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7,370 results for “supplement”
Annotating Cognates in Phylogenetic Studies of South-East Asian Languages [Supplement]
<p>Source code and data accompanying the study "<strong>Annotating Cognates in Phylogenetic Studies of South-East Asian Languages" by M.-S. Wu and J.-M. List.</strong></p>
Supplemental material for "Enhanced collisionless laser absorption in strongly magnetized plasmas"
<p>This dataset constitutes supplemental material for the paper titled "Enhanced collisionless laser absorption in strongly magnetized plasmas" by Lili Manzo, Matthew R. Edwards, and Yuan Shi.</p> <p>• figure_data.zip<br> When unzipped, this folder contains subfolders fig1, fig2, …, fig10, each contains data used to generate figures 1,2, …,10 in the paper. The data files are in .txt, .mat, or .dat format, and are intended to be read by MATLAB.</p> <p>The data underlying fig1 and fig3 are generated using the Three-Wave-MATLAB code (https://gitlab.com/seanYuanSHI/three-wave-matlab).</p> <p>The data underlying fig2, fig4, and figs5-10 are raw simulation data or post-processed results of the epoch1d code (https://github.com/Warwick-Plasma/epoch).<br> <br> • figure_programs.zip<br> When unzipped, this folder contains plot_fig1.m, plot_fig2.m, …, plot_fig10.m, which are MATLAB scripts used to plot the corresponding data. Except for plot_fig2.m, which requires MATLAB version 2018 or later, all other scripts can run on MATLAB 2013 or later. The scripts plot the data but does not reproduce the formatting of the figures as shown in the paper.</p> <p>• input.deck<br> This is an example input for the epoch1d code (version 4.17.10) used to generate simulation data in the paper. </p>
Data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'
<p>This is the data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'. Both algorithm and dataset described in this publication can be found in this folder.</p> <p>Please cite 'Global dataset of thermohaline staircases obtained from Argo floats and Ice-Tethered Profilers' when using this data set (doi: 10.5194/essd-2020-197).</p> <p>The newest/most updated version of the code can be found on GitHub: https://github.com/cvanderboog/Staircase-detection-algorithm.</p> <p> </p> <p> </p> <p> </p>
Supplement to "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks"
<p>This deposit contains the scripts and data used in the research article "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks", which proposed a novel method for electricity demand forecasting in low voltage networks.</p> <p>The scripts are written in the form of R markdown and include additional commentary on the methodology. Both input data and the resulting forecast data and evaluation results are provided, though the latter two may be regenerated by running the scripts.</p>
Economic losses from hurricanes cannot be nationally offset under unabated warming - Data Supplement
<p>This data set includes the raw data for the figures of the article "Economic losses from hurricanes cannot be nationally offset under unabated warming".</p>
Hoofprints in the Sand Supplement S2: LSI Datasheet
<p>Provenience, dating, measurements (mm), and catalogue numbers for the skeletal elements used in the LSI analysis, as demonstrated in Harding, S. et al. Hoofprints in the Sand: A Metric Study of Livestock on the Southern Phoenician Coast. In preparation for <em>Quaternary International</em>.</p> <p>v.2 changed the spelling of Tel Shiqmona</p>
Corpus Criticorum (1450-1650) - Supplement 1 - A comprehensive dataset of early modern publications featuring the notion of critique on their title pages
<p>Complementing the classical bibliography of the Corpus Criticorum (1450-1650), this comprehensive dataset includes: (1) internal project identifiers; (2) URL links to source catalogues used in the survey; (3) a tabular list of the names of all official contributors (authors, editors and translators); (4) an exact transcription of the title page; (5) publication date; (6) place of publication both as it appears on the title page itself and in modernised form; (7) publishing statement as it appears on the title page; (9) book format; (10) URL links to online images of title pages or, when not available, to a library holding a copy of the text in question. </p>
