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1,045 results for “Generated Data”
Simulation data for Upstream shift of generation region for whistler-mode rising-tone emission in the magnetosphere
<p>Simulation data for the article "Upstream shift of generation region for whistler-mode rising-tone emission in the magnetosphere" by Nogi and Omura (2023).</p> <p>Nogi, T., & Omura, Y. (2023). Upstream shift of generation region of whistler-mode rising-tone emissions in the magnetosphere. <em>Journal of Geophysical Research: Space Physics</em>, <em>128</em>, e2022JA031024. https://doi. org/10.1029/2022JA031024</p>
Data and code for: Generation and applications of simulated datasets to integrate social network and demographic analyses
<p class="MsoNormal"><span>Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social networks and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals are observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers test other sampling considerations in social network studies.</span></p>
Load and generation time series for German federal states: Static vs. dynamic regionalization factors (data)
<p>This dataset contains regionalization factors for electricity generation and demand time series in Germany for the years 2019 - 2022. The factors can be used to distribute national generation and demand time series available from SMARD or ENTSO-E to federal state level. The methods underlying the regionalization factors are described in [1], with a focus on the year 2021. However, an extended version of the dataset covering the years 2019-2022 is also included for comprehensive analysis. Moreover, the dataset comprises the corresponding regionalized generation and demand time series at the federal state level of Germany. This time series has been generated using the provided distribution factors for the years 2019-2022 and corresponding generation and demand time series from SMARD [2]. Addtionally, the regionalization methodology for the distributed generation and demand data for the year 2021 has been supplemented with validation data, as described in [1]. This data has been cross-checked against the available SMARD Transmission System Operator (TSO) data. A description of the preprocessing required to obtain the TSO data comparison is provided in a separate .txt file. A PDF document has been prepared, which includes scatter plots that illustrate a comparison between actual and allocated generation per production type or demand data for TSOs on an hourly basis for the year 2021.</p> <p><strong>"static_regionalization_factors.2021[csv, xlsx]"</strong></p> <p>Each column corresponds to one factor per federal state and per production type or demand. Regionalization factors are based on share of generation capacity in each state (generation) or population and GDP (demand).</p> <p><strong>"dynamic_regionalization_factors_2021.[csv, xlsx]"<br> “dynamic_regionalization_factors_all.[csv, xlsx]”</strong></p> <p>Each column corresponds to one factor per federal state and per production type or demand. Each row corresponds to a specific hour of the years 2019 through 2022. Regionalization factors are based on a combination of per unit generation data and share of generation capacity in each state, simulated renewable generation data based on spatio-temporal weather data and distribution of wind and solar generation capacities, and a regionalized load dataset for 2015 [3].</p> <p><strong>“time_series_federal_states_all.[csv, xlsx]”</strong></p> <p>Each column corresponds to the allocated electricity generation or demand per federal state per production type or demand in units of MWh. Each row corresponds to a specific hour of the years 2019 through 2022. The regionalized generation and demand time series has been created by utilizing the dynamic regionalization factors provided in the dataset, in conjunction with the national electricity generation and demand data of Germany as provided by SMARD [2].</p> <p><strong>“TSO_actual.[csv, xlsx]”<br> “TSO_allocated.