txt.gz</code></p>
Effective realization of abatement measures can reduce HFC-23 emissions
<p>Atmospheric observations (mole fractions) of halogenated greenhouse gases (HFC-23 (CHF<sub>3</sub>), PFC-318 (c-C<sub>4</sub>F<sub>8</sub>), HCFC-22 (CHClF<sub>2</sub>), HCFC-21 (CHCl<sub>2</sub>F), HFC-4310mee (C<sub>5</sub>H<sub>2</sub>F<sub>10</sub>), HFC-161 (C<sub>2</sub>H<sub>5</sub>F)) at the tall tower site at Cabauw, the Netherlands (51.972 °N, 4.927 °E, altitude -0.7 m a.s.l., 207 m a.g.l.), for the duration of a tracer (HFC-161) release experiment (17.06.2022 – 07.08.2022) within an extended (19.11.2021 – 7.8.2022) measurement campaign of halogenated greenhouse gases (>60 substances) at the Cabauw tall tower site. The measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography and mass spectrometry (GC-MS), as is used within the global AGAGE network (<a href="https://agage.mit.edu/">https://agage.mit.edu/</a>). The HFC-161 tracer was released at 22 km distance from the Cabauw tall tower site, at 4 m a.s.l., 10 m a.g.l, at various flow rates. HFC-161 mole fractions are provided as the measured mole fractions and as the measured mole fractions normalised to the set tracer release flow rates.</p> <p>In addition, a subset of the above-described data is provided. This was used to assess the emissions of the above listed halogenated greenhouse gases from an industrial factory, by reference to the released HFC-161 tracer.</p> <p>The data are related to an article in Nature (https://doi.org/10.1038/s41586-024-07833-y).</p>
Data from: Long-term effects of meadow management on seed bank diversity and composition
<p>Aims: Oligotrophic grasslands are habitats that host among the most diverse plant communities in Europe. Altering management regimes by either intensifying or ceasing management is known to decrease plant diversity. Yet, despite its importance for the recovery of plant communities after disturbances, little is known about whether seed banks are also affected by changes in management. Here, we investigate the effect of management practices on a meadow seed bank using a long-term manipulative experiment. We focus on the response of the seed bank to the treatments, and the relationship between the seed bank and the vegetation response.</p> <p>Methods: The study was conducted in a species-rich wet meadow. The experiment consists of a factorial combination of fertilization, mowing, and removal of the dominant species. After 20 years of management, the seed bank was sampled seasonally at two soil layer depths. Standing vegetation was recorded in June at the peak of vegetation.</p> <p>Results: All seed bank characteristics varied between soil layers. Mowing decreased seed density and diversity, while fertilization significantly affected the species composition. Dominant removal had no effect on the seed bank. While seed bank diversity was not correlated to vegetation diversity, individual species’ responses to mowing and fertilization were positively correlated in the seed bank and the vegetation.</p> <p>Conclusions: Our results show that long-term management influences the seed bank down to 10 cm of soil depth. Whereas mowing apparently reduced seed density and diversity, the effects of fertilization on these characteristics were harder to interpret. After 20 years, most species had concordant responses to both mowing and fertilization, indicating a low legacy of previous management regimes on the seed bank. Our study reveals that the intensification of grassland management has a profound effect on plant diversity by directly affecting plant communities and their seed bank-driven recovery potential.</p>
Effects of sleep restriction and light intensity on mental effort during cognitive challenge Study 2
<p>This repository contains the data of Study 2 of the manuscript "Effects of sleep restriction and light intenisty on mental effort during cognitive challenge" by Larissa Wüst and Ruta Lasauskaite.</p>
Data supporting: Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem
<p>Data used to obtain the results of the research paper entitled: "Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem", published in the journal "Water Research". The data derives from an outdoor (meso-) cosm experiment in Spain (Imdea Water, Alcala de Henares) in which the transportable temperature and heatwave control device (TENTACLE) was used to investigate the multiple stressors effects of two different climate change scenarios related to temperature (i.e., elevated temperature and reoccurring heatwaves) in combination with the neonicotinoid insecticide imidacloprid.</p>
