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708 results for “Global dataset”
Dataset of the study "Think globally, act locally": A glocal approach to old and new literacies for social media skills
<p>This Zenodo item contains the dataset of the study: Manca, S., Bocconi, S., & Gleason, B. (2020). “Think globally, act locally”. A glocal approach to old and new literacies for social media skills. Computers & Education.</p> <p>Abstract</p> <p>Despite the documented number of studies that investigate social media in teaching and learning settings, the topic of social media literacy is still an under-researched area. This study adopts the theoretical lens of New Literacy studies to suggest a combined perspective for investigating social media literacies. This perspective considers both social media skills that are transversal across different social media (global skills) or that pertain to a specific social media platform (local skills). It examines practices that are decontextualized (literacy as something to be acquired) or situated and context-dependent (literacy through participation). To map current research on social media skills, a systematic literature review was conducted. Analysis of 54 publications was carried out following the UNESCO DLGF framework for digital literacy competencies, and also using a critical lens based on four metaphors whereby, for learning purposes, social media are seen as a <em>tool</em>, as a <em>process</em>, as <em>collaboration</em>, and as <em>participation</em>. The results show that most of the studies consider global social media skills, while only a few examine skills sets specific to a particular social media platform. Besides, most of the identified skills concern decontextualized practices, with very few studies emphasizing the importance of fostering situated social media practices. We conclude that there is a need for more expansive theoretical elaboration in the field, and provide a number of recommendations for investigating, understanding, and designing educational curricula and activities that support the development of social media literacy.</p>
Global Transnational Mobility Dataset
<p>The <strong>Global Transnational Mobility Dataset</strong> provides estimates of country-to-country cross-border human travels on the basis of worldwide statistics on tourism and air passenger traffic. The two sources are adjusted and merged, resulting into a set of data that covers more than 15 billion estimated travels over the years 2011 to 2016.</p>
Dataset of global extent of marine infrastructure as of 2018
<p>See methods in article:</p> <p>Bugnot AB, Mayer-Pinto M, Airoldi L, Heery EC, Johnston EL, Critchley LP, Strain EMA, Morris RL, Loke LHL, Bishop MJ, Sheehan EV, Coleman RA, Dafforn KA (2020) "Current and projected global extent of marine built structures" Nature Sustainability, 10.1038/s41893-020-00595-1</p>
Dataset for Gelatinous zooplankton-mediated carbon flows in the global oceans: A data-driven modeling study
<p>Gridded dataset of gelatinous zooplankton (GZ) biomass (mg C m<sup>-3</sup>) and numeric density (individuals m<sup>-3</sup>), time-averaged, in a 1-degree grid. Data are separated by phyla: Cnidaria, Ctenophora, and Chordata (pelagic tunicates). Original data compiled as part of the Jellyfish Database Initiative Project (JeDI; Condon et al. 2015, doi:10.1575/1912/7191) and converted to carbon biomass units for Lucas et al. 2014.</p> <p>Cnidarian additions to this dataset include records from the northern California Current (Brodeur et al., 2014) and Gulf of Mexico (Robinson et al., 2015). Chordata additions include salps from the Bermuda Atlantic Time Series (BATS; Stone & Steinberg, 2014), Western Antarctic Peninsula (WAP; Steinberg et al., 2015), and Southern Ocean, from KRILLBASE (Atkinson et al., 2017). Note that we excluded the KRILLBASE records from the WAP region that to prevent double-counting. See Methods in Luo et al. (2020) for details on biometric conversions to carbon biomass.</p> <p>Data were averaged by time (season, then year), and then within each 1-degree grid cell.</p> <p> </p> <p> </p> <p>Code for the model using this dataset is available at: <a href="https://github.com/jessluo/gz_biogeochem_pub">https://github.com/jessluo/gz_biogeochem_pub</a></p> <p> </p> <p><strong>Luo, Jessica Y.</strong>, Condon, R. H., Stock, C. A., Duarte, C. M., Lucas, C. H., Pitt, K. A., & Cowen, R. K. (2020). Gelatinous zooplankton‐mediated carbon flows in the global oceans: A data‐driven modeling study. <em>Global Biogeochemical Cycles</em>, 34, e2020GB006704. <a href="https://doi.org/10.1029/2020GB006704">https://doi.org/10.1029/2020GB006704</a></p>