[csv, xlsx]”</strong></p> <p>Each column corresponds to the spatially aggregated electricity generation per type or demand per TSO in units of GWh. Each row corresponds to one hour of the year 2021. The TSOs in Germany do not hold direct responsibility for individual federal states, but rather for specific regions. In order to assess the validity of the regionalization methodology employed, it was necessary to generate data at the NUTS3 level and subsequently aggregate it to correspond with the relevant TSOs. The data is pre-processed at NUTS3 level and then undergoes the same methodology as outlined in [1]. The preprocessing steps required to map the installed capacity to the TSO level are explained in the accompanying .txt file. The allocated generation and demand data are aggregated to correspond to the TSO level using a shapefile of mapped regions in Germany that correspond to the TSOs [4]. The actual TSO data is generation and demand as published by SMARD [2]. The accompanying PDF presents scatter plots that showcase the actual vs allocated hourly generation types or demand per TSO, expanding on the information provided in the article.</p> <p>[1] M. Sundblad, T. Fürmann, A. Weidlich and M. Schäfer, "<a href="https://arxiv.org/abs/2304.02951">Load and generation time series for German federal states: Static vs. dynamic regionalization factors</a>," <em>2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)</em>, Aachen, Germany, 2023, pp. 1-6, doi: 10.1109/OSMSES58477.2023.10089686.</p> <p>[2] Bundesnetzagentur | <a href="https://www.smard.de/home">SMARD.de</a></p> <p>[3] Matthias Kühnbach, Anke Bekk, and Anke Weidlich (2021). <a href="https://www.forecast-model.eu/forecast-en/content/publications.php">Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany</a>. Energy Strategy Reviews, 34:100609, 20</p> <p>[4] Frysztacki, Martha Maria. (2023). Mapping of districts to control zones of German Transmission System Operators (TSOs) (v0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7530196">https://doi.org/10.5281/zenodo.7530196</a></p>
Data for Process Design and Energy Assessment of an Onboard Carbon Capture System with Boilers or Heat Pumps for Additional Steam Generation
<p>1. Supporting file includes main stream information used in Aspen HYSYS model, process simulations of boiler and heat pump for model construction.<br> 2. Supporting file also includes main information used in ProMAX model, process simulations of carbon capture process for model construction.</p>
Digital Twin of a Multi-Arm Robot Platform based on Isaac Sim for Synthetic Data Generation
<p>This data set is required by the following repository<br> https://github.com/AISciencePlatform/icra2023_synthetic_data_pretraining_for_robotics</p>
Data from: Triggered Golgi membrane enrichment promotes PtdIns(4,5)P2 generation for plasma membrane repair
<p><span>The maintenance of plasma membrane integrity and a capacity for efficiently repairing damaged membranes are essential for cell survival. Large-scale wounding depletes various membrane components at the wound sites, including phosphatidylinositols, yet little is known about how phosphatidylinositols are generated after depletion. Here, working with our <em>in</em> <em>vivo C. elegans</em> epidermal cell wounding model, we discovered phosphatidylinositol 4-phosphate (PtdIns4<em>P</em>) accumulation and local phosphatidylinositol 4,5-bisphosphate [PtdIns(4,5)<em>P<sub>2</sub></em>] generation at the wound site. We found that PtdIns(4,5)<em>P<sub>2</sub></em> generation depends on the delivery of PtdIns4<em>P</em>, PI4K, and PI4P 5-kinase PPK-1. In addition, we show that wounding triggers enrichment of the Golgi membrane to the wound site, and that is required for membrane repair. Moreover, genetic and pharmacological inhibitor experiments support that the Golgi membrane provides the PtdIns4<em>P</em> for PtdIns(4,5)<em>P<sub>2</sub></em> generation at the wounds. Our findings demonstrate how the Golgi apparatus facilitates membrane repair in response to wounding and offers a valuable perspective on cellular survival mechanisms upon mechanical stress in a physiological context. </span></p>
Supporting data for Assessing clouds using satellite observations through three generations of global atmosphere models
<p>Monthly data from CAM4, CAM5, and CAM6 that are needed to reproduce the analysis and figures in the manuscript entitled: Assessing clouds using satellite observations through three generations of global atmosphere models by Brian Medeiros, Jonah Shaw, Jennifer Kay, and Isaac Davis.</p>
TFG Systematization process of generating semantic data and ontology
