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3,197 results for “atlas”
Plant Atlas 2020 — Plant native statuses for Britain, Ireland and the Channel Islands
<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 statements concerning species’ native statuses, for various geographical levels and areas, presented in the Plant Atlas 2020 website (<a href="http://www.plantatlas2020.org"><span>www.plantatlas2020.org</span></a><span>) and book (Stroh et al., 2023).</span></p>
North Pacific Subtropical Gyre RCLV Atlas (Version 2)
<p>This dataset tracks Rotationally Coherent Lagrangian Vortices (RCLVs) at an 8-day resolution in the North Pacific Subtropical Gyre region around Hawai’i. The ‘lat’ and ‘lon’ variables represent the center coordinates of the vortex, or the extremum of integrated relative vorticity. The contour boundaries demark the edge of the RCLV based on 32-day backward-in-time Lagrangian trajectories. In other words, eddies in this dataset represents a fluid mass of substantial size that was coherent for at least 32 days. The fluid masses are tracked through time to assign IDs and RCLV ages. The software used to generate the dataset is publicly available at https://github.com/lexi-jones/RCLVatlas (DOI: <a href="https://doi.org/10.5281/zenodo.7702978" target="_blank" rel="noopener noreferrer">10.5281/ZENODO.7702978</a>).</p> <p>Version 1 of the dataset (https://simonscmap.com/catalog/datasets/RCLV_atlas; DOI: <a href="https://doi.org/10.5281/zenodo.8139149" target="_blank" rel="noopener noreferrer">10.5281/ZENODO.8139149</a>) only includes contours that represent features of age 32 days or older. Version 2 extends the dataset to include eddy genesis by advecting the particle sets backward-in-time to age 24, 16, and 8 days.</p>
Plant Atlas 2020 — British altitude-by-latitude diagrams
<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 British altitude-by-latitude diagrams presented in the Plant Atlas book (Stroh et al., 2023) and on the website (www.plantatlas2020.org).</p>
Plant Atlas 2020 — British and Irish species weekly apparency (including by-latitude breakdown for Britain), 2000–2019
<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 contains the species weekly “apparency” metrics presented within the Plant Atlas book and website, including a breakdown of species apparency by latitude for Britain. Apparency at the scale presented here (2 x 2 km spatially, smoothed over the period 2000–2019) combines aspects of recorder activity and detectability, with the latter being primarily influenced by phenology at this spatio-temporal scale.</p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km distribution trends for 1930–2019 (long-term) and 1987–2019 (short-term), including country-level breakdowns
<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 for the long- (1930–2019) and short- term (1987–2019) 10 x 10 km (“hectad”) distribution trends, presented in both the <em>Plant Atlas 2020</em> book (Stroh et al., 2023) and website (www.plantatlas2020.org).</p>
Gene expression ATLAS of Arabidopsis thaliana (accession Columbia) across its lifecycle
<p><strong>Abstract: </strong>Arabidopsis thaliana (accession- Columbia) is an important model plant. RNA-Seq based study of 36 gene expression libraries was carried out to explore transcriptional programs operating in different plant parts (seedling, rosette, root, inflorescence, flower, fruit silique, and seed) and developmental stages (2-leaf stage, 6-leaf stage, 12-leaf stage, senescence stage, dry mature and imbibed seed stage). For each tissue type and developmental stage, three individual plants were used as biological replicates.</p> <div><strong><span>Organism part: </span></strong><span>inflorescence, whole plant, seed, root, silique fruit, flower, rosette</span></div> <div> </div> <div><span><strong>Developmental stage:</strong> </span><span>LP.02 two leaves visible stage, IL.00 inflorescence just visible stage, fruit size 30 to 50% stage, LP.12 twelve leaves visible stage, root development stage, fruit size 70% to final stage, LP.06 six leaves visible stage, dry seed stage, flowering stage, seed imbibition stage, sporophyte senescent stage, inflorescence development stage</span></div> <div> </div> <div> <div><strong><span>Organism: </span></strong><span>Arabidopsis thaliana</span></div> <div> </div> <div><span><strong>Ecotype:</strong> </span><span>Col-0</span></div> <div> </div> <div><strong><span>Genotype: </span></strong><span>wild type genotype</span></div> <div> </div> <div><span><strong>Age:</strong> Samples are from </span><span>20-day, 49-day, 39-day, 15-day, 21-day, 9-day, 22-day, 55-day, 26-day, 45-day</span></div> <div> </div> <div><span><strong><span>Experimental Designs: </span></strong><span>growth chamber study<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://purl.obolibrary.org/obo/EO_0007269" target="_blank" rel="noopener"> EFO</a></span>, <span>development or differentiation design<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0001746" target="_blank" rel="noopener"> EFO</a></span>, <span>organism