Open-source DGGS comparison data supplement
<p>A DGGS is a type of spatial reference system that partitions the globe into many individual, evenly spaced, and well-aligned cells to encode location. We calculated normalized area and compactness of cell geometries for 5 open-source DGGS implementations - Uber H3, Google S2, RiskAware OpenEAGGR, rHEALPix by Landcare Research New Zealand, HEALPix by NASA Jet Propulsion Labs, and DGGRID by Southern Oregon University - to evaluate their suitability for a global-level statistical data cube.</p> <p>This repository contains all generated data and statistics.</p> <ul> <li>EAGGR doesn't seem to have a predefined logic of hierarchical cell resolutions for ISEA3H</li> <li>EAGGR doesn't seem to have a region filling algorithm available, neither for ISEA4T nor ISEA3H</li> <li>rHEALPix is pure Python (with Numpy/Scipy support), but cell generation/conversion is slower than the other C/C++ based implementations</li> <li>DGGRID is a commandline tool and can predominantly only be used to generate a grid and fill with sampling data, the Python API is only a wrapper</li> <li>healpy is a Python package to handle pixelated data on the sphere. It is based on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) scheme and bundles the HEALPix C++ library.</li> </ul> <p>Kmoch et. al (2022). Area and Shape Distortions in Open-Source Discrete Global Grid Systems. <strong><em>Big Earth Data</em></strong></p>
Supplemental Files for Eckstein and Bates et al., Cell (2024)
<p>synister_fw_mat571_t11_synapses.feather - synapse level transmitter predictions for the FAFB dataset<br>========================================================================================</p> <p>Columns:id : pre-synapse ID<br>pre : neuron ID of pre-synaptic neuron<br>post : neuron ID of post-synaptic neuron<br>pre_pt_position_{x, y, z} : coordinates of pre-synapse in nm<br>nts_11.* : predicted score for being one of the six neurotransmitter types (or "neither")</p> <p> </p> <p>hemibrain-v1.2-tbar-neurotransmitters.feather.bz2 - synapse level transmitter predictions for the hemibrain dataset<br>========================================================================================</p> <p>Columns:x, y, z : voxel coordinates of pre-synapse<br>conf : confidence value of synapse detection<br>sv : supervoxel ID of the pre-synapse<br>roi : name of brain region of pre-synapse<br>roi_label : numerical label of brain region of pre-synapse<br>body : body ID of the pre-synaptic neuron<br>nts_8.* : predicted score for being one of the six neurotransmitter types (or "neither")</p> <p> </p> <p>Neuprint_Synapses_manc_v1.ftr - synapse level transmitter predictions for the MANC dataset<br>========================================================================================</p> <p>From Takemura et al. 2023, made with the network and methods described in this repository: https://www.biorxiv.org/content/10.1101/2023.06.05.543757v1</p> <p>Columns:x, y, z : voxel coordinates of pre-synapse<br>conf : confidence value of synapse detection<br>sv : supervoxel ID of the pre-synapse<br>roi : name of brain region of pre-synapse<br>roi_label : numerical label of brain region of pre-synapse<br>body : body ID of the pre-synaptic neuron</p> <p> </p> <p>synister-master.zip - code for training and prediction with the 'synister' pipeline for neurotransmission<br>========================================================================================</p> <p>A clone of out GitHub repository at the time of paper release, found at: https://github.com/funkelab/synister, e738e22.</p> <p>Contains README with in-depth description.