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476 results for “footprints”
Annual terrestrial Human Footprint dataset from 1982 to 2000
<p><a href="https://www.nature.com/articles/s41597-022-01284-8">Human footprint dataset</a> extrapolated to past periods 1982--2000. For each pixel we fit a logit-model and then extrapolate it to past years to produce assumed Human footprint prior to year 2000. This assumes simple linear trends in Human footprint.</p>
Global consensus map of human transcription factor footprints
<p>Vierstra, J. <em>et al.</em> <strong>Global reference mapping of human transcription factor footprints.</strong> <em>Nature</em><strong> </strong>583, 729–736 (2020). <a href="https://doi.org/10.1038/s41586-020-2528-x">https://doi.org/10.1038/s41586-020-2528-x</a></p> <p>Preprint @ bioRxiv: <a href="https://doi.org/10.1101/2020.01.31.927798">https://doi.org/10.1101/2020.01.31.927798</a></p> <p><strong>Contact:</strong> Jeff Vierstra (<a href="mailto:jvierstra@altius.org?subject=Consensus%20DNase%20I%20footprints">jvierstra@altius.org</a>)</p> <p>Genomic DNase I footprinting enables quantitative, nucleotide-resolution delineation of sites of transcription factor occupancy within native chromatin. We combined sampling of >67 billion uniquely mapping DNase I cleavages from >240 human cell types and states to index, with unprecedented accuracy and resolution, human genomic footprints and thereby the sequence elements that encode transcription factor recognition sites.</p> <p>Please see <a href="http://vierstra.org/resources/dgf">http://vierstra.org/resources/dgf </a>for additional information and a complete set of raw DNase I data for individual datasets. Additionally, raw data can also be accessed via the ENCODE data portal (<a href="http://encodeproject.org">http://encodeproject.org</a>) using the dataset accessions found in Supplementary Table 1.</p> <p>Code for footprint analysis and tutorials on how to access and manipulate digital genomic footprint data can be found at <a href="https://footprint-tools.readthedocs.io/en/latest/">https://footprint-tools.readthedocs.io/en/latest/</a>.</p> <p>All files herein correspond to human genome build version GRCh38 (UCSC hg38).</p> <p><strong>Dataset contents:</strong></p> <ul> <li><strong>Biosample metadata</strong> – Supplementary_Table_1.xlsx</li> <li><strong>Motif clustering metadata </strong>– Supplementary_Table_2.xlsx</li> <li><strong>ChIP-seq validation metadata </strong>–<strong> </strong>Supplementary_Table_3.xlsx</li> <li><strong>Consensus footprint coordinates and assigned motif archetypes</strong><br> TSV file (BED-format) with consensus footprint (posterior probability>0.99) coordinates and overlaps with matches to motif model clusters. The legend file contains column definitions in detail. <ul> <li>consensus_footprints_and_motifs_hg38.bed.gz</li> <li>consensus_footprints_and_motifs_legend.txt</li> </ul> </li> <li><strong>Motif archetype matches overlapping consensus footprints</strong><br> TSV file (BED-format) containing the coordinates for clustered motif model matches that overlap consensus footprints <ul> <li>collapsed_motifs_overlaping_consensus_footprints.bed.gz</li> <li>collapsed_motifs_overlaping_consensus_footprints_legend.txt</li> </ul> </li> <li><strong>Footprint occupancy matrix of consensus footprints</strong><br> Rows are same order as the consensus footprint file and columns are same order as in the metadata files. <ul> <li>consensus_index_matrix_full_hg38.txt.gz (Values are –log(1-posterior))</li> <li>consensus_index_matrix_binary_hg38.txt.gz (binary occupancy matrix, where footprints with posterior footprint probability >0.99 are considered occupied)</li> </ul> </li> <li><strong>Single nucleotide variants tested for allelic imbalance </strong><br> The legend file contains column definitions in detail. <ul> <li>genotypes.vcf.gz - Genotyping and allelic read depth for each biosample (see header for more information)</li> <li>tested_snvs_padj.bed.gz - SNVs tested for imbalance (TSV, BED-format)</li> <li>tested_snvs_padj_legend.txt</li> </ul> </li> </ul>
Data for "Rapid seaward expansion of seaport footprints worldwide"