part comparison design<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0001750" target="_blank" rel="noopener"> EFO</a></span></span></div> <div> </div> <div><span>For more description of the data and sample types see the file <a href="../api/records/11133989/draft/files/PRJEB24664_Sample_descriptors.xlsx/content" target="_blank" rel="noopener noreferrer">PRJEB24664_Sample_descriptors.xlsx or visit </a> or visit <a href="https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422/sdrf">https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422/sdrf</a></span></div> <div> </div> <div><span>Original data was submitted from </span></div> <div> <ul> <li><span>EMBL-EBI ArraExpress: <a href="https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422">https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422</a></span></li> <li><span>NCBI SRA: <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB24664">https://www.ncbi.nlm.nih.gov/bioproject/PRJEB24664</a></span></li> </ul> <p><strong><span>Protocol description:</span></strong></p> <table> <tbody><tr> <th>Name</th> <th>Type</th> <th>Description</th> <th>Hardware</th> </tr> </tbody><tbody> <tr> <td>P-MTAB-71349</td> <td><span>growth protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0003789" target="_blank" rel="noopener"> EFO</a></span></td> <td>Seeds were planted in pots containing commercial potting mix with fertilizers. Pots were covered with clear perforated plastic wrap and kept at 4 degrees celsius for 3 days to break the dormancy. After 3 days plants were transferred to the Intellus Ultra growth chamber (Percival Scientific, IA, USA) which was set to temperature 22-23 degrees celsius, light intensity 120-150 micromol/m2sec under the cycle of 16h light and 8h dark. Soil was kept moist by gently spraying with water every 72 hours to maintain humidity to 50-60%. Sampling time point is given in days after germination.</td> <td> </td> </tr> <tr> <td>P-MTAB-71350</td> <td><span>nucleic acid extraction protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0002944" target="_blank" rel="noopener"> EFO</a></span></td> <td>Total RNA from frozen samples was extracted as a method described in Filichkin et al., 2010. Total RNA was used to isolate large RNA as per manufacturer's protocol for miRNeasy Mini kits (Qiagen Inc., USA), and RNase-free DNase (Life Technologies Inc., USA).</td> <td> </td> </tr> <tr> <td>P-MTAB-71351</td> <td><span>nucleic acid library construction protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0004184" target="_blank" rel="noopener"> EFO</a></span></td> <td>True-Seq kit (Illumina Inc.) was used to prepare RNA-seq libraries, according to the manufacturer’s protocol.</td> <td> </td> </tr> <tr> <td>P-MTAB-71352</td> <td><span>nucleic acid sequencing protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0004170" target="_blank" rel="noopener"> EFO</a></span></td> <td>101bp paired-end sequencing of mRNA was performed by using the standard protocols on Illumina HiSeq 3000.</td> <td>Illumina HiSeq 3000</td> </tr> </tbody> </table> </div> </div>
Water Cycle Atlas of India (WCAI) v1.0 [1980-2022, Daily, 0.1°]
<p>WCAI ( Water Cycle Atlas of India) is a long term land surface reanalysis of the Indian subcontinent from Jan 1980 to Dec 2022. It is produced using the Indian Land Data Assimillation System (ILDAS) at the Indian Institute of Technology Delhi, New Delhi India. It provides daily estimates of 16 variables at 0.1 degree resolution. The hydrologic and hydrodynamic model combination used is NoahMP3.6 and HYMAP2 forced with Indian Meteorological Department (IMD) gridded precipitation and MERRA2 reanalysis data. This dataset will be valuable for water balance assessments at continental scale for multiple applications such as water resources planning, soil conservation, urban planning, and natural disaster risk mitigation.</p> <p>Changelog</p> <p>------------------------------------</p> <p>v1.0 - Uncalibrated Model outputs</p> <p> </p> <p> </p>
Natural Products Atlas (NPAtlas) MetFrag Local CSV
<p>This is a local CSV file of the Natural Products Atlas (NPAtlas, <a href="https://www.npatlas.org/joomla/">https://www.npatlas.org/joomla/</a>) for MetFrag (<a href="https://msbi.ipb-halle.de/MetFrag/">https://msbi.ipb-halle.de/MetFrag/</a>).</p> <p>Data was extracted to CSV from the TSV download from the NPAtlas <a href="https://www.npatlas.org/download">website</a>, with column headers for compulsory fields adjusted to fit the MetFrag format. Several entries with charged formulas (one +3, 7 +2, 125 +, 6 negative) had the charges removed from the formula to produce results consistent with other MetFrag files (where neutral formula is required; no adjustment for +/-H was performed so these remained consistent with the mass entries with minimum manipulation). Several overflowing lines were removed (due to new metadata) and NPA023832 was removed as "Ho" is not recognised by MetFrag. </p> <p>This file is for users wanting to integrate the latest NPAtlas into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p>Please credit the data source in any use of this file as the licence is CC-BY - details at <a href="https://www.npatlas.org/">https://www.npatlas.org/</a></p>