</p> <p> </p> <p>supplemental_data_1.csv - original ground truth data by identified cell type<br>========================================================================================</p> <p>A .csv file that provides each</p> <p>cell_type : cell type we used to generate our ground truth data,</p> <p>known_nt : the transmitter it is has been reported to express from the literature</p> <p>known_source : the study that made this report and</p> <p>evidence : the type of evidence it contained</p> <p>Note that we used this data to build our original ground truth, but have since made a wider effort to annotate known transmission for validation, reflected in Supplemental data 3 and 4, columns known_nt and known_source.</p> <p> </p> <p>supplemental_data_2.csv - original ground truth data by individual connectomic neuronal reconstruction<br>========================================================================================</p> <p>A .csv file that indicates each neuronal reconstruction used to generate our ground-truth data. Presynapses from each reconstruction were used.</p> <p>id : a unique identifier, which is the root ID for a flywire neuron, a skeleton ID for a FAFB-Catmaid neuron and a bodyid for a HemiBrain neuron,</p> <p>known_nt : the designated transmitter from the literature</p> <p>cell_type : its cell type and</p> <p>dataset : the dataset from which the neuron comes.</p> <p>FAFB-Catmaid reconstructions can be found on virtual fly brain: https://fafb.catmaid.virtualflybrain.org/. Flywire reconstructions can be found at: https://ngl.flywire.ai/. HemiBrain reconstructions can be found at: https://neuprint.janelia.org/?dataset=hemibrain. We used version 630 in this study.</p> <p> </p> <p>supplemental_data_3.csv - <br>========================================================================================</p> <p>A .csv file in which each row is a single identified HemiBrain reconstruction. Columns provide:</p> <p>bodyid : each neuron’s unique identifier </p> <p>cell_type : morphological cell type</p> <p>pre : number of output synapses</p> <p>cropped : whether or not the neuronal reconstruction is cut off by the HemiBrain volume</p> <p>conf_nt : neuron-level transmitter prediction</p> <p>conf_nt_p : the confidence score for that prediction, which was calculated using the synapse-level confusion metrics and with synapse filtering</p> <p>top_nt, top_nt_p : neuron-level transmitter prediction with no synapse filtering for axon/dendrite</p> <p>{transmitter name} : the proportion of synapse-level transmitter predictions that “voted” for each transmitter </p> <p>segregation_index : the segregation index, a measure of neuronal polarity</p> <p>projection_score : the projection score, a measure of the distance between axon and dendrite</p> <p>ito_lee_hemilineage, hartenstein_hemilineage : the hemilineage to which this neuron belongs</p> <p>in_ground_truth : whether or not presynapses from this neuron were included in our ground-truth data</p> <p> </p> <p>supplemental_data_4.csv - <br>========================================================================================</p> <p>A .csv file where each row is a single identified FAFB-FlyWire reconstruction. Columns provide:</p> <p>root_id_630 : each neuron’s unique identifier from the 630 materialization of the FAFB-FlyWire dataset used in this paper</p> <p>root_id_783 : each neuron’s unique identifier from the new 783 materialization of the FAFB-FlyWire dataset used in this paper</p> <p>cell_type : morphological cell type</p> <p>conf_nt : neuron-level transmitter prediction</p> <p>conf_nt_p : the confidence score for that prediction, which was calculated using the synapse-level confusion metrics and with synapse filtering</p> <p>top_nt, top_nt_p : neuron-level transmitter prediction with no synapse filtering for axon/dendrite</p> <p>{transmitter name} : the proportion of synapse-level transmitter predictions that “voted” for each transmitter </p> <p>segregation_index : the segregation index, a measure of neuronal polarity</p> <p>projection_score : the projection score, a measure of the distance between axon and dendrite</p> <p>ito_lee_hemilineage, hartenstein_hemilineage : the hemilineage to which this neuron belongs</p> <p>side, morphology group, flow, cell class, cell sub class : other metadata from Schlegel et al. 2023, https://zenodo.org/records/8077335</p> <p>in_ground_truth : whether or not presynapses from this neuron were included in our ground-truth data</p> <p>notes : extra notes on the cell