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Data used for manuscript "The coordination of green-brown food webs and their disruption by anthropogenic nutrient inputs"
<p>Data used for manuscript "The coordination of green-brown food webs and their disruption by anthropogenic nutrient inputs".</p> <p>This includes estimations of various properties of food webs, such as stocks of compartments, fluxes between compartments, and conversion efficiencies.</p>
Climate model output for "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"
<p>Climate model output associated with the manuscript "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"</p>
Formatted inputs for hydrofixr
<p>**Consolidated input data for hydrofixr** (https://github.com/pnnl/hydrofixr)<br> Prepared: 2021-12-21<br> Contact: sean.turner@pnnl.gov</p> <p>--------------------------------------------------------------------------<br> **HydroSource_HYC.csv**<br> --------------------------------------------------------------------------<br> Description: Locations and key characteristics of U.S. hydropower plants<br> Original data source: ORNL HydroSource "Existing Hydropower Assets"<br> URL: https://hydrosource.ornl.gov/dataset/EHA2021<br> Cite: Johnson et al., Existing Hydropower Assets, 2021. HydroSource. ORNL.<br> DOI: 10.21951/EHA_FY2021/1782791<br> Downloaded: 2021-10-13<br> Procedure:<br> - Select key data columns<br> - Filter for conventional hydro<br> --------------------------------------------------------------------------</p> <p><br> --------------------------------------------------------------------------<br> **EIA_NetGEN_HYC.csv**<br> --------------------------------------------------------------------------<br> Description: Observed, plant-level, monthly hydropower generation (MWh)<br> Period of coverage: 2001 - 2019<br> Original data source: EIA Form 923 (906/920)<br> URL: https://www.eia.gov/electricity/data/eia923/<br> Downloaded: 2021-10-13<br> Procedure:<br> - Filter for EIA IDs listed in HydroSource<br> - Filter for type "HY" / "HYC" (conventional hydro).<br> - Clean and convert to long format.<br> --------------------------------------------------------------------------</p> <p><br> --------------------------------------------------------------------------<br> **USACE_PNW_monthly_mmm.csv** and **USACE_PNW_weekly_mmm.csv**<br> --------------------------------------------------------------------------<br> Description: Observed, plant-level, monthly and weekly mean/max/min ("mmm")<br> Period of coverage: 2011 - 2020<br> Original data source: USACE Data Query<br> URL: https://www.nwd-wc.usace.army.mil/dd/common/dataquery/www/<br> Downloaded: 2021-10-14<br> Procedure:<br> - Compute mean, max, and min of hourly generation for months and weeks.<br> --------------------------------------------------------------------------</p> <p><br> --------------------------------------------------------------------------<br> **WM_dev_base_case_cropped_2004_2010/**<br> --------------------------------------------------------------------------<br> Description: Output from MOSART-WM simulation; average daily flow rate.<br> Period of coverage: 2004 - 2010<br> Units: m3/s<br> Spatial resolution: 0.125 grid<br> Temporal resolution: daily.<br> Notes: these MOSART-WM outputs have been filtered and cropped...<br> ...for relevant variables and spatial extent to reduce file size.<br> --------------------------------------------------------------------------</p> <p><br> --------------------------------------------------------------------------<br> **EIA_plant_capabilitiy.csv**<br> --------------------------------------------------------------------------<br> Description: Monthly plant capabilities<br> Data source: EIA form 860 (2019) 3_1_Generator file.<br> Approach:<br> - Filter for EIA IDs contained in HydroSource (i.e., hydropower dams).<br> - Summer and winter capabilities assumed to be JJA and DJF, respectively.<br> - Fall and spring capabilities interpolated from summer/winter.<br> - Unit: MW.<br> -------------------------------------------------------------------------</p>
Model input and output data of the FlexMex model comparison