Dataset for article: Pro-cognitive effects of dual tacrine derivatives acting as cholinesterase inhibitors and NMDA receptor antagonists
<p>Figure 1. Chemical structures of tacrine (<strong>a</strong>) and its derivatives created by introducing substituents on the aromatic core and/or altering the size of the cycloalkyl moiety attached to the aromatic region: 7-MEOTA (<strong>b</strong>), K1578 (7-chloro-1<em>H</em>,2<em>H</em>,3<em>H</em>-cyclopenta[<em>b</em>]quinolin-9-amine; <strong>c</strong>), K1592 (1-chloro-6<em>H</em>,7<em>H</em>,8<em>H</em>,9<em>H</em>,10<em>H</em>-cyclohepta[<em>b</em>]quinolin-11-amine; <strong>d</strong>), K1594 (6-methyl-1,2,3,4-tetrahydroacridin-9-amine; <strong>e</strong>), and K1599 (7-methoxy-1<em>H</em>,2<em>H</em>,3<em>H</em>-cyclopenta[<em>b</em>]quinolin-9-amine; <strong>f</strong>). Compounds in this study were used in the form of hydrochloride salts.</p> <p>Figure_2_values. Test results. Morris water maze: scopolamine-induced model of cognitive deficit in the acquisition and reversal phases. The graphs show the effects of K1578 (<strong>a</strong>), K1592 (<strong>b</strong>), K1594 (<strong>c</strong>), and K1599 (<strong>d</strong>) on escape latency during the acquisition phase, where none of the compounds ameliorated the deficit of spatial learning. The remaining graphs display the effects of K1578 (<strong>e</strong>), K1592 (<strong>f</strong>), K1594 (<strong>g</strong>), and K1599 (<strong>h</strong>) in the reversal phase, where K1578 (1 mg/kg) and K1599 (at both doses), and marginally K1592 (1 mg/kg; see in the text), mitigated the scopolamine-induced deficit of reversal learning. VEH – vehicle, the numbers in brackets denote the dose applied (mg/kg). Data are presented as the mean + SEM, * vs. VEH, * p < 0.05, ** p < 0.01, *** p < 0.001. <em>n</em> = 6–9 animals per group. Statistical significance was determined using two-way repeated measures ANOVA (a–d) or ANOVA (e, f, h) followed by Dunnett’s multiple comparisons tests.</p> <p>Figure-3_values. Test results. Morris water maze: MK-801-induced model of cognitive deficit in the acquisition phase. The graphs illustrate the effects of the compounds K1578 (<strong>a</strong>), K1592 (<strong>b</strong>), K1594 (<strong>c</strong>), and K1599 (<strong>d</strong>) on escape latency. Only K1599 (1 mg/kg) ameliorated the MK-801-induced deficit of spatial learning. VEH – vehicle, the numbers in brackets denote the dose (mg/kg). Data are presented as the mean + SEM, * vs. VEH, * p < 0.05, ** p < 0.01. <em>n</em> = 5–7 animals per group. Statistical significance was determined using two-way repeated measures ANOVA followed by Dunnett’s multiple comparisons tests.</p> <p>Figure_4_values. Open field test. The results demonstrate the effects of K1578 (<strong>a</strong>), K1592 (<strong>b</strong>), K1594 (<strong>c</strong>), and K1599 (<strong>d</strong>) on the distance moved by intact and MK-801-treated animals. VEH – vehicle, the numbers in brackets denote the dose (mg/kg). Data are presented as the mean + SEM, * vs. VEH group of the corresponding phenotype, * p < 0.05, ** p < 0.01, **** p < 0.0001. <em>n</em> = 6–14 animals per group. A significant effect of both factors (treatment and phenotype) was determined using two-way ANOVA, followed by Dunnett’s multiple comparisons tests.</p> <p>Figure-5_values. Acetylcholinesterase activity. The results document the effect of the compounds (1 mg/kg ip) on AChE activity in the hippocampus (<strong>a</strong>), prefrontal cortex (<strong>b</strong>), striatum (<strong>c</strong>), and whole brain sample (<strong>d</strong>). K1578 and K1599 decreased AChE activity in the striatum. VEH – vehicle. Data are presented as the median with minimum to maximum range, * vs. VEH, *** p < 0.001, **** p < 0.0001. VEH samples AChE enzyme activities reached the following absolute values (a) 15.19 ± 3.09 U/mg protein, (b) 9.810 ± 1.54 U/mg protein, (c) 26.05 ± 3.27 U/mg protein, and (d) 27.38 ± 3.36 U/mg protein. Significance was determined by ANOVA (graphs c, d), followed by Dunnett’s multiple comparisons tests.