type designation, including alternate names</p> <p>pos_x, pos_y, pos_z : because the root id for neurons is changing as neurons are edited in an active connectome project we also supply the position of a point in the neuron to help identify it in FAFB-FlyWire voxel space</p> <p>nucleus_id : an ID for the nucleus segmentation</p> <p> </p> <p>supplemental_data_5.zip - <br>========================================================================================</p> <p>A .zip archive containing .png files depicting each of the 183 brain hemilineages we have used from the FAFB-FlyWire dataset. Neurons in each hemilineage are colored by their neuron-level transmitter predictions, hemilineage names given in the file name. Hemilineage labels for the FAFB-FlyWire dataset are fully reported in Schlegel et al. 2023, https://zenodo.org/records/8077335.</p> <p> </p> <p>supplemental_data_6.csv - <br>========================================================================================</p> <p>A .csv containing summary results for our 183 central brain secondary hemilineages. Columns provide:</p> <p>ito_lee_hemilineage : each hemilineage’s name </p> <p>hartenstein_hemilineage : and lineage-associated tract</p> <p>left, right, center : the number of neurons on the left and right hemispheres (left, right, center),</p> <p>lineage_type : the lineage type i.e. Type I or II</p> <p>{transmitter} : the number of neurons predicted for each transmitter</p> <p>majority_nt : and the majority vote of the hemilineage.</p> <p>Note, the same information is given for HemiBrain hemilineages, with ‘hemibrain ’ added to the column name.</p> <p> </p> <p>supplemental_data_7.csv - <br>========================================================================================</p> <p>A .csv in which each row is a described cell type of the fly brain. Cell type annotations were drawn from Schlegel et al. 2023, neurons with no cell type or hemibrain type annotated are not included. Columns provide:</p> <p>cell_type : each cell type’s name</p> <p>cell_type_nt : predicted transmitter for the cell type (cell type nt)</p> <p>cell_type_confidence : the cell type level transmitter confidence score</p> <p>{transmitter}_{dataset}_confidence : the sum of neuronlevel transmitter predictions across all FAFB-FlyWire reconstructions for each transmitter</p> <p>{transmitter}_{dataset }_n : the number of neurons with that confidence,</p> <p>known_nt : the transmitters reported to be used by the cell type in the literature</p> <p>known_nt_source : the citations for that information </p> <p>ito_lee_hemilineage, hartenstein_hemilineage, morphology_group, flow, cell_class, cell_sub_class : morphological annotations from Schlegel et al. 2023</p> <p>cell_type_nt : the cell type level transmitter is determined by the highest {transmitter} cell type {dataset} confidence</p> <p>cell_type_nt_conf : The cell type level transmitter confidence score (cell type nt) was calculated using our prediction confusion matrices.</p> <p>Results for both FAFB-FlyWire (‘ fafb’) and HemiBrain (‘hemibrain’) datasets are given.</p> <p> </p> <p>supplemental_data_8.tar.gz<br>========================================================================================</p> <p>Contains PNG files of 2D images cropped around synapses, together with their<br>counterfactual translations into different neurotransmitter types and<br>highlights of the most important areas for classification.There is one PNG file for each pair of neurotransmitters, named<br>`{source}_{target}.png`. E.g., the file `ach_dop.png` contains translations of<br>real cholinergic synapses into counterfactual dopaminergic synapses.Each PNG shows images in six columns, which are:1: original raw + classifier scores<br>2: counterfactual raw + classifier scores<br>3: counterfactual raw, with original raw in mask + classifier scores<br>4: mask, shown on original + relative classifier change<br>5: mask, shown on counterfactual<br>6: difference in mask area</p> <p>Paper: https://www.cell.com/cell/fulltext/S0092-8674(24)00307-6</p>
PhysiCell Studio: a graphical tool to make agent-based modeling more accessible. Supplemental material.
