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194 results for “agreement”
WageIndicator Collective Agreements Database Dataset with Full Texts and Selected Clauses
<p>Since 2012, the <a href="https://wageindicator.org/">WageIndicator Foundation</a> has maintained a <a href="https://wageindicator.org/cbadatabase">Collective Agreements Database</a>, where the texts of 1600 collective agreements (CBAs) from 61 countries and in 27 languages have been uploaded, coded and annotated. This database is a unique example at global level: collective agreements are documents containing conditions of employment that result from negotiations between independent unions and employers, and their content is often surrounded by an atmosphere of secrecy. Under the <a href="https://sshopencloud.eu/">SSHOC project</a> and with the support of the <a href="https://www.clarin.eu/">CLARIN Research Infrastructure</a>, the agreements have been manually and automatically annotated on several levels: for each agreement, the team answers a series of questions and selects the appropriate piece of text (clause) for each. </p> <p>One of the results of the collective agreements' annotation process is the dataset which is available here and includes all the clauses selected for each variable (WageIndicator_CBADatabase_Selected_Clauses). The full collective agreements' texts are stored in another dataset, also available here (WageIndicator_CBADatabase_Full_Texts_211019). A codebook is also included (210125-wageindicator-cba-codebook.pdf).</p>
Modeled riparian stream shading: Agreement with field measurements and sensitivity to riparian conditions
Data was collected from relatively straight channel segments in eight stream and river segments ranging from 12 to 43 meters wide in Clarke County, GA in 2008. Only channels wide enough to feature a canopy gap between the overhanging tree branches were selected. Within each stream segment stream azimuth, latitude and longitude, riparian tree height, bankfull channel width, bank height, canopy overhang height, and measurements of canopy cover with a densiometer were collected. Time and date of collections were recorded.
Dataset for Number agreement and dependency length in Finnish dialects
<p>This material contains the dataset and the scripts from the <a href="https://version.helsinki.fi/gramadapt/depling2021-number-agreement">gitlab repository</a> of the following article. Please cite the article when using the data.</p> <p>Sinnemäki, Kaius & Akira Takaki 2021. Number agreement, dependency length, and word order in Finnish traditional dialects. In <em>Proceedings of the Sixth International Conference on Dependency Linguistics (Depling, SyntaxFest 2021)</em>. Stroudsburg, PA: The Association for Computational Linguistics.</p> <p> </p>
Data for study "Realisation of Paris Agreement pledges may limit warming just below 2°C"
<p>This is the data repository with country-level data and individual scenario data for the study “Realisation of Paris Agreement pledges may limit warming just below 2°C”. Please find below a description of the different data sources available. </p> <p><strong>Emissions Scenario Data (scenarios_12Nov2021a_CR.csv). </strong>The complete set of infilled and extended emissions for each scenario presented in the study. The emissions timeseries span from 2015 to 2100 for 52 emissions species required to run MAGICC7.</p> <p><strong>Scenario Statistics Table (stats_12Nov2021a_CR_short.csv).</strong> Summary statistics for the scenarios used in the sensitivity study. Statistics available include Exceedance probabilities, peak warming, year of peak warming and warming in 2100.