</p> <p>Figure_6_values. Electrophysiology: Inhibition of GluN1/GluN2A receptors by K1599. Representative whole-cell patch-clamp recordings measured from HEK293 cells expressing the GluN1/GluN2A receptors held at a membrane voltage of −80 mV and +60 mV; 30 μM K1599 was applied as indicated. Results summarizing the relative inhibition induced by 30 µM K1599, measured at the indicated membrane potentials. <em>n</em> ≥ 5 cells per each condition.</p> <p>Table_1. The rats were pseudo-randomly assigned to one of the 18 treatment groups listed in. Each group received two injections: one containing the study compound and another containing either MK-801 or scopolamine, as indicated by the group name. The vehicle group (VEH) received the DMSO vehicle (2.5 mL/kg) and saline. The “scopolamine” and “MK-801” groups received scopolamine or MK-801, respectively, along with the DMSO vehicle (2.5 mL/kg).</p> <p>Table 2. Treatment groups and <em>n</em> in biochemical experiments - AChE activity assay.</p>
Data from: Multifaceted density dependence: Social structure and seasonality effects on Serengeti lion demography
<p>This dataset contains the data and R scripts to estimate the survival, transition, and detection probabilities (Lions_Survival_Transition_MultistateCMRModel.zip) as well as the probability of reproduction and recruitment to 1 year old (Lions_Reproduction_Recruitment_GLMM.zip) in a population of African lions (<em>Panthera leo</em>) monitored between 1984 and 2014 in the Serengeti National Park, Tanzania.</p> <p>We assessed the season-specific effects of density measures at the intra- (number of females in a pride and male coalition size) and extra-group levels (number of nomadic coalitions in the home range of a group) using a Bayesian multistate capture-mark-recapture model for the survival and transition rates and Bayesian generalized linear mixed models for reproduction probability and recruitment. <br><br>The README file further describes each uploaded file.</p>
Viewing behavior and vertical eye-level light for non-image-forming effects
<p>When considering non-image-forming (NIF) light effects on people, knowing the light vertically at eye-level is necessary. However, people are dynamic in their behavior and constantly change their viewing direction. This means that light measured vertically towards a constant direction might differ from the actual light that reaches people’s eyes. If the difference is large, viewing behavior might need to be included in lighting design measurements and simulations predicting the potential of the light to induce NIF light effects. This dataset was collected during an experiment on the difference between the actual dynamic eye-level light of office workers while seated at a desk (dynamic condition) and light measured statically towards a computer screen (static condition). The dataset was collected to test the hypothesis: "There is a significant and relevant difference between simultaneously measured static and dynamic light conditions in an office environment occupied by one user." It includes measured and simulated light quantities (illuminance, alpha-opic quantities according to CIE S026 and light-driven alertness according to the non-visual direct response model) together with participants' measured face orientation (horizontal and vertical) in an office environment with a single user.</p>
Exploring the Relationship Between Upper Ocean States and the Falling Ice Radiative Effects using ECCO Product and Global Climate Models
<p><strong><span>Sensitivity test using CESM1-CAM5 following CMIP5 protocool from 1980-2005</span></strong></p> <p><strong><span>NOS: no falling ice radiative effects (FIREs), four data sets</span></strong></p> <p><strong><span>SON: with FIREs, for data sets</span></strong></p> <p><strong><span> Xsize = 362 Ysize = 182 Zsize = 18</span></strong></p> <p><strong><span>Format: netcdf</span></strong></p> <p><strong><span>Upper 200 meter ocean variables</span></strong></p> <p><strong><span>Annual mean (ANN)</span></strong></p> <p><strong><span>CESM2-var-NOS (or SON)-ANN.nc, var = (UO, VO, WO, TO) = (zonal velocity, meridional velocity, ascending velocity, potential temperature) : (cm/s, cm/s, cm/s, K)</span></strong></p>
Data associated with Altermagnetic superconducting diode effect
<p>Dataset for paper: Altermagnetic Diode Effect (https://arxiv.org/abs/2402.14071)</p>
Series data for the 22PN nonlinear memory effect in the l=2, m=0 mode.