<p>Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification, i.e., a markup language, that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a graphical user interface that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open source software and contributions from the community are encouraged. This dataset provides Supplemental material for the PhysiCell Studio publication.</p>
Supplement - Structure from Motion Raster Data
<p>We created orthorectified images and digital elevation models using Agisoft Metashape, a photogrammetric processing software application that uses SfM. We followed the workflow outlined in Bywater-Reyes and Pratt-Sitaula (2022). Once processed, orthorectified imagery and Digital Elevation Models (DEMs) were exported to ArcGIS Pro for additional analysis. Data collection metadata and postprocessing outcomes can be found in this repository. </p>
Dataset for the comparison of performance of two leak detectors using hydrogen reference leaks (supplement to paper "Advancing Hydrogen Leak Detection: Design and Calibration of Reference Leaks")
<p>Excel file containing some measurements made in December 2023, using three hydrogen reference leaks, to assess the performance of two distinct leak detectors, one portable and made specifically for hydrogen and one MSLD in hydrogen-mode.</p>
Supplemental data for "Inequitable spatial and temporal patterns in the distribution of multiple environmental risks and benefits in Metro Vancouver"
<p><strong>DemoEnPoC2016.csv/DemoEnPoC2006.csv:</strong></p> <p>This is a table including environmental and demographic (Census variables) data at postal code level for Metro Vancouver in the year 2006 and 2016. The environmental data (SO2 metrics, PM2.5 metrics, Calculated ozone metrics, NO2 data, NDVI metrics, and Canadian Active Living Environments Index (Can-ALE) indexed to DMTI Spatial Inc. postal codes) were extracted from CANUE (Canadian Urban Environmental Health Research Consortium). The demographic data is extracted from Canadian Census analyzer (https://datacentre.chass.utoronto.ca/), the deprivation index is downloaded from from the Institut national de santé publique du Québec (INSPQ). </p> <p><strong>DGRwithLable:</strong></p> <p>This is the Dissemination Geographies Relationship File for the 2021 census year (Statistics Canada, 2021) with the lable of urban or rural, indicating which dissemination area (DA) is identified as urban and included in this study. The urban area is named as population certer. </p> <p><strong>Aggregation and SS Determination:</strong></p> <p>This script contains code for:</p> <ul> <li>Aggregating postal code level data to the Dissemination Area (DA) level.</li> <li>Eliminating rural DAs.</li> <li>Converting environmental data into ordinal categories using quartile and even break methods.</li> <li>Identifying sweet and sour spots for each DA based on these methods.</li> </ul> <p><strong>SSEJ Analysis:</strong></p> <p>This script includes code for:</p> <ul> <li>Creating violin and box plots to illustrate descriptive statistics of demographic groups across different environmental categories (sweet, sour, risky, and medium).</li> <li>Performing linear regression analyses between environmental categories and demographic variables.</li> </ul> <p><strong>SS Heatmap:</strong></p> <p>This script comprises code for:</p> <ul> <li>Summarizing the results of the linear regression analyses.</li> <li>Assessing changes in inequities among demographic groups between 2006 and 2016.</li> <li>Visualizing regression coefficients through heatmaps.</li> </ul> <p> </p>
Supplemental material for the manuscript "Extreme genome scrambling in marine planktonic Oikopleura dioica cryptic species".
<p><strong>Supplementary material for the manuscript “Extreme genome scrambling in marine planktonic <em>Oikopleura dioica</em> cryptic species”.<br></strong></p> <p><strong><em>BreakpointsData.tar.xz contains:</em></strong></p> <ul> <li>Pairwise genome alignment files for <em>Oikopleura</em>, <em>Ciona</em>, <em>Caenorhabditis</em>, insects and muntjaks in GFF format in `inst/extdata/`.</li> <li>dN / dS computation results in `inst/extdata/dNdS/`.</li> <li>Annotations of gene models and repeat elements in GFF format in `inst/extdata/Annotations/`.</li> <li>OrthoGroups in `inst/extdata/OrthoFinder/`, where N19 represents the _O. dioica_ clade,</li> <li>N3 the tunicates and N20 the _Ciona_ clade.</li> <li>`BreakpointsData_3.11.0.tar.gz`, a R package installing the above files in the R environments where we ran our computations.</li> <li>The files needed to build the `BreakpointsData` package.</li> </ul> <p><em><strong>Oidioi_pairwise_v3.tar.gz contains:</strong></em></p> <ul> <li>The pairwise alignment files between genomes, in MAF format.