<p>[updated November 2023]</p><p>This dataset comprises data and code used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta & Lazarus, 2023; <a href="https://doi.org/10.1038/s43247-023-01110-y">https://doi.org/10.1038/s43247-023-01110-y</a>).</p><p>This repository includes three .csv files, one .xlsx file, and a .ipynb file:</p><p><strong>'Sengupta_Lazarus_REC_1990_2020_v05.csv' </strong>– annual time series of seaward expansion (km^2) between 1990–2020 through coastal reclamation for 65 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>). Dataset includes Year, Seaport, Country, Region, and Reclaimed area (km^2) [raw measurement]. </p><p><strong>'Sengupta_Lazarus_REC_TEU_2011_2020_v03.csv'</strong> – annual time series of seaward expansion (km^2) and reported container throughput (millions TEU) between 2011–2020 for 43 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and TEU (millions), collated from archived Lloyd's List reports.</p><p><strong>'Sengupta_Lazarus_REC_TEU_totals_v04.csv'</strong> – Simplified dataset listing total seaward expansion (km^2) and container throughput in 2020 for 65 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>). Dataset includes Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], ranked list of seaports by expansion extent, and TEU (millions) handled in 2020, and Lloyd's List rank in 2020 (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>).</p><p><strong>'Sengupta_Lazarus_2023_ports_excluded_v2.xlsx'</strong> – contains list of 35 ports excluded from thus analysis because they are either not on an open coastline (e.g. estuarine, riverine) or expanded less than 1 km^2 seaward between 1990–2020.</p><p><strong>'RECLAIM_port_trajectories_v11.ipynb' </strong>– Jupyter notebook for data wrangling and plotting figures presented in Sengupta & Lazarus (2023). (Note that this notebook does not produce the map-based figures presented in that work.)</p><p>The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (<a href="https://doi.org/10.1029/2022EF002927">2023</a>), and the GEE code is available here: <a href="https://github.com/dhritirajsen/Mapping_Coastal_land_reclamation">https://github.com/dhritirajsen/Seaport_reclamation</a></p><p>These data and code are also available here: <a href="https://github.com/edlazarus/Seaports">https://github.com/edlazarus/Seaports</a></p>
Building footprints Oldenburg derived from aerial imagery
<p>This data set contains about 78000 georeferenced polygons representing all building footprints within the administrative boundaries of the city of Oldenburg, Lower Saxony, Germany. These geometries were created by a deep learning-based image segmentation. The model for this was trained at the State Office of Lower Saxony for Geoinformation and Surveying (LGLN).</p> <p>We publish this data under the CC0 license. <br>You can do whatever you want with it. There are no restrictions.<br><br>If you do something great with the data set, we'd love to hear about it: <a href="mailto:ki-gebaeudeerkennung@geolabs.atlassian.net">ki-gebaeudeerkennung@geolabs.atlassian.net</a><br>If you use this dataset, you are welcome to reference it - but you don't have to.<br>Would you like builiding footprints for another area? We'd love to hear about it.</p>
COMPAIR carbon footprint calculations and greenhouse gas emissions reduction scenarios
<p>Citizens' carbon footprint calculation results and citizen-created scenarios on how Greenhouse Gas emissions can be reduced by 55% by 2030 are available that were gathered as part of the <a href="https://cordis.europa.eu/project/id/101036563">EU Horizon2020 COMPAIR project</a> in Europe. The pilot cities/regions are Berlin, Athens, Sofia, Plovdiv, and Flanders.</p>
MHD Model of Ganymede's Magnetosphere: Predicted OCFB and magnetic footprint surface locations for Juno's flyby
<p>This dataset contains model results from a magnetohydrodynamic (MHD) model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021. Here we publish coordinates for the predicted location of the open-closed-field line-boundary (OCFB) on Ganymede's surface. Additionally we provide coordinates of Juno's magnetic footprint, namely the surface locations that connect to Juno's trajectory through magnetic field lines.</p> <p>For the surface locations we use a western longitude planetographic coordinate system where 0° longitude is in direction of the y-axis and 90° in direction of the x-axis of the cartesian GPhiO system. The GPhiO system is defined by the primary direction<br> z parallel to Jupiter’s rotation axis, the secondary direction y is pointing towards Jupiter barycenter<br> and x completes the right-handed system approximately in direction of plasma flow.