<pre>Set of TALIS files, the main source of data for the investigation, in CSV format.General ontology manually and by new software. Semantic data, as well as the DSL code. Finally, the web design of the new tool.</pre>
Data for: A Modular Double Electrode Flow Cell with Exchangeable Generator and Detector Electrodes
<p>Raw data and processed data shown in figures of the publication titled:</p> <p>"A Modular Double Electrode Flow Cell with Exchangeable Generator and Detector Electrodes"</p> <p>DOI: <a href="https://doi.org/10.1002/celc.202300126">10.1002/celc.202300126</a></p> <p>by</p> <p>Frederik J. Stender<sup>[a]</sup>, Keisuke Obata<sup>[b]</sup>, Max Baumung<sup>[a,c]</sup>, Fatwa F. Abdi<sup>[b]</sup>, Marcel Risch<sup>[a,c]</sup></p> <p>[a] Frederik Johannes Stender, Max Baumung, Dr. Marcel Risch<br> Institut für Material Physik<br> Georg-August-Universität Göttingen<br> Friedrich-Hund-Platz 1, 37085 Göttingen<br> E-mail: mrisch@material.physik.uni-goettingen.de</p> <p>[b] Dr. Keisuke Obata, Dr. Fatwa Firdaus Abdi<br> Institut für Solare Brennstoffe<br> Helmholtz-Zentrum Berlin für Materialien und Energie GmbH<br> Hahn-Meitner-Platz 1, 14109 Berlin</p> <p>[c] Dr. Marcel Risch<br> Nachwuchsgruppe Gestaltung des Sauerstoffentwicklungsmechanismus<br> Helmholtz-Zentrum Berlin für Materialien und Energie GmbH<br> Hahn-Meitner-Platz 1, 14109 Berlin<br> E-mail: marcel.risch@helmholtz-berlin.de</p>
Data from: Comparative LCA studies of simulated HMF-biorefineries from maize and miscanthus as an example of first- and second-generation biomass as a tool for process development
<p class="MsoNormal"><span>5-Hydroxymethylfurfural (HMF) is a versatile platform chemical for a fossil free, bio-based chemical industry. HMF can be produced by using fructose as a feedstock. Using edible, first-generation biomass to produce chemicals has been questioned in terms of potential competition with food supply. Second-generation biomass like miscanthus could be an alternative. However, there is a lack of information if second-generation lignocellulosic biomass is a more sustainable feedstock to produce HMF. Therefore, a life cycle assessment was performed in this study to determine the environmental impacts of HMF production from miscanthus and to compare it with HMF from high fructose corn syrup (HFCS). HFCS from either Hungary or Baden-Württemberg (Germany) was considered. Compared to the HFCS biorefineries the miscanthus concept is producing less emissions in all impact categories studied, except land occupation. Overall, the production and usage of second-generation biomass could be especially beneficial in areas where the use of N-fertilizers is restricted. Besides, conclusions for the further development of the on-farm-biorefinery concept were elaborated. For this purpose, process simulations from a previous study were used. Results of the previous study in terms of TEA and the current LCA study in terms of environmental sustainability indicate that the lignin depolymerization unit in the miscanthus biorefinery has to be improved. The scenario without lignin depolymerization performs better in all impact categories. The authors recommend to not further convert the lignin to products like phenol and other aromatic compounds. The results of the contribution analyses show that the major impact in the HMF production is caused by the auxiliary materials in the separation units and the required heat. Further technical development should focus on efficient heat as well as solvent use and solvent recovery. At this point further optimizations will lead to reduced emissions and costs at the same time. The presented data set is the used inventory to model the environmental impacts. </span></p>
Data from "Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness"