RESOLUTE atlas for brain PET/MR pseudo-CT generation
<p>Template and mask images for performing the <em>Region specific optimization of continuous linear attenuation coefficients based on UTE</em> (RESOLUTE) pseudo-CT generation approach. This dataset can be used in conjunction with an open-source C++ implementation of RESOLUTE (<a href="https://github.com/UCL/petmr-RESOLUTE">https://github.com/UCL/petmr-RESOLUTE</a>) for the Siemens mMR scanner.</p>
Dataset In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging
<p>This is the dataset related to the paper "In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging", E. Najdenovska*, Y. Aléman-Gómez*, G. Battistella, M. Descoteaux, P. Hagmann, S. Jacquemont, P. Maeder, J.-P. Thiran, E. Fornari and M. Bach Cuadra, Sci. Data. 5:180270 doi: 10.1038/sdata.2018.270 (2018). *Equally contributed authors.</p> <p>We provide NifTI-1 files representing a digital atlas of seven thalamic subparts per hemisphere. More precisely, the files include the spatial probabilistic atlas maps for each thalamic subpart (Thalamus_Nuclei-HCP-4DSPAMs.nii.gz) and the maximum likelihood atlas (Thalamus_Nuclei-HCP-MaxProb.nii.gz) in MNI space. The region corresponding to each labeled thalamic part respectively is given in the look-up table Thalamic_Nuclei-ColorLUT.txt. The NIFTI files can be visualised with the main available tools such as tkmedit, freeview or 3D-Slicer.</p> <p>We also provide a step by step pseudo code for creating the atlas.</p>
SolarSMART Social Energy Atlas 2020 Georgia Interviews
<p>The SolarSMART Social Energy Atlas 2020 Georgia Interview dataset provides results from interviews with 2018 residents of the State of Georgia above the age of 18 on their perceptions of photovoltaic solar energy. The interview includes the following: demographic information for each respondent, their location in the state (by ZIP Code); the type of house they reside within; whether they have or have not adopted solar energy (and if so, how did they adopt solar energy); audio recordings of a semi-structured interview between the informants and representatives from the project who were often agents from the UGA Cooperative Extension Service; and the outputs from that interview that also included draw-a-map responses to where they perceived people adopted solar technologies in the United States and the State of Georgia. Respondents were from 25 of the 159 counties of the State of Georgia, with representation of both urban and rural residents. In total, the dataset includes 25 variables currently coded (with over 170 identified. Transcriptions of the interviews are also provided in the dataset alongside graphics for each informant that can be used in presentations and other derivatives involving this dataset in the future. This dataset inspired the SolarSMART 2020 Georgia Survey dataset (DOI: 10.5281/zenodo.4540819). </p>
cGTEx_dataset:A multi-tissue atlas of regulatory variants in cattle
<p>The files are raw data of the cGTEX dataset used in the publication <strong>https://doi.org/10.1038/s41588-022-01153-5</strong>. For details, please read the Methods section. </p> <p>1. cGTEx_meta_data_8646sample.xlsx</p> <p>Metadata consists of sample names with their sample accession, including information such as data size, cleaned reads, mapping rate, and age. The data is extracted from SRA (<a href="https://www.ncbi.nlm.nih.gov/sra">https://www.ncbi.nlm.nih.gov/sra/</a>) and BIGD (<a href="https://bigd.big.ac.cn/bioproject/">https://bigd.big.ac.cn/bioproject/</a>) ( samples starting with CRS)</p> <p>2. cGTEx_count_8646sample_27607gene.txt.gz</p> <p>Data consist of raw RNA-seq read count of 27607 genes (column names as Ensembl gene id )of 8646 samples (as row names) </p> <p>3. cGTEx_TPM_8646sample_27607gene.txt.gz</p> <p>Data consist of TPM values of 27607 genes (column names as Ensembl gene id) in samples (8646 samples as row names)</p> <p>4. cGTEx_imputed_vcf.tar.gz</p> <p>Imputed genotypes (SNP) of 7297 RNA-seq samples in 29 autosomes.</p> <p>5. cGTEx_exon_junction_8646sample.tar.gz</p> <p>Exon junction files of 8646 files </p> <p>Note: Small discrepancies in some sample names or the absence of headers in some data sets compared to https://cgtex.roslin.ed.ac.uk/ are sorted out in this upload.</p> <p> </p>
Brain Hierarchical Atlas 2 (BHA2)
