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Update of: The Global Fire Atlas of individual fire size, duration, speed and direction
<p>This is an updated and extended record of the Global Fire Atlas introduced by Andela et al. (2019). Input data (burned area and land cover products) are updated to the MODIS Collection 6.1 (the original version featured in Andela et al. (2019) was based on collection 6.0 burned area and collection 5.1 land cover products, respectively). The timeseries is extended to cover the period 2002 to August 2024.</p> <h2><strong>Methodological Notes:</strong></h2> <p>The method employed to create the dataset precisely follows the approach described by Andela et al. (2019).</p> <p>The input burned area product is MCD64A1 Collection 6.1. It is described by Giglio et al. (2018) and available at: https://lpdaac.usgs.gov/products/mcd64a1v061/. </p> <p>The input land cover product is MCD12Q1 Collection 6.1. It is described by Sulla-Menashe et al. (2019) and available at: https://lpdaac.usgs.gov/products/mcd12q1v061/. </p> <p>Note that while the methods have remained the same compared to Andela et al. (2019), we do observe small differences between the Global Fire Atlas products originating from differences between the MCD64A1 collection 6.1 burned area data used here and the collection 6 data used in the original product. In addition, we observe more substantial differences in the dominant land cover class associated with each fire due to the differences between the MCD12Q1 collection 6.1 data used here and collection 5.1 data used in the original product. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. For each MODIS tile, the fire season is defined as the twelve months centred on the month with peak burned area (see Andela et al., 2019). For example, for a MODIS tile with peak burned area in December, the 2023 fire season would be defined as the period from July 2023 to June 2024, with the current record ending in August 2024. This is particularly relevant in the Southern extratropics and the northern hemisphere subtropics, where the fire seasons often span the new year. The local definition of the fire season is based on climatological peak in burned area as described by Andela et al. (2019).</p> <p>Here we extended the time-series to include the fire season of 2002, and extended the time-series until February 2025.</p> <h2> </h2> <h2><strong>Usage Notes:</strong></h2> <h3><strong>Incomplete Observations for the Latest Fire Seasons:</strong></h3> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. As such, the time-series can be incomplete for the latest fire season (e.g. the "2024 fire season") and also for the penultimate fire season (e.g. the "2023 fire season") due to the way that fire seasons are defined (see above). For example, if the month with peak burned area for a tile is December, then full data covering the 2023 fire season in that tile are not available until midway through the 2024 calendar year. This contrasts with the original dataset from Andela et al. (2019), which only included the data for entire fire seasons between 2003 and 2016. </p> <h3><strong>Observational Outages:</strong></h3> <p>For the purpose of time-series analysis, we note that the 2002 product may have been affected by outages of Terra-MODIS (most notably, June 15 2001 - July 3 2001 and March 19 2002 - March 28 2002), which affects the burn date estimates and Global Fire Atlas product. Following the launch of Aqua-MODIS in May 2002 burn date estimates are more reliable as estimated from both MODIS sensors onboard Terra and Aqua. </p> <h3><strong>File Naming Convention:</strong></h3> <p>GFA_v<em>{time-stamp}</em>_<em>{data-type}</em>_<em>{fire_season}</em>.<em>{file_type}</em></p> <p><em>{time-stamp}</em><strong> </strong>= Date that code was run.</p> <p><em>{data-type}</em><strong> </strong>= “ignitions” or “perimeters” for vector files; “day_of_burn”, “direction”, “fire_line”, or “speed” for raster files.</p> <p><em>{fire_season} </em>= the locally-defined fire season in which the fire was ignited (see more below).</p> <p><em>{file_type} </em>= ".shp" for vector files; ".tif" for raster files. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. Hence the file GFA_v20240409_perimeters_2003.shp can include fires from the 2003 fire season that ignited in the calendar years 2002 or 2004. </p> <h3>Coordinate systems (Map Projections):</h3> <p>Vector data are provided on the WGS84 projection.</p> <p>Raster data are provided on the MODIS sinusoidal projection used in NASA tiled products. The WKT string defining this projection is:</p> <pre><code>'PROJCS["unnamed",GEOGCS["Unknown datum based upon the custom spheroid",DATUM["Not_specified_based_on_custom_spheroid",SPHEROID["Custom spheroid",6371007.181,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</code></pre> <p> </p> <h2><strong>Data Layers:</strong></h2> <p><em><strong>Table 1: Overview of the Global Fire Atlas data layers. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire, while the underlying 500 m gridded layers reflect the day-to-day behavior of the individual fires. In addition, we provide aggregated monthly summary layers at a 0.25° resolution for regional and global analyses.</p> <table> <tbody> <tr> <td>File name</td> <td>Content</td> </tr> <tr> <td>SHP_ignitions.zip</td> <td>Shapefiles of ignition locations with attribute tables (see Table 2)</td> </tr> <tr> <td>SHP_perimeters.zip</td> <td>Shapefiles of final fire perimeters with attribute tables (see Table 2)</td> </tr> <tr> <td>GeoTIFF_direction.zip</td> <td>500 m resolution daily gridded data on direction of spread (8 classes)</td> </tr> <tr> <td>GeoTIFF_day_of_burn.zip</td> <td>500 m resolution daily gridded data on day of burn (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_speed.zip</td> <td>500 m resolution daily gridded data on speed (km/day)</td> </tr> <tr> <td>GeoTIFF_fire_line.zip</td> <td>500 m resolution daily gridded data on the fire line (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_monthly_summaries.zip</td> <td>Aggregated 0.25° resolution monthly summary layers. These files include the sum of ignitions, average size (km2), average duration (days), average daily fire line (km), average daily fire expansion (km2/day), average speed (km/day), and dominant direction of spread (8 classes). </td> </tr> </tbody> </table> <p> </p> <p><em><strong>Table 2: Overview of the Global Fire Atlas shapefile attribute tables. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire.</p> <table> <tbody> <tr> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>lat, lon</td> <td>Coordinates of ignition location (°)</td> </tr> <tr> <td>size</td> <td>Fire size (km2)</td> </tr> <tr> <td>perimeter</td> <td>Fire perimeter (km)</td> </tr> <tr> <td>start_date, start_DOY</td> <td>Start date (yyyy-mm-dd), start day of year (1-366)</td> </tr> <tr> <td>end_date, end_DOY</td> <td>End date (yyyy-mm-dd), end day of year (1-366)</td> </tr> <tr> <td>duration</td> <td>Duration (days)</td> </tr> <tr> <td>fire_line</td> <td>Average length of daily fire line (km)</td> </tr> <tr> <td>spread</td> <td>Average daily fire growth (km2/day)</td> </tr> <tr> <td>speed</td> <td>Average speed (km/day)</td> </tr> <tr> <td>direction, direc_frac</td> <td>Dominant direction of spread (N, NE, E, SE, S, SW, W, NW) and associated fraction</td> </tr> <tr> <td>MODIS_tile</td> <td>MODIS tile id</td> </tr> <tr> <td>landcover, landc_frac</td> <td>MCD12Q1 dominant land cover class and fraction (UMD classification), provided for 2002-2023</td> </tr> <tr> <td>GFED_regio</td> <td>GFED region (van der Werf et al., 2017; available at https://www.globalfiredata.org/)</td> </tr> </tbody> </table> <p> </p> <p> </p>
Plant Atlas 2020 — British and Irish plant conservation statuses
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data behind the conservation status tables presented on the Conservation tabs of species’ pages of the Plant Atlas 2020 website (<a href="http://www.plantatlas2020.org"><span>www.plantatlas2020.org</span></a><span>).</span></p>
Vascular Territory template and atlases in MNI space
<p><strong>Data</strong></p> <p>Sixteen subjects (mean age (sd): 69.6 (8.2); 37.5% female) were recruited to generate a high-resolution template. The cohort consists of twelve stroke-free, non-demented patients with the sporadic form of cerebral amyloid angiopathy (CAA), and similarly-aged healthy controls (n=4). Each participant underwent high-resolution MRI with a Siemens Magnetom Prisma 3T scanner (using a 32-channel head coil) as part of a separate study. The standardized protocol included a Multiecho T1-weighted (voxel size: 1x1x1 mm<sup>3</sup>; Repetition Time [TR]: 2510 ms), a 3D-FLAIR (voxel size: 0.9x0.9x0.9 mm<sup>3</sup>; TR: 5000 ms; TE: 356 ms), and a T2-weighted Turbo Spin Echo (voxel size: 0.5x0.5x2.0 mm<sup>3</sup>; TR: 7500 ms; TE: 84 ms) sequence. Scans were manually assessed to ensure no gross pathology was present, such as hemorrhage or silent brain infarcts.</p> <p><strong>Template and territorial map creation</strong></p> <p>We employed Advanced Normalization Tools (ANTs) for image processing (Avants et al., 2010, 2011) for creating a brain template based on multimodal information using T1, T2 and 3D-FLAIR sequences. After template creation, we smoothed the resulting templates (FSL; Gaussian smoothing, sigma = 1) and registered the resulting templates into MNI space, again using ANTs (Avants et al., 2011).</p> <p>Vascular territories were outlined on the right hemisphere in the T1-weighted atlas image and contain anatomically validated ACA, MCA, and PCA territories supratentorially. The right hemispheric map was then mirrored onto the left hemisphere to create a full-brain vascular territory map, which was manually assessed and corrected where necessary.</p> <p> </p> <p>For more details, please see the original publication that utilized the template. If you utilize this template, please also cite</p> <p>Schirmer, Markus D., et al. "Spatial signature of white matter hyperintensities in stroke patients." <em>Frontiers in neurology</em> 10 (2019): 208.</p> <p><a href="https://doi.org/10.3389/fneur.2019.00208">https://doi.org/10.3389/fneur.2019.00208</a></p> <p> </p> <p><strong>Files</strong></p> <p><strong>FLAIR template</strong>: caa_flair_in_mni_template_smooth.nii.gz<br> <br> <strong>FLAIR template after brain extraction and intensity normalization</strong>: caa_flair_in_mni_template_smooth_brain_intres.nii.gz<br> <br> <strong>T1 template</strong>: caa_t1_in_mni_template_smooth.nii.gz 27.7 Mb<br> <br> <strong>T2 template</strong>: caa_t2_in_mni_template_smooth.nii.gz 27.7 Mb<br> <br> <strong>Vascular territory map</strong>: mni_vascular_territories.nii.gz</p>