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (1/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file with the name starting 'data' contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) (unit: hPa) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T) (unit: K), zonal wind velocity (u) (unit: m/s), meridional wind velocity (v) (unit: m/s) and vertical wind velocity (w) (unit: m/s), in snapshots of every 1/6 Sol for the periods of 30 degrees in Ls per a file as described below. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020), which is based on the observed dust opacity in Mars Year 24 (MY34).</p> <p>data180rdc-my34.tar.xz: for Ls=180-210 (49 Sols)</p> <p>data210rdc-my34.tar.xz: for Ls=210-240 (47 Sols)</p> <p>data240rdc-my34.tar.xz: for Ls=240-270 (46 Sols)</p> <p>The .tar.xz files can be extracted in Linux with 'tar Jxvf' command, and .grd and .ctl files with the same stem are generated.</p> <p>The file 'flux61ls5-my34.tar.xz' contains the three-dimensional fluxes and physical parameters calculated from the model output with the MY34 dust scenario. The contents are (T')^2, (u')^2, (v')^2, u'v', u'w', v'w' T(bar), u(bar), v(bar), squared Brunt-Vaisala frequency, and geopotential height. (bar) denotes the sum of the total wavenumber s=0-60 components, and the dash denotes the deviation from (bar), i.e. sum of the total wavenumber s=61-106 components. There are 36 time grids between Ls=182.5 and Ls=357.5 with the step of Ls=5 degrees. Kinetic and potential energies can be derived from these values using the formulae in the paper.</p> <p>The file 'flux61ls5-lowdust.tar.xz' is the same as 'flux61ls5-my34.tar.xz', except the model output with the 'low-dust' scenario (Kuroda et al., 2019; Kuroda, 2019a, 2019b).</p> <p>The file 'scripts.zip' contains the FORTRAN scripts to derive the fluxes and physical parameters equivalent to the file 'flux61ls5-my34.tar.xz' from the model outputs in this dataset and Kuroda (2020), i.e. data180rdc-my34.tar.xz, data210rdc-my34.tar.xz, data240rdc-my34.tar.xz, data270rdc-my34.tar.xz, data300rdc-my34.tar.xz and data330rdc-my34.tar.xz. Also, the fluxes and physical parameters equivalent to the file 'flux61ls5-lowdust.tar.xz' can be derived with those scripts from the model outputs data180rdc.tar.xz, data210rdc.tar.xz, data240rdc.tar.xz, data270rdc.tar.xz, data300rdc.tar.xz and data330rdc.tar.xz which are available in Kuroda (2019a, 2019b).</p>
Girasol, a sky imaging and global solar irradiance dataset
<p>The energy available in Micro Grid (MG) that is powered by solar energy is tightly related to the weather conditions in the moment of generation. Very short-term forecast of solar irradiance provides the MG with the capability of automatically controlling the dispatch of energy. We propose to achieve this using a data acquisition systems (DAQ) that simultaneously records sky imaging and Global Solar Irradiance (GSI) measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed as the <em>Girasol Machine</em> (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on an outdoor tracker. The tracker updates its pan an tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal support next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun's positions, has been tagged with timestamps.</p>
A global GOME-2 monthly SIF dataset (2007-2018) with correction of temporal degradation
<p>Monthly instrument degradation corrected 0.5 degree GOME-2 SIF datasets on a global scale from 2007 to 2018. The sun-induced fluorescence global product Joiner et al. (2017) is firstly uscaled to a monthly resolution using a APAR based algorithm by Hu et al. (2018). Then, the mothly GOME-2 SIF was corrected based on a pseudo-invariant method to eliminate the temporal degradation of GOME-2 satellite sensor. Files are organized in TIF format.</p>
Dataset accompanying Maldonando et al 2015 Global Ecology and Biogeography
<p>Dataset containing species records of the plant tribe Cinchoneae (Rubiaceae). Three classes of records are identified: those downloaded from gbif.org, those that we accepted as correct, those that we rejected as correct, and those that we included from new sources. For details please see the original Open Access publication at http://onlinelibrary.wiley.com/doi/10.1111/geb.12326/abstract: </p> <p>Maldonado, C., C. Molina, A. Zizka, C. Persson, Taylor, C., J. Alban, E. Chilquillo, N. Rønsted, Antonelli A (2015). Estimating species diversity and distribution in the era of Big Data: To what extent can we trust public databases? Global Ecology and Biogeography. DOI: 10.1111/geb.12326</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024)
<p>The data contains simulation results from 2000-2024, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671245</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671253</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999)
<p>The data contains simulation results from 1975-1999, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671245</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671254</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974)