<p>This data collection includes the input and output data of the FlexMex model experiment (grant number: 03ET4077A-H) funded by the German Federal Ministry for Economic Affairs and Energy (BMWi). The aim of the FlexMex project is to better understand the interrelationships of modelling approaches and model results in the mapping and analysis of technical-structural flexibilities in future electricity systems.</p> <p>The data are separated in the two subfolders InputData and OutputData. For the input data, a distinction is made between scalar and time series data.</p> <p>Please find additional information on the models, test cases and analysis in the ReadMe and the publications cited there.</p>
Isopret input files
<p>Input files for isopret (https://github.com/TheJacksonLaboratory/isopret).</p>
Dataset for Aqueous habitats and carbon inputs shape the microscale geography and interaction ranges of soil bacteria
<p>This repository hosts data for the paper entitled: "<em>Aqueous habitats and carbon inputs shape the microscale geography and interaction ranges of soil bacteria</em>" by Samuel Bickel and Dani Or.</p> <p>The following files are provided:</p> <p><strong>Microcosm experiment:</strong></p> <p>- Fluorescence microscopy images of the microcosm experiment (*.tif)</p> <p>- Code used for extracting cell locations from images (image_analysis.py)</p> <p><strong>Global model estimates from the bacterial interactions heuristic model:</strong></p> <p>- Maps of estimated cell density and proportion of biomass associated with anoxic cell clusters (*.nc)</p> <p> </p>
Input files required by the bioconvert benchmark
<p>These files required to launch the bioconvert benchmarking snakemake framework. For details see https://bioconvert.readthedocs.io .</p>
Input data for ERIC values calculation for RAA process
<p>For each area (in total 9 areas of SEE) and for each week (in total 2 weeks of 2021) used in final demonstration of CROSSBOW TC1.1.1 one input file is prepared – in total 18 excel files (*.xlsx).</p> <p>Each file is consisted of 4 sheets:</p> <ul> <li>LOAD – load forecast for 168 timestamps of the given week;</li> <li>DISP_GEN_THERMAL – unit capacity and Forced Outage Rate of production for dispatchable thermal units;</li> <li>DISP_GEN_HYDRO – unit capacity and Forced Outage Rate of production for dispatchable hydro units;</li> <li>NONDISP_GEN – forecasted values for Photovoltaic, Wind, Run of River, Combined Heat and Power and Biomass non-dispatchable units for 168 timestamps of the given week.</li> </ul> <p>Sampling period: Week 9 and Week 10 of 2021</p>
A subset dataset of COVID-19 Blood Atlas for CellDrift input
<p>A subset dataset of COVID-19 Blood Atlas for CellDrift input. The original data can be found in this paper: <a href="https://doi.org/10.1016/j.cell.2022.01.012">https://doi.org/10.1016/j.cell.2022.01.012</a>. We did subsetting on the data and extracted 116,124 cells covering 8 disease conditions, 6 PBMC cell types and a series of time points (days since onset) ranging from day 0 to day 25. </p>
Input for Bayesloc calculations for locating the 27 February 2022 Lop Nor earthquake
<p>Steven J Gibbons, NGI<br> 2022-04-04</p> <p>The directories contained within this tar file contain all the files needed to calculate the location estimates<br> of the 2022-02-27 Lop Nor earthquake using the Bayesloc program with various sets of<br> inputs.<br> No output is included, only the input files:</p> <p>bayesloc.cfg<br> arrival.dat<br> station.dat<br> origin_prior.dat<br> and any traveltime tables needed.</p> <p>In each directory, the Bayesloc program is run by typing</p> <p>bayesloc bayesloc.cfg</p> <p>The directories are as follows:</p> <p>(a) USGS_P1only_singleevent<br> The following 5 files needed to locate using only the first P arrivals<br> in the NEIC solution (see Data and resources)</p> <p> ak135_P1.dat arrival.dat bayesloc.cfg origin_prior.dat station.dat</p> <p>(b) superset_singleevent<br> The following 6 files needed to locate using a set of arrivals based upon<br> the USGS arrivals, selected arrivals from the file ISC_info_20220402.txt,<br> and manual picks made from open stations available from IRIS.