Regional E-Atlas of the Greater Phoenix Region: Areas of significant agricultural and residential groundwater use, 1996-2000
Spatial distribution of well water usage (groundwater) for the period 1996 - 2000. These data present significant areas of agricultural and residential groundwater use during this period. This is a spatial data object with a Coordinate Reference System (CRS) of EPSG:3479 NAD83(NSRS2007) / Arizona Central (ft); https://www.spatialreference.org/ref/epsg/3479/). The coordinate reference system (CRS) associated with these data when they were constructed initially was misrepresented in early versions (<= knb-lter-cap.101.8) of this dataset. The CAP LTER has attempted to assign a CRS based on reasonable values but the accuracy of the identified CRS cannot be certain.
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
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ATLAS Rucio Transfers Dataset
<p>This dataset is released to encourage the study of ATLAS file transfers in the Worldwide LHC Computing Grid environment, to better understand the transfer processes in this particularly heterogeneous environment.</p> <p> </p> <p>Joaquin Bogado (UNLP)</p> <p>Mario Lassnig (CERN)</p> <p>Fernando Monticelli (UNLP)</p> <p>Thomas Beermann (University of Wuppertal)</p> <p>Javier Díaz (UNLP)</p> <p>2020-11-27</p> <p>jbogado @ linti.unlp.edu.ar</p> <p>Motivation</p> <p>This dataset is released to encourage the study of ATLAS file transfers in the Worldwide LHC Computing Grid[1] environment, to better understand the transfer processes in this particularly heterogeneous environment.</p> <p>Rucio[2] is a Distributed Data Management system. Data from the Rucio ATLAS instance from June and July 2019 was retrieved and summarized in the present dataset. The Rucio ATLAS instance is responsible to keep track of the files of the ATLAS Experiment[3] at CERN. These files are stored all around the world in 100+ data centers. In order to work with the files, physicists around the world need to move them across sites. Rucio delegates the file transfer to another subsystem called FTS[4]. The rules in Rucio are groups of file transfers that are done as a unit, i.e.: a physicist may need a set of files to do an analysis, then they create a rule specifying which files need to be moved to where, and when the rule is done the analysis can start.</p> <p>If the Rule Time To Complete (RTTC) can be predicted with certain accuracy, this will allow the Rucio system and the ATLAS Experiment to schedule the transfers in a smarter way, eventually helping to optimize the resources the experiment has to do more and faster science.</p> <p>State of the art</p> <p>The metric used to calculate the accuracy is the Fraction of Good Predictions (FoGP). Formally, the FoGP is defined as in the equation that follows</p> <p>FoGP(y, y, τ) = 1/n i = 1ng(yi, yi, τ)</p> <p>Where y is the vector of observations, y is the vector of predictions, g is a function that returns 1 if the relative error | yi - yi | / yi < τ , and 0 otherwise. For a group of predictions we have that FoGP(y, y, τ = 0.1) = 0.5. This means that 50% of the predictions have less than 10% of relative error. This easy to understand metric allows to compare models directly, independently from their implementation, and only focus on the predictions the model made.</p> <p>We estimate a FoGP(τ = 0.1) > 0.95 for a model to be useful. However, there are no known models that can predict the RTTC at the rule creation time with such a high accuracy. Models with FoGP(τ = 0.1) ~= 0.5 could be useful to give feedback to the users about how much time the transfers will take. Best models known have a FoGP(τ = 0.1) = 0.14.</p> <p>Fields description</p> <p>account</p> <p>The hashed account name from the user that issued the transfer. This data has been anonymized and does not represent the real name of the user in the system. </p> <p>state</p> <p>The final state of the transfer. 'D' means the transfer is done, 'F' means the transfer has failed. Other states represent internal states from Rucio and are not important. Very few transfers showed other states than D or F.