</p> <p><strong>Supplementary Table S1 (supplementary_table-s1.csv) - </strong>Estimate of total GHG emissions (excl. LULUCF) in 1990, 2010, 2019 and for 2025 and 2030 under the NDCs. The ‘lower’ and ‘higher’ end of the range span the four cases of full implementation and ‘unconditional-only’ NDC quantification, with their respective ranges (e.g. Australia’s 26%-28% target). Historical data based on country-level data in NDCs and PRIMAP-hist CR. Aggregation of CO2, CH4, N2O, HFCs, PFCs, and SF6 emissions performed in GWP-100 AR6 metrics. Note that the common reporting guidelines under the Paris Agreement’s transparency framework currently chose GWP-100 AR5 metric as default (Decision 5/CMA.3, UNFCCC, 2021).</p> <p><strong>Supplementary Table S2 (supplementary_table-s2.csv) </strong>An estimate of potentially additional methane reductions if the Global Methane Pledge were implemented on a country-by-country level with a 30% reduction. Of the 104 countries signed up by 2 Nov 2018, 101 are here considered (with no sufficient methane data availability for Niue, Palau and Federal State of Micronesia).<strong> </strong></p> <p><strong>Scenario Timeseries (timeseries_12Nov2021a_CR.csv). </strong>Surface Temperature and Kyoto Greenhouse Gas emissions excluding CO2 AFOLU for NDC scenarios presented in the study.<strong> </strong></p> <p><strong>IEA Scenario Timeseries (timeseries_IEA.csv). </strong>Surface Temperature and Kyoto Greenhouse Gas emissions excluding CO2 AFOLU for the IEA scenarios presented in the study.</p> <p>Contact Jared Lewis <jared.lewis@climate-resource.com> for additional data about the target scenarios.</p>
Data for "Paris Agreement requires substantial, broad, and sustained policy efforts beyond COVID-19 recovery packages"
<p>This dataset contains the underlying data for the following publication: Tanaka, K., C. Azar, O. Boucher, P. Ciais, Y. Gaucher, D. J. A. Johansson (2022) Paris Agreement requires substantial, broad, and sustained policy efforts beyond COVID-19 public stimulus packages. <em>Climatic Change</em> <strong>172, </strong>1 (2022). https://doi.org/10.1007/s10584-022-03355-6</p> <p>Earlier manuscripts were published as a preprint. https://arxiv.org/abs/2104.08342</p>
Dataset for Variation in number agreement in Finnish dialects
<p>This material contains the dataset and the scripts from the <a href="https://version.helsinki.fi/gramadapt/verbinlukukongruenssi2021">gitlab repository</a> of the following article. Please cite the article when using the data.</p> <p>Sinnemäki, Kaius & Viljami Haakana 2021. <a href="https://researchportal.helsinki.fi/files/164044959/SinnemakiHaakana_2021_Kongruenssi.pdf">Variationistinen korpustutkimus predikaatin differentiaalisesta lukukongruenssista ja substantiiviluokasta suomen murteissa</a>. In Leena Maria Heikkola, Geda Paulsen, Katarzyna Wojciechowicz & Jutta Rosenberg (eds.), <a href="https://www.doria.fi/handle/10024/181090"><em>Språkets funktion: Juhlakirja Urpo Nikanteen 60-vuotispäivän kunniaksi - Festskrift till Urpo Nikanne på 60-årsdagen - Festschrift for Urpo Nikanne in honor of his 60th birthday</em></a>, 83–112. Åbo: Åbo Akademi Förlag.</p>
High resolution cropland agreement map (30 m) circa 2020
<p>Accurate and precise measurements of global cropland extent are needed for monitoring the sustainability of agriculture at all scales. Recent advancement in remote sensing and land cover mapping methods have greatly increased the ability to estimate cropland area distribution and trends. Here the FAO presents a map of cropland agreement produced by consolidating information at pixel level from six high-resolutions maps for <em>circa </em>2020. The following six high resolution layers were used: ESRI 10 meter LU/LC, FROM-GLC, GLAD, GLC-FCS30, Globeland30 and Worldcover.