</p> <p><strong>Duling2022_JunoGanymede_modeled_surface_OCFB.txt</strong></p> <p>Columns:</p> <p>Longitude [°]<br> Northern OCFB latitude [°]<br> Southern OCFB latitude [°]</p> <p><strong>Duling2022_JunoGanymede_modeled_magnetic_footprint.txt</strong></p> <p>Columns:</p> <p>Spacecraft time [UTC]<br> Magnetic footprint longitude [°]<br> Magnetic footprint latitude [°]<br> Length of field line between Juno and surface [radii]<br> Length of field line between Juno and surface [km]<br> r coordinate of Juno [radii]<br> Latitude of Juno [°]<br> Longitude of Juno [°]<br> x of Juno in GPhiO [km]<br> y of Juno in GPhiO [km]<br> z of Juno in GPhiO [km]</p> <p><strong>Duling2022_JunoGanymede_surface_map.png</strong></p> <p>A plot that visualizes the data of this repository.</p>
[review paper] Sustaining the 'Frozen Footprints' of Scholarly Communication through Open Citations_Dataset
<p>This dataset belongs to the review titled <em>Sustaining the ‘Frozen Footprints’ of Scholarly Communication through Open Citations</em>. The review explores the developments in the open citations movement, the OpenCitations infrastructure, and the Initiative for Open Citations (I4OC), providing a comprehensive overview of key milestones and initiatives.</p> <p>The dataset includes bibliographic and citation data for 174 scholarly outputs and 149 blogposts analyzed in the review. These outputs were drawn from a range of sources, including journal articles, conference proceedings, and other scholarly materials. The data has been curated to adhere to open citation principles, ensuring it is structured, separable, and openly accessible. It is provided in accordance with the licenses and terms of use of the original databases.</p> <p>Researchers, practitioners, and policymakers can use this dataset to explore the evolution of open citations and to further their understanding of the connections between scholarly works in the field of open research.</p>
Dataset for Reaction-Induced Formation of Stable Mononuclear Cu(I)Cl Species on Carbon for Low-Footprint Vinyl Chloride Production
<p>This dataset complements the publication entitled "Reaction-Induced Formation of Stable Mononuclear Cu(I)Cl Species on Carbon for Low-Footprint Vinyl Chloride Production" by Dario Faust Akl, Georgios Giannakakis, Andrea Ruiz-Ferrando, Mikhail Agrachev, Juan D. Medrano-García, Gonzalo Guillén-Gosálbez, Gunnar Jeschke, Adam H. Clark, Olga V. Safonova, Sharon Mitchell, Núria López, Javier Pérez-Ramírez.</p>
Imaging the footprint of nanoscale electrochemical reactions for assessing synergistic hydrogen evolution
<p>Dataset complementary to supporting information, such as optical movies, COMSOL model, and Python codes to analyze the experimental and simulated data according to the manuscript submitted for publication.<br> The movies correspond to cyclic voltammetry operando monitoring by optical microscopy of the reduction of water + KCl in the presence of NiCl2 at an ITO electrode or NiCl2 or MgCl2 at ITO electrode coated with Pt nanoparticles.</p> <p>The python function was used to extract the halo size around each nanoparticle from optical images, the python routines were used to postprocess the COMSOL simulation and evalaute the simulated halo size.</p>
Darwin Core Archive: Santa Barbara Channel fish surveys at deep reefs: Footprint, Piggy Bank, Anacapa Passage
The dataset contains fish surveys from deep natural reefs in the northern Santa Barbara Channel Islands, Southern California, mainly at reefs named Piggy Bank, Footprint (local names) and Anacapa Passage. Data collection began in 1995. Reefs are located at depths between 30 and 360 m (100 and 1,180 feet). Sampling was by the manned submersibles Delta and DualDeepworker and an unmanned Remotely Operated Vehicle (ROV). These sites included a wide range of such habitats as banks, ridges, and carbonate reefs, ranging in size from a few kilometers in length to less than a hectare in area. On these features, we focused on hard bottom macrohabitats, including kelp beds, boulder and cobble fields, and bedrock outcrops. Transects were not deliberately revisited; some reefs were surveyed as many as four times per year. All transects are 2 m wide; transect length varied (see data). Fishes were identified to lowest possible taxon (usually species), and verified against the WoRMs database (http://www.marinespecies.org/). This dataset is formatted as a Darwin Core Archive (DwC-A, occurrence core). This is a derived data product and see provenance for the source data.