</p> <p> ak135_P1.dat ak135_S1.dat arrival.dat bayesloc.cfg origin_prior.dat station.dat</p> <p>(c) 11 different directories<br> fisk_900526<br> fisk_900816<br> fisk_920521<br> fisk_920925<br> fisk_931005<br> fisk_940610<br> fisk_941007<br> fisk_950515<br> fisk_950817<br> fisk_960608<br> fisk_960729<br> <br> In each of these directories, there are the files<br> ak135_P1.dat arrival.dat bayesloc.cfg origin_prior.dat station.dat<br> needed to locate one of the 11 nuclear explosions described in Fisk (2002)<br> on an event by event basis.</p> <p>(d) all_events_joint_USGSonly_fixedGT</p> <p> This solves for the location of the 20220227 event simultaneously with the<br> locations of the 11 GT nuclear tests, using only those arrivals chosen from<br> the USGS solution for the 20220227 event<br> (i.e. the arrivals in the directory USGS_P1only_singleevent )</p> <p> This directory contains five files<br> ak135_P1.dat arrival.dat bayesloc.cfg origin_prior.dat station.dat</p> <p>(e) all_events_joint_superset_fixedGT</p> <p> This solves for the location of the 20220227 event simultaneously with the<br> locations of the 11 GT nuclear tests, using the arrivals from the 20220227<br> event "superset" (i.e. the arrivals in the directory superset_singleevent )</p> <p> This directory contains six files<br> ak135_P1.dat ak135_S1.dat arrival.dat bayesloc.cfg origin_prior.dat station.dat</p> <p>The image "locations_panel.png" contains the output from each of the Bayesloc<br> calculations in these directories, plus a few others using the same input files but with<br> changes to the origin_prior.dat files such that the GT events are not fixed to the locations<br> specified by Fisk (2002).</p> <p>Panel (a) displays the bulletin location estimates listed in the file<br> "ISC_info_20220402.txt" together with the GT locations of the nuclear tests provided by<br> Fisk (2002).</p> <p>Panel (b) displays the results from the single event location runs: i.e. the outputs<br> from all of the directories</p> <p>fisk_900526<br> fisk_900816<br> fisk_920521<br> fisk_920925<br> fisk_931005<br> fisk_940610<br> fisk_941007<br> fisk_950515<br> fisk_950817<br> fisk_960608<br> fisk_960729<br> superset_singleevent<br> USGS_P1only_singleevent</p> <p>Panel (c) displays the locations when the files in the directory<br> all_events_joint_superset_fixedGT are run but with modifications<br> to the origin_prior.dat file to remove the 1 km lateral constraint on the GT<br> events. In addition, the brown points on this map are the output from<br> the directory all_events_joint_USGSonly_fixedGT but with the origin_prior.dat<br> modified to remove the constraints of the GT events.</p> <p>Panel (d) displays the locations when the files in the directory<br> all_events_joint_superset_fixedGT are run as they are.<br> In addition, the brown points on this map are the output from<br> the directory all_events_joint_USGSonly_fixedGT.</p> <p>In panels (c) and (d) the nuclear explosion locations are only displayed<br> for the calculations in the all_events_joint_superset_fixedGT directory.<br> The locations for these events obtained in the all_events_joint_USGSonly_fixedGT<br> directory are very similar.</p> <p>Data and resources<br> ------------------</p> <p>The Bayesloc probabilistic multiple seismic event location software was obtained from<br> <a href="https://www-gs.llnl.gov/nuclear-threat-reduction/nuclear-explosion-monitoring/bayesloc">https://www-gs.llnl.gov/nuclear-threat-reduction/nuclear-explosion-monitoring/bayesloc</a><br> (last accessed April 2022).</p> <p>The file ISC_info_20220402.txt is the output from a search of<br> <a href="http://www.isc.ac.uk/iscbulletin/">http://www.isc.ac.uk/iscbulletin/</a><br> performed on April 2, 2022. (ISC, 2022)</p> <p>The National Earthquake Information Center earthquake report for the 27 February 2022 event is found on<br> <a href="https://earthquake.usgs.gov/earthquakes/eventpage/us6000h0k8/executive">https://earthquake.usgs.gov/earthquakes/eventpage/us6000h0k8/executive</a><br> (last accessed April 2022).</p> <p>References<br> ----------</p> <p>Fisk, M. D. (2002). Accurate Locations of Nuclear Explosions at the Lop Nor Test Site Using Alignment of Seismograms and IKONOS Satellite Imagery. Bull. Seismol. Soc. Am. 92, 29112925. doi:<a href="http://dx.doi.org/10.1785/0120010268">10.1785/0120010268</a>.</p> <p>International Seismological Centre (2022), On-line Bulletin, <a href="https://doi.org/10.31905/D808B830">https://doi.org/10.31905/D808B830</a></p>