<p>Dataset associated with the preprint arXiv:2407.19017, “Waveform models for the gravitational-wave memory effect: Extreme mass-ratio limit and final memory offset” by Arwa Elhashash and David A. Nichols. It contains the 22 post-Newtonian-order series data for the l=2, m=0 spin-weighted spherical harmonic mode of the gravitational-wave memory signal from an extreme-mass ratio inspiral with nonspinning black holes.</p>
The Effect of Prescription Drug Monitoring Programs (PDMPs) and Overdose Reversal Drug Accessibility Laws on Opioid Analgesic Mortality in the United States
<p><strong>Purpose: </strong>This analysis focuses on the impact of Prescription Drug Monitoring Programs (PDMPs) and increased layperson access to the overdose reversal drug Naloxone on prescription opioid mortality rates. The prescription opioid mortality rate was analyzed against state laws governing PDMPs and Naloxone accessibility to laypersons to evaluate if there was a correlation between mortality reduction and implementation of the laws.</p> <p>Three main analyses were conducted:</p> <ol> <li>Does a state’s opioid mortality reduction correlate to an effective PDMP and/or Naloxone law?</li> <li>Do states with strong PDMP laws have a corresponding opioid overdose mortality rate reduction?</li> <li>Do states with strong Naloxone accessibility laws have a corresponding opioid overdose mortality rate reduction?</li> </ol> <p><strong>Conclusion:</strong> Patterns show that PDMP and Naloxone accessibility can be successful in reducing mortality rates, but implementations and results vary dramatically between states. Further changes to both PDMPs and Naloxone accessibility are needed for states to see reliable and consistent mortality reductions. Changes to regulations need time to implement and take effect, meaning longer term measurements and data will be required to see if positive impacts can be sustained.</p> <ol> </ol> <p><strong>Data and Datasets:</strong></p> <p>There are three main datasets used in the analysis.</p> <ul> <li>Opioid mortality rate by state from 2006 to 2016. <ul> <li>This datasetis from the CDC website (<a href="https://www.cdc.gov/drugoverdose/data/statedeaths.html">https://www.cdc.gov/drugoverdose/data/statedeaths.html</a>). I used web scraping and regular expression search to extract the data and calculated mortality reduction of each state.</li> <li><em>Mortality reduction formula: (Highest mortality rate) – (2016 mortality rate)</em><br> </li> </ul> </li> <li>PDMP law implementation in each state and its timelinefrom January 1, 1998 to July 1, 2016. <ul> <li>Dataset is from the Prescription Drug Abuse Policy System(<a href="http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372">http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372</a>). This dataset encompasses laws regulating which professions have access to the database and for what purpose, whether practitioners can delegate their access, whether patients can see their own information, and the extent to which access to individually-identified records may be granted for law enforcement purposes.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing the PDMP.I performed the following data preparation to allow for analysis and visualization:</li> <li><em>PDMP law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul> <ul> <li>Naloxone accessibility to laypersons law implementation in each state and its timeline from January 1, 2001 to December 31, 2016. <ul> <li>Dataset is also from the Prescription Drug Abuse Policy System (<a href="http://pdaps.org./datasets/laws-regulating-administration-of-naloxone-1501695139">http://pdaps.org./datasets/laws-regulating-administration-of-Naloxone-1501695139</a>).This dataset focuses on state laws that provide civil or criminal immunity to licensed healthcare providers or lay responders for opioid antagonist administration.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing Naloxone accessibility to laypersons. I performed the following data preparation to allow for analysis and visualization:</li> <li><em>Naloxone accessibility law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul>
Dataset for "Contrasting Effects of Organic and Mineral Nitrogen Challenge the N-Mining Hypothesis for Soil Organic Matter Priming"
<p>Dataset for the article:</p> <p>Mason-Jones, K., Schmücker, N., Kuzyakov, Y. (2018) Contrasting Effects of Organic and Mineral Nitrogen Challenge the N-Mining Hypothesis for Soil Organic Matter Priming. Soil Biology and Biochemistry 124, 38-46, https://doi.org/10.1016/j.soilbio.2018.05.024</p>
A plant biodiversity effect resolved to a single genetic locus - datasets
<p>Despite extensive evidence that biodiversity promotes plant community productivity, progress towards understanding the mechanistic basis of this effect remains slow, impeding the development of predictive ecological theory and agricultural applications<em>. </em>Here, we analysed non-additive interactions between genetically divergent Arabidopsis accessions in experimental plant communities. By combining methods from ecology and genetics, we identified a major effect locus that promotes complementarity amongst genotypes and above-ground productivity in mixed communities. In experiments with near-isogenic lines, we show that this diversity effect can act independently of other genomic regions and be resolved to a single locus representing less than 0.3% of the genome. Using plant-soil-feedback experiments, we demonstrate that allelic diversity also causes genotype-specific soil legacy responses in a subsequent growing period. Our work thus shows that positive diversity effects can be linked to single Mendelian factors, and that a range of complex community properties, some of which manifest themselves even after the original community has disappeared<strong>, </strong>can have a simple, single cause. This may pave the way to novel breeding strategies, focussing on phenotypic properties that manifest themselves beyond isolated individuals, i.e. at a higher level of biological organisation.</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.