Data for - The environmental footprint of transport by car using renewable energy
<p>Replacing fossil fuels in the transport sector by renewable energy will help combat climate change. However, lowering greenhouse gas emissions by switching to alternative fuels or electricity can come at the expense of land and water resources. To understand the scale of this possible tradeoff we compare and contrast carbon, land and water footprints per driven km in midsize cars utilizing conventional gasoline, biofuels, bioelectricity, solar electricity and solar-based hydrogen. Results show that solar-powered electric cars have the smallest environmental footprints per km, followed by solar-based hydrogen cars, and that biofuel-driven cars have the largest footprints.</p>
Supplementary Dataset for "Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites"
<p>These datasets are supplementary to the paper "<strong>Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites</strong>" by Chu et al. </p> <ul> <li>Dataset S1. Summary of site-specific footprint metrics <ul> <li>filename: All_site_fpt_summary.csv</li> <li>readme: All_site_fpt_summary-README.csv</li> </ul> </li> <li>Dataset S2. All monthly footprint climatology weight maps <ul> <li>filename: monthly_footprint_climatology_weight_map.zip <ul> <li>the zip folder contains individual files of all monthly footprint weight maps</li> <li>filename: <Site-ID>_<Year>_<Month>_<DAY/NIGHT>_fpt_weight.tif</li> </ul> </li> <li>readme: README.txt </li> </ul> </li> <li>Dataset S3. All site-year footprint climatology overlapped with true-color satellite images. <ul> <li>filename: site-year_footprint_climatology_realcolor_map.zip <ul> <li>the zip folder contains individual files of footprint climatologies from all site-years</li> <li>filename: <Site-ID>_<Year>_<Spatial_Extent>_shrink_footprint_climatology.png</li> </ul> </li> <li>readme: README.txt </li> </ul> </li> <li>Dataset S4. Site-specific results and representativeness index based on the land cover type analysis. <ul> <li>filename: All_site_land_cover_dominant_summary2.csv</li> <li>readme:All_site_land_cover_dominant_summary2-README.csv</li> </ul> </li> <li>Dataset S5. Site-specific results and representativeness index based on the EVI analysis. <ul> <li>filename: All_site_Landsat_EVI_fpt_comparison2.csv</li> <li>readme: All_site_Landsat_EVI_fpt_comparison2-README.csv</li> </ul> </li> <li>Dataset S6. All available site-month EVI and time-explicit representativeness. <ul> <li>filename: All_site_Landsat_EVI_all_cutout2.csv</li> <li>readme: All_site_Landsat_EVI_all_cutout2-README.csv</li> </ul> </li> </ul>
Estimating the carbon footprint of citizen science biodiversity monitoring
<p>Datasets used in the production of the paper Gillings, S. & Harris, S.J. 2022. Estimating the carbon footprint of citizen science biodiversity monitoring. People & Nature.</p> <p>The dataset comprises a) the estimated round-trip distances from approximate locations of observers to survey locations for the UK Breeding Bird Survey and b) questionnaire responses concerning mode of travel used to access survey locations. Data have been anonymised and locations have been coarsened to preserve anonymity.</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p> <p> </p> <p> </p>
How much data about the data centres' energy footprint is currently available globally?
<p><strong>By scraping the open database "Datacenter.rs", the candidate analysed and mapped more than 6.000 data centres worldwide on April 9, 2022.</strong></p>
How do Google News' top 100 sources visually represent the data centres' energy footprint?