Flee Input Data Collection
<p>This is a collection of FabFlee input data files, collected on 3-5-2022. For more information on how to use these files, please refer to flee.readthedocs.io.</p>
Inputs of the Jupyter Notebook - Cosmos-UK soil moisture
<p>The dataset contains the inputs of the notebook "Cosmos-UK soil moisture" published in The Environmental Data Science Book.</p> <p>The input data refer to a subset of the public 2013-2019 COSMOS-UK dataset, daily and subhourly observations and metadata for four stations: WYTH1, WADDN, SHEEP and CHIMN. These stations represent the first sites to prototype COSMOS sensors in the UK, see further details in Evans et al. (2016) and they are situated in human-intervened areas (grassland and cropland), except for one in a woodland land cover site.</p> <p>Data from COSMOS-UK up to the end of 2019 are available for download from the UKCEH Environmental Information Data Centre (EIDC). The data are accompanied by documentation that describes the site-specific instrumentation, data and processing including quality control. The full dataset is available for <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">download</a> under the terms of the Open Government License.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> <li> <p>Doran Khamis (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/dorankhamis">@dorankhamis</a></p> </li> <li> <p>Matt Fry (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/mattfry-ceh">@mattfry-ceh</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>UK Centre for Ecology & Hydrology (creator)</p> </li> <li> <p>Natural Environment Research Council (support)</p> </li> </ul> <p><em>Dataset reference and documentation</em></p> <ul> <li> <p>S. Stanley, V. Antoniou, A. Askquith-Ellis, L.A. Ball, E.S. Bennett, J.R. Blake, D.B. Boorman, M. Brooks, M. Clarke, H.M. Cooper, N. Cowan, A. Cumming, J.G. Evans, P. Farrand, M. Fry, O.E. Hitt, W.D. Lord, R. Morrison, G.V. Nash, D. Rylett, P.M. Scarlett, O.D. Swain, M. Szczykulska, J.L. Thornton, E.J. Trill, A.C. Warwick, and B. Winterbourn. Daily and sub-daily hydrometeorological and soil data (2013-2019) [cosmos-uk]. 2021. URL: <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>, <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">doi:10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>.</p> </li> </ul> <p><strong>Further references</strong></p> <ul> <li> <p>Jonathan G. Evans, H. C. Ward, J. R. Blake, E. J. Hewitt, R. Morrison, M. Fry, L. A. Ball, L. C. Doughty, J. W. Libre, O. E. Hitt, D. Rylett, R. J. Ellis, A. C. Warwick, M. Brooks, M. A. Parkes, G. M.H. Wright, A. C. Singer, D. B. Boorman, and A. Jenkins. Soil water content in southern england derived from a cosmic-ray soil moisture observing system – cosmos-uk. <em>Hydrological Processes</em>, 30:4987–4999, 12 2016. <a href="https://doi.org/10.1002/hyp.10929">doi:10.1002/hyp.10929</a>.</p> </li> <li> <p>M. Zreda, W. J. Shuttleworth, X. Zeng, C. Zweck, D. Desilets, T. Franz, and R. Rosolem. Cosmos: the cosmic-ray soil moisture observing system. <em>Hydrology and Earth System Sciences</em>, 16(11):4079–4099, 2012. URL: <a href="https://hess.copernicus.org/articles/16/4079/2012/">https://hess.copernicus.org/articles/16/4079/2012/</a>, <a href="https://doi.org/10.5194/hess-16-4079-2012">doi:10.5194/hess-16-4079-2012</a>.</p> </li> </ul>
Input data and Supplementary Results for "Turnover in life-strategies recapitulates marine microbial succession colonizing model particles"