<p>This repository contains the data from the paper, "Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness." </p> <p>Relevant URLs:</p> <p>https://hewlettpackard.github.io/trust-ml/</p> <p>https://github.com/HewlettPackard/trust-ml/</p> <p> </p> <p>Abstract:</p> <p>We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models. The RLAB framework allows users to customize the types of distortions to be optimally applied to images, which helps address the specific distortions relevant to their deployment. The benchmark can generate datasets at various distortion levels to assess the robustness of different image classifiers. Our results show that the adversarial samples generated by our framework with any of the image classification models, like ResNet-50, Inception-V3, and VGG-16, are effective and transferable to other models causing them to fail. These failures happen even when these models are adversarially retrained using state-of-the-art techniques, demonstrating the generalizability of our adversarial samples. Our framework also allows the creation of adversarial samples for non-ground truth classes at different levels of intensity, enabling tunable benchmarks for the evaluation of false positives. We achieve competitive performance in terms of net $L_2$ distortion compared to state-of-the-art benchmark techniques on CIFAR-10 and ImageNet; however, we demonstrate our framework achieves such results with simple distortions like Gaussian noise without introducing unnatural artifacts or color bleeds. This is made possible by a model-based reinforcement learning (RL) agent and a technique that reduces a deep tree search of the image for model sensitivity to perturbations, to a one-level analysis and action. The flexibility of choosing distortions and setting classification probability thresholds for multiple classes makes our framework suitable for algorithmic audits.</p>
Supplementary Data for "Assessing weathering, pedogenesis, and silt generation in granitoid-hosted soils from contrasting hydroclimates"
<p>Supplementary data includes original sample names and locations, raw geochemical and granulometric data, and calculated geochemical data represented as figures for the manuscript titled "<strong>Assessing weathering, pedogenesis, and silt generation in granitoid-hosted soils from contrasting hydroclimates"</strong>. All datasets are in the same excel file on separate tabs. All data were processed according to established procedures cited in the manuscript text. All software used is open access: ImageJ, adobe illustrator, and Microsoft excel.</p>
Raw data of high-dimensional aptamer selection generated by ProSELEX
<p>This is the raw selection dataset generated by ProSELEX pipeline (https://www.nature.com/articles/s41557-023-01207-z). The target of selection is human myeloperoxidase (MPO). See https://github.com/dwangnu/AptaZ for instructions.</p>
Data and analysis code of "Forestation at the right time with the right species can generate persistent carbon benefits in China"
<p>This collection contains the datasets used in our study ‘<strong><em>Forestation at the right time with the right species can generate persistent carbon benefits in China</em></strong>’.</p> <p> </p> <p><strong><em>Part A: Data</em></strong></p> <p>Most of the data presented here are after pre-processing, such as transforming the projection, extracting variables, clipping to the study region (70<sup>o</sup>E-140<sup>o</sup>E,15<sup>o</sup>N-55<sup>o</sup>N), and resampling to 1-km.</p> <p>The original source of these data sets (usually global, at different resolutions) is given in 'data_original.txt' as well as in the 'Data availability' of the main text.</p> <p>1. Climate_china_1km.7z: This compressed file contains the annual precipitation and temperature from Peng et al. 2019 at 1km.</p> <p>2. Soil_china_1km.7z: This compressed file contains the soil properties derived from Soilgrid250m for China at 1-km resolution.</p> <p>3. Topography_china_1km.7z: This compressed file contains the topographic properties derived from Global Multi-resolution Terrain Elevation Data 2010 for China at 1-km resolution.</p> <p>4. MaxEnt_process.7z: This compressed file contains all the input data and model results of the MaxEnt model: 1) environment layers in ’.asc’, 2) rarefied occurrence points for the 15 forest types, 3) MaxEnt results in ’.tif’ (average of the 10-folds results)</p> <p>5. Potential_china_forest_1km.7z: contains the potential forest distributions for China at 1-km resolution from multiple source (Random Forest, WRI and ORCHIDEE). Note: the forest distribution is in the form of logical variables in the .mat file, where a value of 1 or true means that the grid point is potentially forestable, and a value of 0 or false means this grid is not suitable for forest.