</p> <p>Two bands are included in the dataset:</p> <ol> <li>Simple agreement (values between 1 and 6)</li> <li>Detailed agreement (values between 1 and 63)</li> </ol> <p>The map, developed in the Google Earth Engine platform, combines the 6 land cover/cropland layers to show their cropland agreement on pixel level at a spatial resolution of 30 meters. The simple agreement has pixel values that range from 1 (only 1 dataset classifies as cropland) to 6 (all datasets agree on presence of cropland). Pixels with a value of 0 indicate pixels where all datasets agree on absence of cropland. The second band includes a detailed agreement, showing which combination of the 6 datasets classify a pixel as cropland. The overview table (<em>DetailedAgreement_LookupTable.xlsx</em>) shows what the pixel values of this detailed agreement (from 1 to 63) correspond to.</p> <p>The dataset has been uploaded in 16 tiles, in the preview below and in the file "A<em>CroplandAgreement_30m_Tiles.png</em>" the extent of each tile can be found.</p> <p>For more information on FAO statistics on land cover and land use:</p> <p>FAO. 2022. <em>Land use statistics and indicators. Global, regional and country trends, 2000–2020</em>. FAOSTAT Analytical Brief, no. 48. Rome. <a href="https://doi.org/10.4060/cc0963en">https://doi.org/10.4060/cc0963en</a></p> <p>FAO. 2021. <em>Land cover statistics. Global, regional and country trends, 2000–2019</em>. FAOSTAT Analytical Brief Series No. 37. Rome. </p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Metrics and peer review agreement at the institutional level - Data
<p>This data is released to accompany the paper:</p> <p>Traag, VA, Malgarini, M and Sarlo, S (2020) Metrics and peer review agreement at the institutional level.</p>
Map Of Land Cover Agreement - MOLCA
<p>Map Of Land Cover Agreement (MOLCA) is generated by reusing existing datasets for High-Resolution Land Cover (HRLC), but only select portions with unanimous agreement among multiple datasets. We combined multiple HRLCs using intersection methods, retaining only the areas where all datasets agree on the land cover classes and disregarding areas of disagreement. The following HRLCs were used:</p> <ul> <li>FROM-GLC 2017 (<a href="http://data.ess.tsinghua.edu.cn/">[1]</a><a href="https://doi.org/10.1080/01431161.2012.748992">[2]</a><a href="https://doi.org/10.1016/j.scib.2017.03.011">[3]</a><a href="https://doi.org/10.1016/j.scib.2019.03.002">[4]</a>),</li> <li>GL30 2020 (<a href="http://www.globallandcover.com/">[5]</a><a href="https://doi.org/10.1016/j.isprsjprs.2014.09.002">[6]</a>),</li> <li>GHS BU S1NODSM 2016 (<a href="https://doi.org/10.1080/20964471.2017.1397899">[7]</a><a href="https://doi.org/10.3390/rs8040299">[8]</a>),</li> <li>WSF 2019 (<a href="https://geoservice.dlr.de/web/maps/eoc:wsf2019">[9]</a><a href="https://doi.org/10.6084/M9.FIGSHARE.C.4712852.V1">[10]</a><a href="https://doi.org/10.1553/giscience2021_01_s33">[11]</a>),</li> <li>GSW 2019 (<a href="https://global-surface-water.appspot.com/">[12]</a><a href="https://doi.org/10.1038/nature20584">[13]</a>),</li> <li>FNF 2018 (<a href="https://earth.jaxa.jp/en/data/2555/index.html">[14]</a><a href="https://doi.org/10.1016/j.rse.2014.04.014">[15]</a>),</li> <li>MapBiomas 2019 (<a href="https://doi.org/10.3390/rs12172735">[16]</a><a href="https://mapbiomas.org/en/accuracy-statistics?cama_set_language=en">[17]</a>),</li> <li>CCI Africa Prototype 2016 (<a href="https://2016africalandcover20m.esrin.esa.int/">[18]</a><a href="https://iiasa.dev.local/">[19]</a>),</li> <li>ESA DUE GlobPermafrost 2016 (<a href="https://doi.org/10.13140/RG.2.2.30661.76007">[20]</a><a href="https://doi.org/10.1594/PANGAEA.897916">[21]</a></li> </ul> <p> MOLCA contains around 117 billion 10-meter pixels (covering about 11.7 million square kilometers) distributed across a total area of 19 million square kilometers. It is available for 3 macro-regions in Siberia, Africa, and Amazon. The land cover classes represented in MOLCA are Bareland, Built-up, Cropland, Forest, Grassland, Shrubland, Water, Wetland, and Permanent ice