<p><strong>README</strong></p> <p>This page contains processed input data used for downstream analysis and some Supplementary Results for the paper:</p> <p>Pascual-García, A., Schwartzman, J., Enke, T.N., Iffland-Stettner, A., Cordero, O.X., Bonhoeffer, S., Turnover in life-strategies recapitulates marine microbial succession colonizing model particles (2022).</p> <p> </p> <p><strong>Input data</strong></p> <p> </p> <ul> <li> <p>File <em>“count_table.ESV.biom”</em>: Table containing the abundance of each Exact Sequence Variant (ESV) in the different samples (biom format).</p> </li> <li> <p>File <em>“count-table_</em><em>metagenomes</em><em>_KEGGs.L3.spf”</em>. Table containing the abundances of genes found in the shotgun metagenomics experiments annotated in KEGG and then aggregated into classes according to the finest classification in KEGG's hierarchy (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“count-table_</em><em>PICRUST2</em><em>_KEGGs.L3.spf</em>”. Table containing the abundances of genes predicted with PICRUSt v2. These genes were annotated in KEGG and aggregated into classes according to the finest hierarchy in KEGG (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“count-table_Isolates_KEGGs.L3.spf</em>”. Table containing the abundances of genes found in the isolates genomes that were annotated in KEGG and aggregated into classes according to the finest classification in KEGG's hierarchy (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“samples_metadata.tsv”</em>. Metadata table describing the samples.</p> </li> <li> <p>File <em>“isolates_metadata.tsv”.</em> Metadata table describing the isolates, it includes shallow phylogenetic levels and a categorical identifier describing the environmental preference estimated for the ESV having a 100% sequence identity with a ZINB-GLM.</p> </li> <li> <p>File <em>“sequences.ESV.</em><em>fasta</em><em>”.</em> File containing the Exact Sequence Variants fasta.</p> </li> </ul> <p><strong>Supplementary Materials</strong></p> <p> </p> <ul> <li> <p>File <em>“qiime2_visualizations.zip”</em>. A file containing visualizations compatible with the qiime2 viewer (simply drag and drop the file in <a href="https://view.qiime2.org/">https://view.qiime2.org/</a>) for each sample or combination of samples, labelled as `$substrate.$medium.$replicate`, where `$medium = {Beads, Seawater}` and `$replicate = {A,B,C}`. If the label is not present for one field, it means that all samples are aggregated for that field e.g.:</p> <ul> <li> <p>“<em>count_table.ESV.Chitosan.Beads.A.bar-plots.</em><em>qzv”</em> Contains the replicate experiment A for communities on the synthetic beads in chitosan.</p> </li> <li> <p>“<em>count_table.ESV.Chitosan.bar-plots.</em><em>qzv”</em> Contains all samples in chitosan (both seawater communities and the three replicates of communities on the beads).</p> </li> </ul> </li> <li> <p>File <em>“README.odt”</em>. This readme in libreoffice format.</p> </li> <li> <p>File <em>“</em><em>Genome_deposition_information.xlsx”. </em> NCBI identifiers for the isolates’ genomes.</p> </li> <li> <p>File "barcodes_to_samples_MGRAST.xlsx". Contains the barcodes of each sample and its metadata as it was deposited in MG-RAST. In a second tab, there is a subset of samples with a low number of reads that MG-RAST analyzed together, generating a single entry (termed "mixed").</p> </li> <li> <p>Access to raw an processed metagenomes and analysis are provided through MG-RAST following [this link](<a href="https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp85635">https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp85635</a>).</p> <ul> <li> <p>As of May 30th, 2022, there are two issues with the dataset in MG-RAST which are out of our scope to solve. We will report any update here. The first problem is related to the entry TCCTGAGC-GTAAGGAG-s_2_, which does not load in MG-RAST. These are very low samples and were discarded in most analyses. In addition, you will find in the metadata 17 metagenomes that do not belong to our project.</p> </li> </ul> </li> </ul>
Input files for CelparBTE
<p>These files are examples of mesh definition used in CelparBTE. The filenames follow a format of Mk_Np.in, where M is the number of cells (in thousand) and N is the number of partition.</p> <p>CelparBTE is a Fortran-90 source code designated for solving the phonon Boltzmann Transport Equation (BTE) to predict the temperature distribution in semiconductors using a 'synthetic' parallelization method. Specifically, the parallelization method includes three options: cell-based, batched cell-based, and hybrid band-based/cell-based.</p>
The input data set includes 729 objects (patients) and 39 variables (clinical qualitative and quantitative descriptors).