</p> <p>6. Existing_china_forest_1km.7z: contains the existing forest distributions for China at 1-km resolution from multiple source (FI2013-2017, Hansen, MODIS, ESA-CCI, CNLUCC, GLC-FCS30 and GlobeLand30). Note: the existing forest distribution is in the form of logical variables in the .mat file, where a value of 1 or true means there is forest distribution, and a value of 0 or false means there is currently no forest distribution.</p> <p>7. Crop_urban_china_1km.7z: similar to the Existing_china_forest_1km.zip, but stores the distribution of cropland and urban.</p> <p>8. Masks_area_china_1km.7z: area mask and the shp files of the national and provincial boundaries of China.</p> <p>9. CMIP6_outputs.7z: contains historical (1970-2014) and future (2015-2100) climate and CO2 fertilization factor simulated by Earth System Models participating in CMIP6.</p> <p>10. Ori_carbon_all_grid_1km.mat: Living biomass carbon densities in 2010 for China at 1-km (unit: Mg C ha<sup>-1</sup>). Both aboveground and belowground biomass carbon values are included. The original biomass map is from Spawn et al. 2020.</p> <p>11. Forest_inventory_data_5th_9th.xlsx: 1) The forest area reported in 5th to 9th national forest inventory 2) The forest area of different age classes derived from the 9th national forest inventory.</p> <p>12. Forest_age_CN2019.7z: the forest stand age map for China updated to 2019.</p> <p>13. data_original.txt: the original source of these data sets</p> <p><strong>Part B: MATLAB Code</strong></p> <p>This file (Matlab_code.7z) contains the code, functions and parameters for our analysis of the data, mainly MATLAB files (.m or mat)</p> <p><strong>Part C: Demo/Example data and code</strong></p> <p>This file (Demo.7z) contains the demo of our code running, which includes the demo code along with code comments, input data for the demo, and the expected output results.</p> <p><strong>Part D: Docs</strong></p> <p>Reference and guidelines (Docs.7z).</p> <p>If you have any questions or suggestions, please contact xuhaotony@pku.edu.cn</p>
Simulation data for HIVE experiment generated using VirtualLab
<p>HIVE is a high heat flux experimental facility at the UK Atomic Energy Authority designed to test plasma facing components for fusion devices. This data has been generated to train machine learning models to maximise the impact of the facility. There are 4 datasets;</p> <p>PowerVariation.hdf - This is for training models to predict the amount of power delivered to a component by the induction heating system, and the variation (non-uniformity) of the heating profile.</p> <p>JouleHeating.hdf - Values for the Joule heating on the coil adjacent surface. This is used to train a 2D surrogate model of the heating profile. Values are in W/m^3.</p> <p>TempNodal.hdf - Temperature at each node of the component. This is used to train a 3D surrogate model of the temperature field throughout the component. Values are in Celcius.</p> <p>VMNodal.hdf - Von Mises stress at each node within the component. This is used to train a 3D surrogate model of the Von Mises tress field throughout the component. Values are in MPa.</p> <p>HIVE_component.med - Mesh file which is required for constructing surrogate models.</p>
Swedish Diachronic Corpus - User-generated Data - Wikipedia
<p>The Swedish Diachronic Corpus (https://www2.lingfil.uu.se/person/pettersson/svediakorp/) is a project funded by Swe-Clarin (<a href="https://sweclarin.se/eng">https://sweclarin.se/eng</a>). The purpose of the project is to provide a corpus of texts covering the time period from Old Swedish to present day, with a wide variety of text types and freely available for download and search. The texts are provided in a plain text format and in a uniform CoNLL format, with placeholders for linguistic annotation. </p> <p>The dataset provided here is the Swedish Wikipedia section of the User-generated Data in the Swedish Diachronic Corpus. For other datasets within the corpus, see further the corpus website: https://www2.lingfil.uu.se/person/pettersson/svediakorp/. </p> <p>The project members are Eva Pettersson (Uppsala University) and Lars Borin (University of Gothenburg). For questions or comments, or if you are aware of any corpus resource that could be included in the Swedish Diachronic Corpus, don't hesitate to contact us!<br> <br> Eva Pettersson Department of Linguistics and Philology, Uppsala University eva.pettersson@lingfil.uu.se<br> Lars Borin Department of Swedish, University of Gothenburg lars.borin@svenska.gu.se</p>
Swedish Diachronic Corpus - User-generated Data - Blog text