and snow, covering the period between 2016 and 2020 The accuracy estimate for MOLCA indicates an Overall Accuracy (OA) of 96%. </p> <p>The repository contains:</p> <ul> <li><strong>MOLCA_<em>nnxxx</em>_v1.tif </strong>x 2075: 2075 MOLCA tiles (893 in Africa, 658 in Amazon, 524 in Siberia). Tiles of MOLCA follow the Sentinel-2 Level-1C product tiling grid, and consequently, the identifier of MOLCA tiles (<em>nnxxx</em>) is the same as the corresponding Sentinel-2 Level-1C tile identifier. </li> <li><strong>MOLCA_tiles_with_statistics.gpkg</strong>: Vector of MOLCA tile extents with statistics (number of pixels per class, total number of pixels, proportion of valid values) of each tile in the attribute table.</li> <li><strong>MOLCA_legend.csv</strong>: Class code and labels of MOLCA</li> </ul> <p> </p> <p>The creation of the MOLCA dataset was done in the project Climate Change Initiative Extension (CCI+) Phase 1 New Essential Climate Variables (NEW ECVS) High Resolution Land Cover ECV (HR_LandCover_cci) funded by the European Space Agency (ESA) known under the abbreviation CCI HRLC or CCI+ HRLC <a href="https://climate esa int/en/projects/high-resolution-land-cover/">[22]</a>.</p>
BLM-AgrF: A New French Benchmark to Investigate Generalization of Agreement in Neural Networks
<p>BLM-AgrF is a French dataset for learning the underlying rules of subject-verb agreement in sentences, developed in the BLM framework, a new task inspired by visual IQ tests known as Raven's Progressive Matrices. In this task, an instance consists of sequences of sentences with specific attributes. To predict the correct answer as the next element of the sequence, a model must correctly detect the generative model used to produce the dataset.</p>
Data used in the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan
This dataset includes modeled data describing the potential benefits of the Voluntary Agreements (VAs) from the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan.
3D Point Cloud of a railway slope - MOMIT (Multi-scale observation and monitoring of railway infrastructure threats) EU project - H2020-EU.3.4.8.3. - Grant agreement ID: 777630
<p>3D point cloud of a railway trench in Lavancia-Épercy (France). The 3D point cloud has been generated from pictures obtained by means of a UAV (DJI Matrice 600 Pro) and processed using Agisoft Metashape. The 3D point cloud is composed of 110,356,682<strong> </strong> million points containing XYZ and RGB information.</p> <p>The original file is in .bin format and is compressed in zip format.</p> <p> </p>
Content Hosting & Services Agreement between Google Arts & Culture and The University of Texas at Austin
<p>This document was obtained through a public records request filed under the <a href="https://www.texasattorneygeneral.gov/open-government/members-public/overview-public-information-act" target="_blank" rel="noopener">Texas Public Information Act</a>. It details the terms of a collaboration between the University of Texas at Austin and the Google Cultural Institute (now Google Arts & Culture). The document was requested as part of a data collection process for my PhD dissertation at the University of Texas at Austin, which, among other topics, examined the platform's use by cultural institutions. Contracts, alongside terms of service and content guidelines, are a critical aspect of a platform’s governance mechanisms.</p> <p>The document was released in response to request <strong>R005413-103123</strong> and made available on <strong>November 17, 2023</strong>, without redactions. For this version, I redacted email addresses and other personally identifiable information.</p> <p>You can find my dissertation, which references this document, on <a href="https://zenodo.org/records/13994044" target="_blank" rel="noopener">Zenodo</a> or through the <a href="https://doi.org/10.26153/tsw/55818" target="_blank" rel="noopener">University</a>.</p>