</p> <p>activity</p> <p>The activity of the transfer. It's related to the priority of the transfers inside the system. Priorities are based on shares and related to the 'share' field. As transfers requests are queued in Rucio and in FTS, transfers are picked to be served with a probability equal to its share among all the transfers that are in the queue at that time.</p> <p>SIZE</p> <p>The size in bytes of the file to be transferred.</p> <p>src_rse/dst_rse</p> <p>The source/destination Rucio Storage Element (RSE). An RSE is a logical unit inside Rucio that represents a dedicated storage location of a data center. Usually there are more than one physical machine. Rucio doesn't know how many storage nodes compose an RSE, so this is the minimum logical unit of storage for the system. Both fields have been anonymized.</p> <p>id</p> <p>The unique identifier of a transfer request. If a transfer needs to be retried, the next attempt will have a different id. </p> <p>previous_attempt_id</p> <p>If the transfer request is a retry, the id of the previous attempt is filled in. Otherwise, this field is empty.</p> <p>retry_count</p> <p>This is the number of times a transfer has been retried. If it is the first attempt, the field is 0. </p> <p>rule_id</p> <p>This is the id of the rule the transfer belongs to. All the transfers in the same rule share the same rule_id.</p> <p>external_host</p> <p>This is the hash of the FTS server that will trigger the actual transfer of files between the files. There are several FTS servers and some are shared with other Experiments outside ATLAS. It is known that the server with hash fe1d4db902b6271 is used by ATLAS Experiment exclusively, so this can be a good place to start.</p> <p>RTIME</p> <p>This is the time in seconds the transfer spends in the Rucio System, since it is created at the created timestamp, till the transfer is submitted to the FTS system, at the submitted timestamp. This can be calculated as submitted - created. This value is not available to the system until the transfer is submitted, that is the submitted timestamp.</p> <p>QTIME</p> <p>This is the time in seconds the transfer spends in the FTS System, since it is submitted by Rucio the submitted timestamp, till the transfer starts its network time at the started timestamp. This can be calculated as started - submitted. This value is not available to the system until the transfer ends, that is until the ended timestamp, because FTS does not propagate the started time of a transfer immediately, but only once the transfer ends or fails.</p> <p>NTIME</p> <p>This is the actual time in seconds the file is being transferred, using the network, since the transfer is started by FTS at the started timestamp, till the transfer ends at the ended timestamp. This can be calculated as ended - started. The value is not available to the system until the transfer ends, that is the ended timestamp.</p> <p>RATE</p> <p>This is the average rate in bytes per second of each transfer. It is calculated as SIZE/NTIME and is not available till the transfer ends.</p> <p>link</p> <p>This is the hash that represents a source/destination RSE pair. Links have peculiarities that make them unique, and likely affect the RTTC, e.g., some links have higher bandwidth, or the disks of the associated storages in the respective source and destination RSEs are faster than the ones on other links. </p> <p>created</p> <p>This is the time at which a transfer request is created in Rucio. For all the transfers that share the same rule_id, the minimum created timestamp is also the rule creation time, at which we want to know the RTTC. All date timestamps have a resolution of 1 second.</p> <p>submitted</p> <p>This is the time at which the transfer request is submitted from Rucio to FTS. </p> <p>started</p> <p>This is the time at which the transfer request starts the actual transfer, using the network. This data will not be known until the transfer ends because FTS doesn't publish this data immediately but only once the transfer ends.</p> <p>ended</p> <p>This is the time at which the transfer ends. For all the transfers that share the same rule_id, the maximum ended timestamp is also the ending time of the rule. </p> <p>share</p> <p>This is a number between 0 and 1 that represents the weighted probability of a transfer of being picked to be served given its activity.</p> <p>Target</p> <p>The target of the study is to know the Rule Time To Complete (RTTC) at the creation time of the rule. The creation time of the rule is the minimum created timestamp of those transfers that share the same rule_id. The RTTC can be computed as the ending time of the rule minus the starting time of the rule, being the ending time of the rule, the maximum ended timestamp of all the transfers that share the same rule_id.</p> <p> </p> <p> </p> <p>References</p> <p> </p> <ol> <li> <p>Worldwide LHC Computing Grid. <a href="https://wlcg.web.cern.ch/">https://wlcg.web.cern.ch/</a> Retrieved 23/11/2020</p> </li> <li> <p>Rucio Scientific Data Management. <a href="https://rucio.cern.ch/">https://rucio.cern.ch/</a> Retrieved 23/11/2020</p> </li> <li> <p>The ATLAS Experiment. <a href="https://atlas.cern/">https://atlas.cern/</a> Retrieved 23/11/2020</p> </li> </ol> <p>File Transfer Service. <a href="https://fts.web.cern.ch/fts/">https://fts.web.cern.ch/fts/</a> Retrieved 23/11/2020</p>