<p>The Swedish Diachronic Corpus (https://www2.lingfil.uu.se/person/pettersson/svediakorp/) is a project funded by Swe-Clarin (<a href="https://sweclarin.se/eng">https://sweclarin.se/eng</a>). The purpose of the project is to provide a corpus of texts covering the time period from Old Swedish to present day, with a wide variety of text types and freely available for download and search. The texts are provided in a plain text format and in a uniform CoNLL format, with placeholders for linguistic annotation. </p> <p>The dataset provided here is the blog section of the User-generated Data in the Swedish Diachronic Corpus. For other datasets within the corpus, see further the corpus website: https://www2.lingfil.uu.se/person/pettersson/svediakorp/. </p> <p>The project members are Eva Pettersson (Uppsala University) and Lars Borin (University of Gothenburg). For questions or comments, or if you are aware of any corpus resource that could be included in the Swedish Diachronic Corpus, don't hesitate to contact us!<br> <br> Eva Pettersson Department of Linguistics and Philology, Uppsala University eva.pettersson@lingfil.uu.se<br> Lars Borin Department of Swedish, University of Gothenburg lars.borin@svenska.gu.se</p>
Swedish Diachronic Corpus - User-generated Data - Familjeliv Part I
<p>The Swedish Diachronic Corpus (https://www2.lingfil.uu.se/person/pettersson/svediakorp/) is a project funded by Swe-Clarin (<a href="https://sweclarin.se/eng">https://sweclarin.se/eng</a>). The purpose of the project is to provide a corpus of texts covering the time period from Old Swedish to present day, with a wide variety of text types and freely available for download and search. The texts are provided in a plain text format and in a uniform CoNLL format, with placeholders for linguistic annotation. </p> <p>The dataset provided here is the first part of the Familjeliv section of the User-generated Data in the Swedish Diachronic Corpus. For other datasets within the corpus, see further the corpus website: https://www2.lingfil.uu.se/person/pettersson/svediakorp/. </p> <p>The project members are Eva Pettersson (Uppsala University) and Lars Borin (University of Gothenburg). For questions or comments, or if you are aware of any corpus resource that could be included in the Swedish Diachronic Corpus, don't hesitate to contact us!<br> <br> Eva Pettersson Department of Linguistics and Philology, Uppsala University eva.pettersson@lingfil.uu.se<br> Lars Borin Department of Swedish, University of Gothenburg lars.borin@svenska.gu.se</p>
Swedish Diachronic Corpus - User-generated Data - Familjeliv Part II
<p>The Swedish Diachronic Corpus (https://www2.lingfil.uu.se/person/pettersson/svediakorp/) is a project funded by Swe-Clarin (<a href="https://sweclarin.se/eng">https://sweclarin.se/eng</a>). The purpose of the project is to provide a corpus of texts covering the time period from Old Swedish to present day, with a wide variety of text types and freely available for download and search. The texts are provided in a plain text format and in a uniform CoNLL format, with placeholders for linguistic annotation. </p> <p>The dataset provided here is the second part of the Familjeliv section of the User-generated Data in the Swedish Diachronic Corpus. For other datasets within the corpus, see further the corpus website: https://www2.lingfil.uu.se/person/pettersson/svediakorp/. </p> <p>The project members are Eva Pettersson (Uppsala University) and Lars Borin (University of Gothenburg). For questions or comments, or if you are aware of any corpus resource that could be included in the Swedish Diachronic Corpus, don't hesitate to contact us!<br> <br> Eva Pettersson Department of Linguistics and Philology, Uppsala University eva.pettersson@lingfil.uu.se<br> Lars Borin Department of Swedish, University of Gothenburg lars.borin@svenska.gu.se</p>
Data From: Dynamic environments generate geographic fluctuations in population structure of an inland shorebird
<p>Data From: Dynamic environments generate geographic fluctuations in population structure of an inland shorebird. Table S2: δ2H values in breast feathers sampled from 352 pre-fledged young mountain plovers from across the breeding range used for isoscape calibration. The table provides the latitude and longitude coordinates for each δ2H feather sample. </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.