<p>The data contains simulation results from 1950-1974, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:<br>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671253</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671254</p>
Reconstructed Three-decade Global Fine-Grained Nighttime Light Dataset
<p>Nighttime light (NTL) is a foundational data source for studying human activities from a remote sensing perspective. This dataset from 1992 to 2021 is the first global long-term and fine-grained NTL observations. It represents a milestone in facilitating the study of human activities. It is created by a new super-resolution model DeepNTL which converts DMSP-OLS images into NPP-VIIRS images. Compared with baseline models, including RCAN, SwinIR and AutoEncoder, DeepNTL has the hightest accuracy and best generalization ability for untrained years. It provides a good extension of NPP-VIIRS to the early years, and the future annual NPP-VIIRS data can be directly appended to this dataset by users own. More information about the dataset can be found in the "read_me.txt" file. Technical details and evaluations are presented in our paper. Any questions are welcome to be sent to jinyuguo23@m.fudan.edu.cn.</p>
Global Vibrio Cholerae O1 dataset
<p>This repository contains six files. Files with the n4196 file notation pertain the full core genome alignment, while the n100 FASTA file can be used for testing purposes. The files (.csv and .tsv) conains metadata of the sequences in the FASTA and tree files. Both core genome alignments (FASTA files) contain sequence outputs from SNIPPY and has not been processed through the clustering or masking steps in CholeraSeq. </p>
Global continuous 0.05 degree atmospheric carbon dioxide dataset (GCXCO2) based OCO-2 satellite, CAMS and CarbonTracker simulation data from 2000 to 2020
<p>This dataset provides global seamless 8-day XCO2 (column-averaged CO2 dry air mole fraction) with a spatial resolution of 0.05 degree from 2000 to 2020. The unit is ppm. The detailed process and product validation accuracy can be found in our paper at https://doi.org/10.1016/j.scitotenv.2024.177051</p>
Dataset for paper "Global method for gender profile estimation from distribution of first names"
<p>Full dataset for paper "<i>Global method for gender profile estimation from distribution of first names</i>". See https://arxiv.org/abs/2305.07587</p>
Dataset of global plant-soil feedback
<p>Plant-soil feedback (PSF) is an important mechanism determining plant community dynamics and structure. Understanding the geographic patterns and drivers of PSF is essential for understanding the mechanisms underlying geographic plant diversity patterns. We compiled a large dataset containing 5969 observations of PSF from 202 studies to demonstrate the global patterns and drivers of PSF for woody and non-woody species. Overall, PSF was negative on average and was influenced by plant attributes and environmental settings. Woody species PSFs did not vary with latitude, but non-woody PSFs were more negative at higher latitudes. PSF was consistently more positive with increasing aridity for both woody and non-woody species, likely due to increased mutualistic microbes relative to soil-borne pathogens. These findings were consistent between field and greenhouse experiments, suggesting that PSF variation can be driven by soil legacies from climates. Our findings call for caution to use PSF as an explanation of the latitudinal diversity gradient and highlight that aridity can influence plant community dynamics and structure across broad scales through mediating plant soil-microbe interactions.</p>
The global distribution of plants used by humans datasets: list of utilised species, occurrence data and model outputs at 10 arc-minutes spatial resolution
<p>Datasets and model outputs used to map the global distribution of utilised plants by humans. The folder is composed of two subfolders <em>raw_data</em> and <em>processed_data</em> containing respectively the list of utilised plant species modelled -<em>utilised_plants_species_list.csv</em>-, and their occurrence data -<em>occurrence_data.zip-</em> and predicted distribution -<em>species_proba_per_cell.rds-.</em></p> <p> </p> <ul> <li>The file <em>utilised_plants_species_list.csv</em> in the <em>raw_data</em> folder contains a<strong> </strong>list of 35687 plant species (and hybrids) used by humans and 10 plant use categories with the following 14 fields:</li> </ul> <p><strong>plant_ID:<em> </em></strong>plant identifier number ranging from between 1-35687</p> <p><strong>binomial_acc_name:</strong> binomial accepted name of the plant species</p> <p><strong>author_acc_name</strong>: name of the author(s)</p> <p><strong>is_hybrid:</strong> logical TRUE or FALSE indicating whether the species is an hybrid or not.