<p><strong>By querying "data centres' energy footprint" on Google News in incognito mode, the candidate has selected and mapped the top 100 results according to the ranking on May 15, 2022. </strong></p>
IN01004 Podagadh Foundation of Visnu Footprint of Skandavarman. Sanskrit XML file
<p>IN01004 Poḍāgaḍh Foundation of Viṣṇu Footprint of Skandavarman. Sanskrit XML file (without metadata).</p>
Dataset associated with Schyns & Vanham (2019) "The water footprint of wood for energy consumed in the European Union"
<p>Input and output datasets related to the paper Schyns & Vanham (2019) The water footprint of wood for energy consumed in the European Union. <em>Water</em>, 11(2): 206.</p>
Global mining deforestation footprint data from 2000 to 2019
<p>The data in this repository is available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/<br><br>This repository includes two datasets. The first is a collection of polygons covering mines globally and the associated forest cover loss from 2000 to 2019. The polygons were derived by merging the "global-scale mining polygons version 2" (Maus et al., 2022) and mining and quarry polygon features extracted from the OpenStreetMap database (OpenStreetMap contributors, 2017). To remove double counting of areas the overlaps between the datasets were resolved by uniting intersecting features into single polygon features, i.e. keeping only the external borders of intersecting features. A random visual check was conducted, and a few small manual editing of polygons was performed where errors were identified.</p> <p>The resulting dataset is encoded as a Geopackage in the file 'global_mining_polygons.gpkg'. The GeoPackage includes a single layer with 192,584 entries called 'mining_polygons' with the following attributes:</p> <ul> <li><em>id<string></em> unique feature identifier</li> <li><em>isoa3<string></em> ISO 3166-1 alpha-3 country codes</li> <li><em>country<string></em> country names</li> <li><em>area<double></em> area of the polygon in squared kilometres</li> <li><em>geom<polygon></em> the geometry of the features in geographical coordinates WGS84</li> </ul> <p>The second dataset provides annual time series of global tree cover loss within mines from 2000 to 2019, covering all polygons in the above dataset. The area of tree cover loss for each polygon was calculated from the Global Forest Change database (Hansen et al., 2013). Each polygon also has additional string attributes with biomes derived from <em>Ecoregions 2017 <sup>© Resolve </sup></em>(Dinerstein et al., 2017) and the level of protection derived from The World Database on Protected Areas (UNEP-WCMC and IUCN, 2022).</p> <p>This dataset is encoded in CSV format in the file 'global_mining_forest_loss.csv', which includes 416,412 entries and 53 variables, such that:</p> <ul> <li><em>id<string></em> unique feature identifier</li> <li><em>id_hcluster<string></em> unique feature identifier</li> <li><em>list_of_commodities<string></em> a comma-separated list of commodities</li> <li><em>isoa3<string></em> ISO 3166-1 alpha-3 country codes</li> <li><em>country<string></em> country names</li> <li>ecoregion<em><string></em> ecoregion name</li> <li><em>biome<string></em> biome name</li> <li><em>year<numeric></em> the year</li> <li>area_forest_loss_XXX_YYY<double> the area of forest cover loss within a polygon per year in squared kilometres.</li> </ul> <p>The values of tree cover loss are disaggregated per initial percentage of tree cover (XXX) and per protection level (YYY).