<p>Elucidating the intricate relationship between the structure and function of the brain, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Magnetic Resonance Imaging (MRI) has helped in the understanding of this matter, with diffusion images providing information about structural connectivity (SC) and resting-state functional MRI revealing the functional connectivity (FC).</p> <p>Furthermore, the brain operates by discrete multiscale computations in both the time and spatial domains, in a way that is far from known (Churchland and Sejnowski, The MIT Press, 1994). To advance in the understanding of this puzzle, a dual structure-function hierarchical clustering strategy was proposed in (Diez et. al, SciRep, 2015), providing a common skeleton shared by structure and function. Here, we further extend this approach by:<br> 1. Fine-tuning the amount of matching between SC and FC via a free-parameter gamma. Specifically, when gamma is set to 0, SC is fully recovered, while when gamma is set to 1, FC is recovered. In between these extremes, a fusion scenario occurs, where both SC and FC contribute to the connectivity patterns. The raw data to generate the SC and FC matrices came from (Babayan et. al, Scientific Data, 2019), and can be downloaded from https://fcon_1000.projects.nitrc.org/indi/retro/MPI_LEMON.html.<br> 2. Making use of brain-transcriptomic data to shed light on biological interpretability of brain-related diseases in the gamma-modulated multiscale structure-function correspondence.<br> 3. Providing to the scientific community open data of different scenarios of structure-function sharing and at different spatial scales, and open code to generate them in a MRI dataset.</p> <p>The dataset is organized in the following way:</p> <p>data<br> │ ├───iPA_nROIS [Different spatial scales 183, 391, 568, 729, 964, 1242, 1584, 1795 and 2165]<br> │ │ ├───iPA_nROIS.nii.gz [Brain parcellation image]<br> │ │ ├───iPA_nROIS.csv [MNI Coordinates and location of the brain parcellation ROIs]<br> │ │ ├───SC [Structural connectivity matrices]<br> │ │ ├───FC [Functional connectivity matrices]<br> │ │ ├───ts [Resting-state functional connectivity timeseries]<br> │ │ | ├───confounds [Confounds used to filter the timeseries]<br> │ │ ├───gamma-trees [Gamma-trees of nROIs levels]<br> │ │ ├───transcriptomics.csv [Transcriptomics' of each ROI]</p> <p>If you want to use this dataset, please cite:</p> <p><em>Antonio Jimenez-Marin, Ibai Diez, Asier Erramuzpe, Sebastiano Stramaglia, Paolo Bonifazi, Jesus M Cortes</em>. <strong>Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain</strong>. biorxiv. 2023. <a href="https://doi.org/10.1101/2023.08.04.551953">https://doi.org/10.1101/2023.08.04.551953</a></p>
The South Pacific Drought Atlas (SPaDA)
<p>Instrumental and reconstructed Standardised Precipitation Evapotranspiration Index (SPEI) generated and analysed in the paper 'Extreme events in the multi-proxy South Pacific drought atlas' (Higgins et al., 2023, 10.1007/s10584-023-03585-2).</p>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2023
<p><strong>Note: The authors recommend the use of the ESA CCI-SST version of this dataset.</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) version 2023 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/r7xa-bt92">https://doi.org/10.25921/r7xa-bt92</a>) is a quality-controlled dataset containing 35.6 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore, the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models.</p> <p>Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values; Watson et al., 2020; Woolf et al., 2016). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system.</p> <p>Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in Goddijn-Murphy et al. (2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes.</p> <p>This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2023 dataset to a consistent sub-skin temperature field. The reanalysis was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Holding et al., 2019; Shutler et al., 2016). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The reanalysis dataset was produced for two climate quality and depth consistent temperature datasets: (1) The ESA SST-CCI sea surface temperature product (Merchant et al., 2019) and (2) The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Banzon et al., 2016; Huang et al., 2021; Reynolds et al., 2007).</p> <p>For both datasets, the original daily data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2023 dataset.</p> <p>The original SOCAT version 2023 data are included in full, with four additional columns containing the reanalysed data:</p> <p>* T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al., 2013). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). Full meta data are included within the file.</p> <p><strong>Comments</strong></p> <p>1. Due to the temporal range of the OISST and CCI-SST datasets the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns.</p> <p>2. This submission contains four files contained within a single zip file: SOCATv2023with_header.tsv, SOCATv2023.nc, SOCATv2023with_header_ESACCI.tsv and SOCATv2023_ESACCI.nc. The first two files correspond to the OISST version, and the second two the ESA SST-CCI version. The .tsv files are the ungridded data, and the .nc files are the gridded data for the corresponding temperature datasets.</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p><strong>How to cite these data</strong></p> <p>Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Holding et al., 2019; Shutler et al., 2016) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013).</p> <p><strong>Previous versions</strong></p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p>v2022: <a href="https://doi.org/10.5281/zenodo.8228585">https://doi.org/10.5281/zenodo.8228585</a></p> <p><strong>Acknowledgements</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p>These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>) and the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>). The UK part of the Horizon Europe OceanICU project is funded by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10063673].</p>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2022