Mathematics Subject Classification interrater agreement dataset
<p>The Mathematics Subject Classification organizes Publications, Software, and Research Data into a hierarchical classification scheme maintained by MathSciNet (mr) and zbMATH Open (zbmath). According to the classification scheme, both organizations mr and zbmath agree on this classification and use labels to organize publications from mathematics and related fields. However, the classification of individual papers is done independently of each other. This dataset contains references to papers that occur in both collections (mr and zbmath) together with the respective classification labels.</p> <p>The dataset is followed in the follwing form</p> <pre><code>zbmath-id, zbmath-msc, mr-id, mr-msc 5635019, 55-06 57-06 55R70 57Q45 00B25, MR2556072, 54-06 55-06 5641347, 68R10 05C85, MR2588354, 68W25 05C70 05C85 5641348, 68R10, MR2588355, 68Q25 05C65 05C70 05C85 68Q15 5641349, 68Q05, MR2588356, 68Q05 5641350, 68M20 68W25 68T42, MR2588357, 68T42 68W25 5641351, 68M10, MR2588358, 68Q85 68Q10 5641352, 68R15, MR2588359, 68R15 05A05 05C78 5641353, 68T30 68R10, MR2588360, 05C62 68R10 5641354, 68Q30, MR2588361, 68Q30 60A99 60J20 5641355, 68W27 68M10, MR2588362, 68M10 05C82 05C85 68W27 68W40 5641356, 68W05 68T05, MR2588363, 68T05 62H30 5641357, 68W40, MR2588364, 05A15 68R05 5641358, 91A10 91A05 68Q17 91A06, MR2588365, 91A05 68W25 5641359, 91A10 68T42 68M10, MR2588366, 91B26 5641360, 68W40 68P05 68P10, MR2588367, 68W40 68P05 68Q87 5641361, 68P25 94A62, MR2588368, 94A62 11T71 68P25 5641362, 68Q45, MR2588369, 68Q45 05A05 5641363, 90C35, MR2588370, 68R10 05C85 68W25 90C35 5641364, 54H20, MR2588371, 37E10 37B10 37E45 </code></pre> <p>The meaning of the fields is:</p> <ul> <li><strong>zbmath-id</strong> Unique identifier from zbMATH Open. Prefix with <code>https://zbmath.org/</code> to visit additional information on the article. For example, <code>5635019</code> is associated with <a href="https://zbmath.org/5635019">https://zbmath.org/5635019</a></li> <li><strong>zbmath-msc</strong> space separated list of <a href="https://zbmath.org/classification/">Mathemematics Subject Classifiction</a> labels created by zbMATH Open staff. A description of the label can be retrieved by prefixing <code>https://zbmath.org/classification/?q=</code>. For example, <code>55-06</code> is associated with <a href="https://zbmath.org/classification/?q=55-06">https://zbmath.org/classification/?q=55-06</a></li> <li><strong>mr-id</strong> Unique identifier from MathReviews. Prefix with <code>https://mathscinet.ams.org/mathscinet-getitem?mr=</code> to retrieve additional information on the publication. For example, <code>MR3844132</code> is associated with <a href="https://mathscinet.ams.org/mathscinet-getitem?mr=MR2556072">https://mathscinet.ams.org/mathscinet-getitem?mr=MR2556072</a>.</li> <li><strong>mr-msc</strong> space seperated list of <a href="https://zbmath.org/classification/">Mathemematics Subject Classifiction</a> labels created by MathSciNet staff.</li> </ul> <p>The dataset was retrieved in 2016 by querying MathSciNet and zbMATH Open. Therefore, the classifications are based on the MSC 2010 version.</p> <p>This dataset was used in</p> <p>Schubotz M., Scharpf P., Teschke O., Kühnemund A., Breitinger C., Gipp B. (2020) AutoMSC: Automatic Assignment of Mathematics Subject Classification Labels. In: Benzmüller C., Miller B. (eds) Intelligent Computer Mathematics. CICM 2020. Lecture Notes in Computer Science, vol 12236. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-53518-6_15">https://doi.org/10.1007/978-3-030-53518-6_15</a></p> <p>and is now released to the public as an addendum to the paper.</p>
Data for publication "Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action".