</p> <p><strong>AnimalFood:</strong> forage and fodder for vertebrate animals only.</p> <p><strong>EnvironmentalUses:</strong> examples include intercrops and nurse crops, ornamentals, barrier hedges, shade plants, windbreaks, soil improvers, plants for revegetation and erosion control, wastewater purifiers, indicators of the presence of metals, pollution, or underground water.</p> <p><strong>Fuels:</strong> charcoal, petroleum substitutes, fuel alcohols, etc. Given the importance of energy plants for people, those were distinguished from Materials.</p> <p><strong>GeneSources:</strong> wild relatives of major crops which may possess traits associated with biotic or abiotic resistance and may be valuable for breeding programs.</p> <p><strong>HumanFood:</strong> food for humans only, including beverages and food additives.</p> <p><strong>InvertebrateFood:</strong> plants consumed by invertebrates used by humans, such as bees, silkworms, lac insects and edible grubs.</p> <p><strong>Materials:</strong> woods, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, lipids, etc. and their derived products.</p> <p><strong>Medicines:</strong> both human and veterinary.</p> <p><strong>Poisons:</strong> plants which are poisonous to both vertebrates and invertebrates, both accidentally and intentionally, e.g., for hunting and fishing, molluscicides, herbicides, insecticides.</p> <p><strong>SocialsUses:</strong> plants used for social purposes, which cannot be defined as food or medicine, for instance, masticatories, smoking materials, narcotics, hallucinogens and psychoactive drugs, and plants with ritual or religious significance.</p> <p><strong>Totals:</strong> total number of uses recorded for a species</p> <p> </p> <ul> <li>The zipfile <em>occurrence_data.zip</em> in the <em>processed_data</em> folder contains 35687 Comma Separated Values (CSV) files, one for each species, containing curated geographic occurrence records used to build species distribution models with the following 14 fields:</li> </ul> <p><strong>Species:</strong> the binomial accepted name of the species</p> <p><strong>Fullname:</strong> same as species</p> <p><strong>decimalLongitude:</strong> the geographic longitude of the occurrence records of the species in decimal degrees</p> <p><strong>decimalLatitude:</strong> the geographic latitude of the occurrence records of the species in decimal degrees</p> <p><strong>countryCode:</strong> a three-letter standard abbreviation for the country of the occurrence locality</p> <p><strong>coordinateUncertaintyinMeters</strong>: indicator for the accuracy of the coordinate location, described as the radius of a circle around the stated point location</p> <p><strong>year:</strong> year of the observation of the occurrence record of the species</p> <p><strong>individualCount:</strong> the number of individuals present at the time of the observation</p> <p><strong>gbifID:</strong> unique identifier number for the occurrence from the original database</p> <p><strong>basisOfRecords:</strong> the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen</p> <p><strong>institutionCode</strong>: the name of the institution or organization listed as the data publisher on GBIF</p> <p><strong>establishmentMeans:</strong> statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans</p> <p><strong>is_cultivated_observation:</strong> whether or not an organism is cultivated</p> <p><strong>sourceID:</strong> name of the source database</p> <p> </p> <ul> <li>The file <em>species_proba_per_cell.rds</em> in the <em>processed_data</em> folder is<em> a R Data Serialization </em>(RDS) file containing a data.table object with the following 3 fields:</li> </ul> <p><strong>plant_ID:</strong><em> </em>plant identifier number ranging from between 1-35687</p> <p><strong>proba:</strong> species occurrence probability</p> <p><strong>cell:</strong><em> </em>raster grid cell number between 1-2251762</p> <p>This object can be used in combination with a raster layer to reconstruct the modelled distribution of each species or retrieve species richness and endemism.</p>
Code and Dataset used for assessment of key variables in manuscript titled "Equity Assessment of Global Mitigation Pathways in the IPCC Sixth Assessment Report".
<p>This repository contains the code used for extraction of data from the IPCC scenarios database for key variables that are assessed in the manuscript titled "Equity Assessment of Global Mitigation Pathways in the IPCC Sixth Assessment Report". It also contains data for key variables for scenario categories C1, C2, C3, and C4</p>
Dataset from the project entitled HimFunDiff. Related to research article: Global warming alters Himalayan alpine shrub growth dynamics and climate sensitivity.