scRNA-seq atlases for 3 Caenorhabditis species - annotated cell datasets
<p>Annotated datasets (monocle3 objects) of scRNA-seq data for <em>C. elegans</em>, <em>C. briggsae</em> and <em>C. tropicalis</em> L2 nematodes. The datasets are published together with the manuscript "Divergence in neuronal signaling pathways despite conserved neuronal identity among <em>Caenorhabditis</em> species".</p> <p><a href="https://doi.org/10.1016/j.cub.2025.05.036" target="_blank" rel="noopener">https://doi.org/10.1016/j.cub.2025.05.036</a></p> <p>Files deposited include cell datasets for all sequenced cells ("all_cds") and datasets for all cells annotated as neurons ("neu_cds"). </p> <p><em>C. elegans</em> strain - N2.</p> <p><em>C. briggsae</em> strain - AF16.</p> <p><em>C. tropicalis</em> strain - NIC203.</p>
Neural Retina Atlas
<p>This repository contains intermediate files used in analysis for the manuscript 'A proteogenomic atlas of the human neural retina' by Riepe et al. (2024). The code is available at https://github.com/cmbi/Neural-Retina-Atlas.</p>
Reference data for neural retina atlas
<p>This repository contains reference files used in analysis for the manuscript 'A proteogenomic atlas of the human neural retina' by Riepe et al. (2024). The code is available at https://github.com/cmbi/Neural-Retina-Atlas.</p>
Transcriptomic atlas reveals organ-specific disease tolerance in sickle cell mice: dataset bone marrow HbAA mice injected or not with heme
<p>The objective of this experiment was to explore the transcriptome of the HbSS Townes mouse model of sickle cell disease. Townes model mice carry several human hemoglobin knock-in genes replacing the endogenous mouse genes and may be useful in studying sickle cell disease. All mice were genotyped, age- and sex-matched littermates. All HbAA (control, normal human hemoglobin) vs HbSS (sickle cell disease, mutated human hemoglobin) mice were used for experimentations at 6-8 weeks of age, to limit intra-group heterogeneity. Hemin (Ferriprotoporphyrin IX) was purchased from Frontiers Scientific and injected intravenously (iv.) in a retroorbital sinus at a concentration of 24 µmol/kg. Control mice received PBS instead. Mice were anesthetized with isoflurane 2-3% for injections, blood collection and sacrifice. All mice were sacrificed by cervical dislocation, 4 hours after injection.</p> <p>This dataset contains the results of the HbAA mice with and without heme.</p> <p>The corresponding HbSS mice with and without heme are deposited under number 10.5281/zenodo.10962782</p> <p>Bone marrow RNA was extracted by Macherey Nagel kit, according to the manufacturer’s instructions. The quality and quantity of mRNA were evaluated using a 2100<br>bioanalyzer with TNA 6000 NanoKits (all Agilent Technologies, Palo Alto, CA, USA). RNA Integrity Numbers superior to 7 were eligible for subsequent reverse transcription into cDNA. RNAseq was performed at the GenomIC plateform Cochin Institute INSERM U1016. After RNA extraction, RNA quality (RNA integrity number) was estimated. 1μg of high-quality total RNA sample (RIN &gt;7) was processed to build up the libraries, using TruSeq Stranded mRNA kit (Illumina) according to manufacturer instructions. Briefly, purified poly-A containing mRNA molecules were fragmented and reverse-transcribed using random primers. Replacement of dTTP by dUTP during second strand synthesis allowed us to achieve strand specificity. Addition of a single A base to the cDNA was followed by ligation of Illumina adapters.<br>Libraries were quantified by qPCR using KAPA Library Quantification Kits for Illumina Libraries (KapaBiosystems, Wilmington, MA). Library profiles were assessed using DNA High Sensitivity LabChip kits on an Agilent Bioanalyzer. Libraries were sequenced on an Illumina Nextseq 500 instrument using 75 base-lengths read V2 chemistry in a paired-end mode. After sequencing, primary analysis based on AOZAN software (ENS, Paris), was applied to demultiplex and control the quality of the raw data (based of FastQC modules / version 0.11.5).</p> <p>The dataset here represents 4 groups of mice, 4 mice per group as follows: HbAA PBS, HbAA heme, HbSS PBS, HbSS heme. </p> <p> </p>
Primary Breast Tumor Atlas
<p>Integrated scRNA-seq atlas of primary breast tumors containing 236,363 cells from 119 biopsy samples across eight original source datasets. <a title="Persistent link using digital object identifier" href="https://doi-org.foyer.swmed.edu/10.1016/j.xcrm.2024.101511" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.xcrm.2024.101511</span></a></p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 2 x 2 km grid square locations up to 2019