<p><strong>Note: The authors recommend the use of the ESA CCI-SST version of this dataset.</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) version 2022 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/1h9f-nb73">https://doi.org/10.25921/1h9f-nb73</a>) is a quality-controlled dataset containing 33.7 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore, the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models.</p> <p>Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values; Watson et al., 2020; Woolf et al., 2016). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system.</p> <p>Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in Goddijn-Murphy et al. (2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes.</p> <p>This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2022 dataset to a consistent sub-skin temperature field. The reanalysis was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Holding et al., 2019; Shutler et al., 2016). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The reanalysis dataset was produced for two climate quality and depth consistent temperature datasets: (1) The ESA SST-CCI sea surface temperature product (Merchant et al., 2019) and (2) The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Banzon et al., 2016; Huang et al., 2021; Reynolds et al., 2007).</p> <p>For both datasets, the original daily data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2022 dataset.</p> <p>The original SOCAT version 2022 data are included in full, with four additional columns containing the reanalysed data:</p> <p>* T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al., 2013). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). Full meta data are included within the file.</p> <p><strong>Comments</strong></p> <p>1. Due to the temporal range of the OISST and CCI-SST datasets the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns.</p> <p>2. This submission contains four files contained within a single zip file: SOCATv2022with_header.tsv, SOCATv2022.nc, SOCATv2022with_header_ESACCI.tsv and SOCATv2022_ESACCI.nc. The first two files correspond to the OISST version, and the second two the ESA SST-CCI version. The .tsv files are the ungridded data, and the .nc files are the gridded data for the corresponding temperature datasets.</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p><strong>How to cite these data</strong></p> <p>Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Holding et al., 2019; Shutler et al., 2016) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013).</p> <p>Previous versions:</p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p><strong>Acknowledgements</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p>These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>) and the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>). The UK part of the Horizon Europe OceanICU project is funded by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10063673].</p>
HCP-YA Tractography Atlas (Figures)
<p>Yeh, F. C., Panesar, S., Fernandes, D., Meola, A., Yoshino, M., Fernandez-Miranda, J. C., ... & Verstynen, T. (2018). Population-averaged atlas of the macroscale human structural connectome and its network topology. NeuroImage, 178, 57-68.</p>
HCP-YA Tractography Atlas (NIFTI Files)
<p>Yeh, F. C., Panesar, S., Fernandes, D., Meola, A., Yoshino, M., Fernandez-Miranda, J. C., ... & Verstynen, T. (2018). Population-averaged atlas of the macroscale human structural connectome and its network topology. NeuroImage, 178, 57-68.</p>
cldf-datasets/apics: The "Atlas of Pidgin and Creole Language Structures Online" as CLDF dataset
<p>Cite as</p> <blockquote> <p>Michaelis, Susanne Maria & Maurer, Philippe & Haspelmath, Martin & Huber, Magnus (eds.) 2013. Atlas of Pidgin and Creole Language Structures Online. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at <a href="https://apics-online.info">https://apics-online.info</a>)</p> </blockquote>
Human Brain MRI Template and Myelin Atlas
<p>The structural template, quantitative myelin water imaging atlases, tissue segmentations, and regions of interest (ROIs) generated and analyzed for <em>An atlas for human brain myelin content throughout the adult life span</em></p> <p><a href="https://www.nature.com/articles/s41598-020-79540-3">https://www.nature.com/articles/s41598-020-79540-3</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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