</li> <li>A copy of the Nextflow pipeline used to generate them.</li> </ul> <p><em><strong>oist-assembler.tar.gz contains:</strong></em></p> <ul> <li>A Singularity image and its definition file for flye version 2.8.3-b1763` Flye-flye.2.8.3-b1763.sif` and `Flye-flye.def`.</li> <li>A copy of the Nextflow pipeline used to assemble the Bar2_p4 genome in `oist-assembler-Bar2_p4`.</li> <li>A copy of the Nextflow pipeline used to assemble the other genome in `oist-assembler-other_genomes`.</li> </ul> <p><em>Please note that these files are provided for reproducibility only and probably can not be used easily for other purposes.</em></p> <p><em><strong>Oidioi_genomes.tar.gz contains:</strong></em></p> <ul> <li>For each genome, one file (`<genome>.fa`) containing the whole genome sequence and one directory (`<genome>`) containing each chromosome, scaffold or contig of the genome as a separate file.</li> <li>For each genome, one R package, its source directory, and the vignette to create it, providing the genome information as a `BSgenome` object.</li> </ul> <p><em><strong>OrthoFinderRun.tar.xz contains:</strong></em></p> <ul> <li>A full copy of the OrthoFinder2 run that we used to compute hierarchical orthogroups.</li> </ul> <p><em><strong>Supplemental_Code.tar.gz contains:</strong></em></p> <ul> <li>A copy of <https://github.com/oist/LuscombeU_OikScrambling>, where the `.git` and `doc` directories were removed to save space.</li> </ul> <p><em><strong>AugustusAnnotation.tar.gz (added July 26th 2024) contains:</strong></em></p> <ul> <li>AUGUSTUS runs to produce the annotations that were input to OrthoFinder2. We provide them for reproducibility, with no guarantee that they are suitable for other purposes. The annotations used in the manuscript are AOM-5-5f.sm.OSKA-CDS, Bar2_p4_Flye.sm, Bsty_SCLE01.1.sm.abi.cionamodel, Fbor_SDII01.1.sm.abi, KUM-M3-7f.sm.OKI-CDS, Mery_SCLF01.1.sm.abi.cionamodel, Oalb_SCLG01.1.sm.abi.cionamodel, OKI2018_I69_annotv2.sm, Olon_SCLD01.1.sm.abi, OSKA2016v1.9.sm and Ovan_SCLH01.1.sm.abi.cionamodel.</li> </ul>
Identification and Functional Characterization of an Alternative Cancer-derived PD-L1 Isoform (supplemental data)
<p>The enclosed files contain all of the supplemental data from: Identification and Functional Characterization of an Alternative Cancer-derived PD-L1 Isoform. The files include the complete tables in CSV-formatted files.</p>
Data and analysis supplement for: Functional imagery training versus motivational interviewing for weight loss: a randomised controlled trial of brief individual interventions for overweight and obesity.
<p>This submission provides the data and code for analyses reported in our publication.</p>
Supplemental data for "The possible transition from glacial surge to ice stream on Vavilov Ice Cap"
<p>Data presented in the paper "The possible transition from glacial surge to ice stream on Vavilov Ice Cap".</p>
Supplemental Data for "Stability of fractional Chern insulators with a non-Landau level continuum limit"
<p>Scripts and data to supplement the paper "Stability of fractional Chern insulators with a non-Landau level continuum limit".</p>
Supplemental data for: Mapping lifestyle factors in blood glucose variability in adolescents with Type 1 Diabetes Mellitus- A pilot study
<div> <p>The dataset was used in the paper “Mapping lifestyle factors in blood glucose variability in adolescents with Type 1 Diabetes Mellitus- A pilot study”. The article is currently under review for publication. DOI to be inserted.</p> </div> <div> <p>A data-in-brief article is to be published to give in-depth information about the data collected to improve reproducibility "Dataset for: Lifestyle Factors and Blood Glucose Variability in Adolescents with Type 1 Diabetes Mellitus". DOI to be inserted. </p> <p> </p> <p>The aim of the study was to assess whether adolescents with T1D in Ireland meet current nutrition and physical activity (PA) guidelines and to explore the impact of nutrition and PA on glycaemic variability (GV). The dataset includes continuous glucose monitoring (CGM) data, dietary intake records, and PA metrics, providing a comprehensive view of the participants' glucose levels and associated lifestyle behaviours.</p> </div>
Supplemental to: Physical and mechanical depth relationships of rocks from the Rotokawa Geothermal Reservoir, Taupō Volcanic Zone, New Zealand
<p>This contains supplementary informations for the Manuscript </p> <p>Physical and mechanical depth relationships of rocks from the Rotokawa Geothermal Reservoir, TaupōVolcanic Zone, New Zealand</p> <p>submitted for review at the New Zealand Journal of Geology and Geophysics</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.