<p><span>This resource provides the data behind the 2 × 2 km grid square (tetrad) British and Irish distribution maps, for 3,431 taxa, presented in both the Plant Atlas 2020 book and website (</span><a href="http://www.plantatlas2020.org"><span><span>www.plantatlas2020.org</span></span></a><span><span>), up to 2019. These are presence-only data, indicating where a taxon was reported from a tetrad</span></span><span>. These 2 km square presences are based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s.</span></p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km grid square locations, subdivided by survey period, up to 2019
<p><span>This resource provides the data behind the 10 × 10 km grid square (hectad) British and Irish distribution maps, for 3,497 taxa, presented in both the Plant Atlas 2020 book and website (</span><a href="http://www.plantatlas2020.org"><span><span>www.plantatlas2020.org</span></span></a><span><span>), subdivided by time period<a><span>.</span></a> These are presence-only data, indicating where a taxon was reported from a hectad, within a given</span><span><span></span></span></span><span> multi-year period, up to 2019. These time periods cover the 20<sup>th</sup> Century, but also extend back to the earliest botanical records known for Britain and Ireland in the first period (pre-1930). These 10 km square presences are based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s.</span></p>
EDEN2020 Ovine Diffusion Tensor Magnetic Resonance Tractography Atlas
<p>This dataset has been created to share the first <em>in vivo, </em>population-averaged Diffusion Tensor Magnetic Resonance Imaging (DTI) Ovine Tractography Atlas (OTA), where the course of the main white matter fiber bundles of the ovine brain has been reconstructed. The OTA has been described in the related paper ‘In vivo Diffusion Tensor Magnetic Resonance Tractography of the Sheep Brain: An Atlas of the Ovine White Matter Fiber Bundles’ by Pieri <em>et al. </em>(2019) <a href="https://doi.org/10.3389/fvets.2019.00345">https://doi.org/10.3389/fvets.2019.00345</a></p> <p>In the context of the EU’s Horizon EDEN2020 project, in vivo brain MRI protocol for ovine animal models was optimized on a 1.5T scanner. High resolution conventional MRI scans and DTI sequences (b-value = 1,000 s/mm<sup>2</sup>, 15 directions) were acquired on ten anesthetized sheep <em>ovis aries</em>, to define the diffusion features of normal adult ovine brain tissue. Topography of the ovine cortex was studied, and DTI maps were derived, to perform DTI deterministic tractography reconstruction of the corticospinal tract (CST), corpus callosum (CC), fornix (FX), visual pathway (VP), and occipitofrontal fascicle (OF), bilaterally for all the animals. Binary masks of the tracts were then coregistered and reported in the space of a standard stereotaxic ovine reference system ('ovine_model_05.nii', Nitzsche B. <em>et al.</em>, <em>Front. Neuroanat.</em> 9:69. <a href="https://doi.org/10.3389/fnana.2015.00069">https://doi.org/10.3389/fnana.2015.00069</a>). Finally, these were combined across animals to obtain population probability masks for each tract, representing voxel- by-voxel probability of the presence of the tract in the 10 animals, thus ranged between 0 and 10. </p> <p>Please don't forget to cite this publication when using the Ovine Tractography Atlas: </p> <p>Pieri V., Trovatelli M., Cadioli M., Zani D.D., Brizzola S., Ravasio G., Acocella F., Di Giancamillo M., Malfassi L., Dolera M., Riva M., Bello L., Falini A., & Castellano A. (2019). In vivo Diffusion Tensor Magnetic Resonance Tractography of the Sheep Brain: An Atlas of the Ovine White Matter Fiber Bundles. <em>Front Vet Sci, 6</em>(345), 345 <a href="https://doi.org/10.3389/fvets.2019.00345">https://doi.org/10.3389/fvets.2019.00345</a> </p> <p>This work has been carried out in the context of the EDEN2020 (Enhanced Delivery Ecosystem for Neurosurgery in 2020, www.eden2020.eu) project, that received funding from the European Union’s EU Research and Innovation programme Horizon 2020 under Grant Agreement No. 688279.</p>
Open Soil Atlas Motivation Survey
<p>The Open Soil Atlas motivation study was conducted within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON) on citizen science. Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc.<br> Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement.<br> The goal of the study is to analyse the motivation to participate of a specific citizen science community focused on fighting soil pollution in the Open Soil Atlas pilot supported by the ACTION project. More info on the pilot available at <a href="https://actionproject.eu/citizen-science-pilots/open-soil-atlas/">https://actionproject.eu/citizen-science-pilots/open-soil-atlas/</a>.</p> <p>The Open Soil Atlas motivation study is part of the study about motivation in citizen science projects conducted within the ACTION project (<a href="https://doi.org/10.5281/zenodo.5753092">https://doi.org/10.5281/zenodo.5753092</a>). The survey was designed and administered using the <a href="https://coney.cefriel.com/">Coney</a> toolkit.