<p>Data underlying the publication "Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action".</p> <p>Journal: Nature Communications</p> <p>Authors: <em>Matthias Mengel<sup>1*</sup></em><em>, Alexander Nauels</em><sup><em>2</em></sup><em>, Joeri Rogelj</em><sup><em>3,4</em></sup><em>, Carl-Friedrich Schleussner<sup>1,5</sup></em></p> <p>(1) Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany</p> <p>(2) Australian-German College of Climate & Energy Transitions, The University of Melbourne, Parkville, Victoria 3010, Australia</p> <p>(3) ENE Program, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria</p> <p>(4) Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, Zurich 8006, Switzerland</p> <p>(5) Climate Analytics, Ritterstr. 3, 10969 Berlin, Germany</p> <p>(*) email matthias.mengel@pik-potsdam.de</p> <p>Abstract:</p> <p>Sea-level rise is a major consequence of climate change that will continue long after emissions of greenhouse gases have stopped. The 2015 Paris Agreement aims at reducing climate-related risks by reducing greenhouse gas emissions to net zero and limiting global-mean temperature increase. Here we quantify the effect of these constraints on global sea-level rise until 2300 including Antarctic ice-sheet instabilities. We estimate median sea-level rise between 0.7 and 1.2m if net zero greenhouse gas emissions are sustained until 2300, varying with the pathway of emissions during this century. Temperature stabilization below 2°C is insufficient to hold median sea-level rise until 2300 below 1.5m. We find that each 5-year delay in near-term peaking of CO2 emissions increases median year-2300 sea-level rise estimates by ca. 0.2m, and extreme sea-level rise estimates at the 95th percentile by up to 1m. Our results underline the importance of near-term mitigation action for limiting long-term sea-level rise risks.</p> <p> </p> <p>Large zip files provides data. Small zip file python code for plotting and writing supplementary data.</p>
Freshwater resources under success and failure of the Paris climate agreement
<p>This dataset represents the core output of the analysis presented in: Heinke, J., Müller, C., Lannerstad, M., Gerten, D., and Lucht, W.: Freshwater resources under success and failure of the Paris climate agreement, Earth Syst. Dynam., 10, 205-217, 10.5194/esd-10-205-2019, 2019. Please refer to this publication for a comprehensive description of methods and references to the datasets and materials used to produce this data.</p> <p>When using the data, cite it as follows: Heinke, Jens, Müller, Christoph, Lannerstad, Mats, Gerten, Dieter, & Lucht, Wolfgang (2019). Freshwater resources under success and failure of the Paris climate agreement [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2562056. Please also cite the reference article that this dataset belongs to.</p> <p> </p> <p>Files:</p> <ul> <li>frac_drought_19gcm_8gmt.nc contains the fraction of drought months for 19 GCM patterns and 8 levels of global mean temperature increase</li> <li>frac_drought_ref.nc contains the fraction of drought months for the reference case (equivalent to global mean temperature increase in 2009)</li> <li>mean_annual_discharge_19gcm_8gmt.nc contains mean annual discharge for 19 GCM patterns and 8 levels of global mean temperature increase</li> <li>mean_annual_discharge_ref.nc contains mean annual discharge for the reference case (equivalent to global mean temperature increase in 2009)</li> <li>q10_19gcm_8gmt.nc contains 5-day average peak flow exceeded in 1 of 10 years for 19 GCM patterns and 8 levels of global mean temperature increase</li> <li>q10_ref.nc contains 5-day average peak flow exceeded in 1 of 10 years for the reference case (equivalent to global mean temperature increase in 2009)</li> <li>water_crowding_1950-2010.nc contains estimates of grid-based water crowding for the historic period (1950-2010)</li> <li>water_crowding_2011-2100_5ssp.nc contains estimates of grid-based water crowding for the scenario period (2011-2100) for 5 different SSPs.</li> </ul> <p>All data have a spatial resolution of 0.5° x 0.5° and cover the global land area except Antarctica.</p> <p> </p> <p> </p> <p> </p>
Agreements between PAM 43662 and PAM 43697
<p>PAM 43662 displays 109 fragments of which 64 are also displayed on PAM 43697 (and 1 on PAM 43691). </p>
data set related to article Brainstem enlargement in preschool children with autism Results from an intermethod agreement study of segmentation algorithms
<p>This record contains raw data related to article Brainstem enlargement in preschool children with autism Results from an intermethod agreement study of segmentation algorithms</p>
Performance and agreement between WGS variant calling pipelines used for bovine tuberculosis control: towards international standardisation
<p>This repository contains the simulated genomes and FASTQ files used for the analyses reported in the publication: <strong>Performance and agreement between WGS variant calling pipelines used for bovine tuberculosis control: towards international standardisation</strong>.</p> <p>This dataset is based on previously published data: <a href="https://doi.org/10.1099/mgen.0.000388">https://doi.org/10.1099/mgen.0.000388</a></p> <p>Processing scripts can be found in: <a href="https://github.com/Viloleal/bTB-pipeline-comparison-data-and-tools">https://github.com/Viloleal/bTB-pipeline-comparison-data-and-tools</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.
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.