<p>For reliable data treatment and interpretation qualitative descriptors were omitted and only numerical clinical indicators were included in the data matrix. Finally, the data set dimension was [729 x 18].</p> <p> The data were treated by hierarchical cluster analysis and factor analysis. The major goal of the data mining was to reach statistically significant partitioning of the objects and variables into similarity patterns (clusters) which helps to better understand the data structure, to assess the meaning of the partitioning achieved, thus promoting the evaluation of the health status of the patients and the role of specific descriptors for the formation of the partitioning patterns.</p> <p>3D classification Python tool.</p>
Global input datasets for use in constraints on global seafloor biogenic methane production from deterministic and machine learning modeling
<p>This dataset includes 9 grids used as model input for manuscript "Constraints on global seafloor biogenic methane production from deterministic and machine learning modeling". Additionally, there are four grids (heat flow, total organic carbon, porosity, and crust age) for which variable uncertainty was given.</p> <p>Grids here are available in xyz (longitude in decimal degrees, latitude in decimal degrees, and variable) ascii file format. Each reference is below is the grids native reference. For more information on the creation of these grids please visit the main manuscript.</p> <p>Below are respective file names and variable name/units:</p> <p>Dataset 1: Elevation in Meters (+ indicates above sea level, - below sea level)</p> <p>Tozer, B., Sandwell, D. T., Smith, W. H. F., Olson, C., Beale, J. R., & Wessel, P. (2019). Global bathymetry and topography at 15 arc sec: SRTM15+. <em>Earth and Space Science</em>, 6. https://doi.org/10.1029/ 2019EA000658</p> <p>Dataset 2: Seawater Density in Kilograms per Cubic Meter</p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 3: Seawater Temperature in Degrees Celcius </p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 4: Seawater Salinity in Percent Salinity Units</p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 5: Heat Flow in Milliwatts per Square Meter</p> <p>Global Heat Flow Compilation Group (2013). Component parts of the World Heat Flow Data Collection. <em>PANGAEA</em>, https://doi.org/10.1594/PANGAEA.810104</p> <p>Hornbach, M. J., Harris, R. N. & Phrampus, B. J. (2020). Heat flow on the U.S. Beaufort Margin, Arctic Ocean: Implications for ocean warming, methane hydrate stability, and regional tectonics. <em>Geochemistry, Geophysics, Geosystems</em>, 21(5). e2020GC008933. https://doi.org/10.1029/2020GC008933</p> <p>Dataset 6: Sediment Thickness in Meters</p> <p>Straume, E. O., Gaina, C., Medvedev, S., Hochmuth, K., Gohl, K., Whittaker, J. M., … Hopper, J. R. (2019). GlobSed: updated total sediment thickness in the world’s oceans. <em>Geochemistry, Geophysics, Geosystems</em>, 20(4), 1756–1772.</p> <p>Dataset 7: Seafloor Porosity in Fraction</p> <p>Martin, K. M., Wood, W. T., & Becker, J. J. (2015). A global prediction of seafloor sediment porosity using machine learning. <em>Geophysical Research Letters</em>, 42(24), 2015GL065279. https://doi.org/10.1002/2015GL065279</p> <p>Dataset 8: Seafloor Total Organic Carbon in Percent Dry Weight</p> <p>Lee, T.R., Wood, W.T., & Phrampus, B.J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. <em>Global Biogeochemical Cycles</em>. 33, 37–46, doi:10.1029/2018GB005992.</p> <p>Dataset 9: Crust Age in Million Years</p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust. <em>Geochemistry, Geophysics, Geosystems</em>, 9, Q04006. https://doi.org/10.1029/2007GC001743</p> <p>Dataset 10: Seafloor Porosity Uncertainty in Fraction</p> <p>Dataset 11: Seafloor Total Organic Carbon Uncertainty in Percent Dry Weight</p> <p>Lee, T.R., Wood, W.T., & Phrampus, B.J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. <em>Global Biogeochemical Cycles</em>. 33, 37–46, doi:10.1029/2018GB005992.</p> <p>Dataset 12: Heat Flow Uncertainty in Milliwatts per Square Meter</p> <p>Dataset 13: Crust Age Uncertainty in Million Years</p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust. <em>Geochemistry, Geophysics, Geosystems</em>, 9, Q04006. https://doi.org/10.1029/2007GC001743</p>