</p> <p>The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are:</p> <ul> <li><em>*-procedure.ttl</em> contains the RDF representation of the structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-results.ttl </em>contains the RDF representation of the answers collected using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-survey.tll </em>contains a comprehensive RDF representation of the survey data using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-results.csv </em>contains the CSV of the collected answers</li> <li>*-<em>script.R</em> is the R script developed to analyse the collected answers</li> <li>*-<em>mean-var-motivating-questions.csv </em>contains the computed mean and average for each question considered (observable variables)</li> <li>*-<em>mean-var-motivating-factor.csv </em>contains the computed mean and average for each motivation factor considered (latent variables)</li> <li>*-<em>correlation-factors-global-motivation.csv </em>contains the correlation analysis between each motivation factor and the global motivation </li> </ul>
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity: Associated data
<p>This dataset contains raw and processed data from the COvid-19 Multi-omics Blood ATlas (COMBAT) consortium. Data are divided into 26 datasets representing anonymised raw and processed data from deep immune phenotyping of peripheral blood from COVID-19 patients. </p> <p>In addition to the data listed below, some datasets are available through other repositories: </p> <ul> <li> <p>Proteomics data (CBD-KEY-PROTEOMICS) is available at PRIDE</p> <ul> <li> <p>Accession number: PDX023175</p> </li> <li> <p>Contact: Roman Fischer</p> </li> </ul> </li> </ul> <ul> <li> <p>Genetic data and detailed clinical information are available via a data access agreement through EGA</p> <ul> <li> <p>Study accession: EGAS00001005493 </p> </li> </ul> </li> </ul> <p>For further information regarding specific datasets, please contact the individuals listed in Dataset_descriptions.pdf through <a href="mailto:contact@combat.ox.ac.uk">contact@combat.ox.ac.uk</a>. </p>
Pre-processed data of atlas in EUCP-WP2
<p>Outputs from the probabilistic projection methods developed or assessed in the European Climate Projection system (EUCP) Horizon2020 project. The data can be previewed through our <a href="\"https://eucp-project.github.io/atlas/\"">interactive atlas</a>.</p> <p> </p> <p>For more information, see the <a href="\"https://eucp-project.github.io/atlas/about\"">atlas about page</a>, or the corresponding <a href="\"https://eucp-project.github.io/storyboards/atlas/1_intro\"">storyboard</a>.</p> <p> </p> <p><strong>Preprocessed data of Atlas in EUCP-WP2</strong></p> <p>We provide some notebooks that check the original/raw data, fix/add the metadata using <a href="\"https://cfconventions.org/Data/cf-conventions/cf-conventions-1.9/cf-conventions.html\"">CF-conventions</a> and save data in a NetCDF format. See <a href="\"https://github.com/eucp-project/atlas/blob/main/python/README.md\"">https://github.com/eucp-project/atlas/blob/main/python/README.md</a>.</p> <p>For two of the methods, REA and ClimWIP, pre-calculated weights have also been included. Note that these weights are only valid in the context of this specific model ensemble. Therefore, the original (pre-processed) model data is published together with the weights.</p> <p>The pre-processed data follows the following standards:</p> <p><strong>coordinates</strong></p> <ul> <li>climatology_bounds (climatology_bounds) datetime64[ns] ['2050-06-01', '2050-09-01', '2050-12-01', '2051-03-01']</li> <li>time (time) (datetime64[ns]) [2050-07-16 2051-01-16] # "JJA", "DJF"</li> <li>latitude (lat) (float64) [30, ..., 75]</li> <li>longitude (lon) (float64) [-10, ..., 40]</li> <li>percentile (percentile) (int64) [10, 25, 50, 75, 90]</li> </ul> <p><strong>variables</strong></p> <ul> <li>tas (time, latitude, longitude, percentile) (float64)</li> <li>pr (time, latitude, longitude, percentile) (float64)</li> </ul> <p><strong>attributes</strong></p> <p>The attributes of variables and coordinates are defined as:</p> <ul> <li>"tas": {<br> "description": "Change in Air Temperature",<br> "standard_name": "Change in Air Temperature",<br> "long_name": "Change in Near-Surface Air Temperature",<br> "units": "K",<br> "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014",<br> },</li> <li>"pr": {<br> "description": "Relative precipitation",<br> "standard_name": "Relative precipitation",<br> "long_name": "Relative precipitation",<br> "units": "%",<br> "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014",<br> },</li> <li>"latitude": {"units": "degrees_north", "long_name": "latitude", "axis": "Y"},</li> <li>"longitude": {"units": "degrees_east", "long_name": "longitude", "axis": "X"},</li> <li>"time": {<br> "climatology": "climatology_bounds",<br> "long_name": "time",<br> "axis": "T",<br> "climatology_bounds": ["2050-6-1", "2050-9-1", "2050-12-1", "2051-3-1"],<br> "description": "mean changes over 20 years 2041-2060 vs 1995-2014. The