<p>We examined a total of 9 populations of Rhododendron anthopogon, which were located between 3200 m and 4200 m above sea level (asl). These populations were distributed across three geographically distant transects, with each transect consisting of three sites (along an elevation gradient). The transects are referred to as northern, intermediate, and southern, while the sites at each transect are categorized as low, mid, and high (as depicted in Thakur et al 2024). The northern transect exhibited colder temperatures and lower rainfall compared to the other two transects. On the other hand, the two remaining transects had relatively similar temperatures, but the southernmost transect received higher levels of precipitation. The mean annual temperature of these populations ranged from 2 °C to 5 °C from 2021 through 2022, while volumetric soil moisture levels varied from 0.198 to 0.377 based on onsite measurements using TMS4 dataloggers (Wild et al., 2019).</p> <p>We collected a total of 81 wood disc samples, with 9 samples obtained from each of the 9 sites studied (9 populations × 9 discs). The samples were collected by cutting a single piece from the thickest stem segment, approximately 5 cm in length, from 81 different mature and healthy individuals. Within each site, the 9 samples were obtained from three separate plots (three samples per plot), each covering an area of approximately 100 m2. The selection criteria for these plots included: (1) the presence of Rhododendron anthopogon as one of the dominant species; (2) minimal anthropogenic disturbance; and (3) the absence of large shrubs or trees. The sampled individuals within a plot were spaced at least 5 m apart from each other, and the plots themselves were at least 20 m apart. To prevent rapid drying, the cut stem samples were immediately placed in a wet paper towel. Within 48 hours of sampling, the stem samples underwent dehydration by being immersed in 50 % ethanol for the initial 3 days, followed by 70 % ethanol for the subsequent 7 to 10 days. After the ethanol dehydration process, the samples were air-dried for 72 hours and then stored in paper bags until further processing.</p> <p>Plant age and growth data for each of the sampled individuals were obtained following established protocols (Doležal et al., 2018). In the laboratory, we utilized a sledge microtome to cut cross-sections from each stem sample. These cross-sections were then stained with Astra Blue and Safranin and permanently affixed to microscope slides using Canada Balsam (Doležal et al., 2022). High-resolution images of the fixed sections were captured using an Olympus BX53 microscope equipped with an Olympus DP73 camera. The software CellSense Entry 1.9 was employed to analyse the best image obtained from each individual. We measured annual radial growth increments from pith to bark to the nearest micrometre. </p> <p>More details are given in the article entitled </p> <h1>Global warming alters Himalayan alpine shrub growth dynamics and climate sensitivity. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.170252" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.scitotenv.2024.170252</a></h1>
GLobAl building MOrphology dataset for URban climate modelling
<p>GLobAl building MOrphology dataset for URban climate modelling (GLAMOUR) offers the building footprint and height files at the resolution of 100 m in global urban centers.</p> <ul> <li>the `BH_100m` contains the building height files where each file is named as `BH_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif`.</li> <li>the `BF_100m` contains the building footprint files where each file is named as `BF_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif`.</li> </ul> <p>Here `lon_start`, `lon_end`, `lat_start`, `lat_end` denote the starting and ending positions of the longitude and latitude of target mapping areas.</p> <p>To avoid possible confusion, it should be clarified that the 'building footprint' in GLAMOUR represents the 'building surface fraction', i.e., the ratio of building plan area to total plan area.</p> <p> </p> <p>We also offer the snapshot of source code used for the generation of the GLAMOUR dataset including:</p> <ul> <li>`GC_ROI_def.py` defines regions of interest (ROI) used in the mapping of the GLAMOUR dataset.</li> <li>`GC_user_download.py` retrieves satellite images including Sentinel-1/2, NASADEM and Copernicus DEM from Google Earth Engine and exports them into Google Cloud Storage.</li> <li>`GC_master_pred.py` downloads exported data records from Google Cloud Storage and then performs the estimation of building footprint and height using Tensorflow-based models.</li> <li>`GC_postprocess.py` performs postprocessing on initial estimations by pixel masking with the World Settlement Footprint layer for 2019 (WSF2019).</li> <li>`GC_postprocess_agg.py` aggregates masked patches into larger tiles contained in the GLAMOUR dataset.</li> </ul>
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.