Modelling assumptions and input dataset for the case study of the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study"
<p>This data package includes the modelling assumptions and input data to replicate the results of the case study included in the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study". This paper is part of the 18th International Conference on the European Energy Market (EEM22).</p> <p>The case study models the Belgian day-ahead electricity market, in which the existing storage is considered, in addition to large-scale battery energy storage systems of different sizes for varying renewable energy shares. A detailed description of the case study is provided in the readme file. </p> <p>This supplementary data package includes the following files: </p> <p> --Belgium Model Input Data.xlsx: Dataset used as input in the case study of the mentioned paper<br> --Modelling Assumptions.pdf: Modelling assumptions considered in the case study<br> --readme.txt (this file): Includes a detailed description of the data package</p> <p> </p> <p>The data included in this dataset was collected from public open sources [1]-[2]. Please notice that this dataset does not replace the original open access information. For accessing the data, please visit the following websites:</p> <p>[1] “ENTSO-E Transparency Platform.” [Online]. Available: https://transparency.entsoe.eu/dashboard/show. [Accessed: 06-Jul-2022].<br> [2] “Grid data.” [Online]. Available: https://www.elia.be/en/grid-data. [Accessed: 06-Jul-2022].</p> <p><br> </p> <p> </p>
Input files for SICOPOLIS v5.3
<p>Archives containing the input files (and corresponding READMEs) for <a href="https://doi.org/10.5281/zenodo.6872648">SICOPOLIS v5.3</a>:<br> ant.tgz - Antarctica,<br> grl.tgz - Greenland,<br> asf.tgz - Austfonna,<br> nhem.tgz - Northern hemisphere,<br> scand.tgz - Scandinavia,<br> tibet.tgz - Tibet,<br> nmars.tgz - North polar cap of Mars,<br> smars.tgz - South polar cap of Mars,<br> eismint.tgz - EISMINT (Phase 2 SGE and modifications),<br> heino.tgz - ISMIP HEINO,<br> mocho.tgz - Mocho-Choshuenco ice cap.</p> <p>Manual download of these archives is not required! This will happen automatically when <a href="https://doi.org/10.5281/zenodo.6872648">SICOPOLIS v5.3</a> is installed.</p>
The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip, supplementary table 3
<p>Supplementary table 3: Summary statistics from an EWAS assessing the relationship between variance in DNA methylation value and DNA input level.</p>
[Demo Input Data] for SCAFE: a software suite for analysis of transcribed cis-regulatory elements in single cells
<p>This archive (input.tar.gz) contains the demo data for SCAFE v1.0.0 (on <a href="https://doi.org/10.5281/zenodo.7023163">Zenodo</a> or <a href="https://github.com/chung-lab/SCAFE/releases/tag/v1.0.0">Github</a>)</p> <p><em>SCAFE</em> (Single Cell Analysis of Five-prime Ends) provides an end-to-end solution for processing of single cell 5’end RNA-seq data. It takes a read alignment file (*.bam) from single-cell RNA-5’end-sequencing (e.g. 10xGenomics Chromimum®), precisely maps the cDNA 5'ends (i.e. transcription start sites, TSS), filters for the artefacts and identifies genuine TSS clusters using logistic regression. Based on the TSS clusters, it defines transcribed cis-regulatory elements (tCRE) and annotated them to gene models. It then counts the UMI in tCRE in single cells and returns a tCRE UMI/cellbarcode matrix ready for downstream analyses, e.g. cell-type clustering, linking promoters to enhancers by co-activity <em>etc</em>.</p> <p>For details on installation, usage and test run on demo data, visit <a href="https://github.com/chung-lab/SCAFE">https://github.com/chung-lab/SCAFE</a></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.