mid point 2050 is chosen as the representative time.",<br> },</li> <li>"percentile": {"units": "%", "long_name": "percentile", "axis": "Z"},</li> </ul> <p>The attributes of the data is defined as:</p> <ul> <li>"description": "Contains modified <code>institute</code> <code>method</code> data used for Atlas in EUCP project.",</li> <li>"history": "original <code>institute</code> <code>method</code> data files ...",</li> </ul> <p><strong>output file names</strong></p> <p>output_file_name = <code>prefix_activity_institution-id_source_method_sub-method_cmor-var</code></p> <p>example: atlas_EUCP_CNRM_CMIP6_KCC_cons_tas.nc</p> <p><strong>Reference</strong>:</p> <p><a href="\"https://eucp-project.github.io/atlas/about\"">https://eucp-project.github.io/atlas/about</a></p>
Atlas of the Solar Intensity Spectrum and its Center-to-Limb Variation
<p>The atlas of the Third Solar Spectrum (SS3) represents the ratio between the intensity spectrum at different distances from the solar limb and the intensity spectrum at disk center (µ = 1.0), both in units of the intensity of the local continuum level. The observed positions of the measurements cover 9 different µ values along the solar axis ranging from 0.1 to 0.9 in step of 0.1, where µ=cosθ is the cosine of the heliocentric angle θ. The current version of the atlas covers the range 4384- 6610 Å.</p> <p>In the PDF file, the first plot represents the spectrum at the center of the solar disk, recorded at IRSOL. The next 9 plots represent the 3rd solar spectrum for µ=0.1, µ=0.2, µ=0.3, …, µ=0.9</p> <p>Columns of the CSV file:</p> <table> <tbody> <tr> <td><strong>WL:</strong></td> <td>Wavelength, 4384-6610 Å</td> </tr> <tr> <td><strong>IC:</strong></td> <td>I/I<sub>c</sub> at disc center</td> </tr> <tr> <td><strong>RMU01:</strong></td> <td>limb / disk-center ratio at µ=0.1</td> </tr> <tr> <td><strong>RMU02:</strong></td> <td>limb / disk-center ratio at µ=0.2</td> </tr> <tr> <td><strong>…</strong></td> <td>…</td> </tr> <tr> <td><strong>RMU09:</strong></td> <td>limb / disk-center ratio at µ=0.9</td> </tr> </tbody> </table>
Biodiversity atlas data
<p><strong>Introdution</strong><br> This dataset contains six sets of biodiversity atlas data that have been used as a test of occupancy downscaling methods. They are derived from two taxonomic groups, vascular plants and birds and are either regional or national atlases. The original atlas have already been published and the references are below. Should these data be used these citations should be used.</p> <p><strong>File contents</strong><br> Each zip file contains a set of text files, one for each taxa. Each text file has three tab seperated columns, longitude, latitude and presence. The presence column indicates whether the grid cell was occupied (1) or unoccupied (0). The longitude and latitude columns are the grid references of each grid cell surveyed. For Ireland the Irish grid system is used (EPSG:29903); for the UK the Ordnance Survey Grid (EPSG:27700) and for Belgian the Lambert 72 system (EPSG:31370). The grid cell area for all plant datasets is 4 km<sup>2</sup>. Whereas the grid areas for the Flemish birds is 25 km<sup>2</sup> and for the Irish breeding birds is 100 km<sup>2</sup>. </p> <p><strong>References</strong></p> <ol> <li>Evans, P., Evans, I. & Rothero, G. (2002) Flora of Assynt: fowering plants and ferns. P.A. Evans and I.M. Evans. ISBN 0954181301.</li> <li>Forbes, R.S. & Northridge, R.H. (2012) The Flora of County Fermanagh. National Museums Northern Ireland. ISBN 1905989288</li> <li>Halliday, G. (1997) A Flora of Cumbria. Centre for North-West Regional Studies, University of Lancaster. ISBN 1862200203.</li> <li>National Biodiversity Data Centre (2011) The Second Atlas of Breeding Birds in Britain and Ireland: 1988-1991. URL https://doi.org/10.15468/pkhsnb</li> <li>Shropshire Ecological Data Network (2017) Shropshire Ecological Data Network database. Occurrence Dataset. URL https://doi.org/10.15468/5v5pvk</li> <li>Lockton, A.J. & Whild, S.J. (2015) The Flora and Vegetation of Shropshire. Shropshire Botanical Society. ISBN 0953093727.</li> <li>Vermeersch, G., Anselin, A., Devos, K., Herremans, M., Stevens, J., Gabriëls, J., Van Der Krieken, B., Brosens, D. & Desmet, P. (2014) Broedvogels - Atlas of the breeding birds in Flanders 2000-2002. v1.5. URL http://doi.org/10.15468/sccg5a</li> </ol>
Plant Atlas 2020 — British and Irish phenological data (flowering and leafing ranges)
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data behind the phenological diagrams (flowering and leafing) presented on the Plant Atlas 2020 website (<a href="http://www.plantatlas2020.org"><span>www.plantatlas2020.org</span></a><span>) and in the <em>Plant Atlas 2020</em> book. </span><span>Note that for non-flowering plants included in the atlas (e.g. ferns, horsetails etc.), the “flowering” fields in the phenology file included here are equivalent to the months when spore-bearing structures are visible.</span></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.