</p> <ul> <li>XXX can take one of: <ul> <li>000: total tree cover loss independently from the initial tree cover</li> <li>025: tree cover loss on pixels with initial tree cover between 0 and 25%</li> <li>050: tree cover loss on pixels with initial tree cover between 25 and 50%</li> <li>075: tree cover loss on pixels with initial tree cover between 50 and 75%</li> <li>100: tree cover loss on pixels with initial tree cover between 75 and 100%</li> </ul> </li> <li>YYY can take one of: <ul> <li>la: tree cover loss within strict nature reserve</li> <li>Ib: tree cover loss within wilderness area</li> <li>II: tree cover loss within national park</li> <li>III: tree cover loss within natural monument or feature</li> <li>IV: tree cover loss within habitat/species management area</li> <li>V: tree cover loss within protected landscape/seascape</li> <li>VI: tree cover loss within PA with sustainable use of natural resources</li> <li>p: tree cover loss within any type of protection, including not applicable, not assigned, or not reported</li> <li>none: when YYY is omitted, total tree cover loss within the polygon</li> </ul> </li> </ul> <p>For details about the protection levels definition see the UNEP-WCMC and IUCN (2022). The <em>id</em> can be used to link polygons to forest loss data.</p>
Rutor glacier fronts footprints
<p>Footprints of the various glacial fronts obtained from the elaborated cartographic products: orthophoto from August 2017 satellite imagery, September 2020 aerial orthophoto, July 2021 UAV orthophoto, and September 2021 aerial orthophoto.</p>
Multi-scale footprinting
<p>Data associated with the multi-scale footprinting project.</p> <p>(1) <strong>Tn5_NN_model.h5</strong></p> <p>Pre-trained CNN-based Tn5 bias model implemented with Keras. Takes local DNA sequence context as input and predicts Tn5 insertion bias. See tutorial for how to use this model.</p> <p>(2) <strong>Tn5ModelTutorial.ipynb</strong></p> <p>Tutorial showing how to use the pre-trained Tn5 bias model to score input sequences.</p> <p>(3) <strong>hg38Tn5Bias.tar.gz, hg19Tn5Bias.tar.gz, mm10Tn5Bias.tar.gz, mm39_bias_v2.h5, panTro6Tn5Bias.tar.gz, sacCer3Tn5Bias.tar.gz, dm6Tn5Bias.tar.gz, danRer11Tn5Bias.tar.gz, ce11Tn5Bias.tar.gz</strong></p> <p>h5 files containing the genome-wide Tn5 bias pre-computed using our convolutional neural net model.</p> <p>(4) <strong>dispModel.tar.gz</strong></p> <p>Zipped folder containing Tn5 cutting dispersion models for each footprint window radius. The footprint window size in our paper refers to the diameter the footprint window, which is twice the number listed here. During footprinting, these models are loaded into the footprintingProject object and then used for footprinting.</p> <p>(5) <strong>cisBP_mouse_pwms_2021.rds, cisBP_human_pwms_2021.rds</strong></p> <p>Motif PWMs used in our study.</p> <p>(6) <strong>TFBS_model.h5</strong></p> <p>Pre-trained footprint-to-TF binding prediction models. The models takes local multi-scale footprints as input and predict whether a genomic position is bound by a TF if the corresponding motif is present. This is obsolete. For the best performance of TF binding prediction, please use our seq2PRINT-based TF binding prediction. </p> <p>(7) <strong>clusterLabels.txt, clusterLabelsAllTFs.txt</strong></p> <p>Cluster labels of TFs. clusterLabels.txt is the clustering result directly obtained from clustering multi-scale footprints of all TFs with ChIP data. clusterLabelsAllTFs.txt includes other TFs without ChIP data. The cluster membership of these TFs were assigned based on motif homology among TFs.</p> <p>(8) <strong>BMMCTutorial.tar.gz</strong></p> <p>Data needed for our R version tutorial. Content of this foder can be put into the /data/BMMCTutorial folder.</p> <p>(9) <strong>PBMC_bulk_ATAC_tutorial fragments files</strong></p> <p>Files used by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details.</p> <p>(10) <strong>PBMC_bulk_ATAC_tutorial example result TFBS bigwigs (Bcell_0_TFBS.bigwig, Bcell_1_TFBS.bigwig, Monocyte_0_TFBS.bigwig, Monocyte_1_TFBS.bigwig , Tcell_0_TFBS.bigwig, Tcell_1_TFBS.bigwig).</strong></p> <p>Example result files generated by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details. Here we filtered ATAC-seq peaks based on accessibility, keeping ~70k highly accessible peaks.</p> <p>(11) <strong>CTCF_degron.tar.gz</strong></p> <p>Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details. </p> <p>(12) <strong>obsBias.tsv</strong></p> <p>Input data used for training the Tn5 bias model. For more details see https://github.com/buenrostrolab/PRINT/blob/main/code/predictBias.py (line 84)</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.