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Datasets of Listeria monocytogenes phenotypes colected in the EJP OH LISTADAPT project
<p>LISTADAPT datasets</p> <p>This repository gathered the phenotypic datasets in the LISTADAPT project (https://onehealthejp.eu/projects/jrp7-listadapt/)<br> It includes the Listeria monocytogenes phenotypic data associated to:<br> - biofilm formation<br> - MIC of antimicrobials<br> - Growth and survival in soil microsom<br> - Growth and survival in stressed conditions (culture medium)</p> <p>200 strains: 100 isolated in main RTE foods and 100 isolated from environment/farm/animals are studied.</p> <p>A versionning of the files is proposed, permitting to determine the more recent files in the repository. </p> <p> </p>
Relate-estimated coalescence rates, allele ages, and selection p-values for the 1000 Genomes Project
<p><strong>Overview</strong></p> <p>Coalescence rates, allele ages, and p-values for evidence of positive selection calculated for 2478 samples of the 1000 Genomes Project using Relate.</p> <p>We estimated the joint genealogy of all 1000 GP populations and then extracted the embedded genealogy for each population.<br> For the genealogy of each population, we jointly estimated the population size history and branch lengths. <br> Variants segregating in more than one population therefore have correlated but different allele ages in each population.</p> <p>Please refer to <a href="https://www.nature.com/articles/s41588-019-0484-x">Speidel et al. Nature Genetics (2019)</a> for more details or email leo.speidel@outlook.com for any queries.</p> <p><strong>Coalescence rates</strong></p> <p>The zipped directory coalescence_rates.zip contains coalescence rates for 26 populations in the 1000 Genomes Project data set.</p> <ul> <li>The .coal files show the haploid coalescence rates, please refer to the <a href="https://myersgroup.github.io/relate/modules.html#PopulationSizeScript_FileFormats">Relate documentation</a> for the file format.</li> <li>The popsize.RData file is an R data frame storing the diploid population sizes (0.5/coalescence rate) calculated using the .coal files. The columns of this data frame, named "pop_size", are <ul> <li>gens_ago: Time in generations at which epoch starts. (To get years from generations, we multiply by 28.)</li> <li>population_size: Diploid population size in this epoch.</li> <li>population: Name of population </li> <li>region: Name of region (AFR, AMR, EAS, EUR, SAS)</li> </ul> </li> </ul> <p><strong>Allele ages and selection p-values</strong></p> <p>The zipped directories allele_ages_*.zip contain R data frames for each 1000GP population storing allele ages and selection p-values.<br> Please note that only mutations that segregate in the population and map to a unique branch in the Relate-estimated marginal trees are included. Selection p-values are only provided for mutations of DAF > 2 that pass quality filters (see Speidel et al., 2019). </p> <p>To get an age estimate for a neutral mutation, use 0.5*(lower_age + upper_age). To get years from generations, we multiply by 28.</p> <p>The columns of these data frames, named "allele_ages", are</p> <ul> <li>CHR: chromosome index</li> <li>BP: base-pair position (GRCh37)</li> <li>ID: id of SNP</li> <li>lower_age: Age in generations of coalescence event at the lower end of the branch onto which the mutation maps</li> <li>upper_age: Age in generations of coalescence event at the upper end of the branch onto which the mutation maps</li> <li>ancestral/derived: Ancestral/derived allele</li> <li>upstream: Upstream (5') allele</li> <li>downstream: Downstream (3') allele</li> <li>DAF: Derived-allele frequency</li> <li>pvalue: log10 p-value for selection evidence</li> </ul>
Projection of potential future tree cover persistence for 2029 based on the global model
<p>Tree cover persistence projection results for 2029 based on the global model under a business-as-usual scenario.</p>
Projection of potential future tree cover persistence for 2029 based on the six regional models
<p>Tree cover persistence projection results for 2029 based on the six regional models under a business-as-usual scenario.</p>
Project STORM: monitoring the masonry of Hall I, at the Baths of Diocletian, with a prototype based on Arduino UNO. Dataset 2017 - 2019
<p>This dataset for monitoring the masonry of Aula I at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a prototype based on Arduino UNO, have been investigated:</p> <ul> <li>Temperature and Relative Humidity of the internal environment and Temperature and Relative Humidity of contact on the masonry (with two sensors DHT22 AM2302);</li> <li>Acceleration along the three spatial axes (X, Y and Z), Pitch Index and Roll Index (with Gy 521 sensor);</li> <li>Surface pressure for the movement of a lesion (with FlexiForxe sensor).</li> </ul> <p>The data produced by the sensors were acquired and sent to the computer which automatically saved them every 10 seconds. The dataset is composed of the data obtained from 2017 to 2019 and separated by month in 11 sheets. In total about 40 million values were recorded, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Project STORM: monitoring the masonry of Michelangelo's Cloister, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2018 - 2019
<p>This dataset for monitoring the masonry of Michelangelo's Cloister at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0 and S3);</li> <li>Temperature of masonry (sensors S1, S2 and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2018 to May 2019 and separated by month in 8 sheets. In total more than 2 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
PDF projections for the HL-LHC and the LHeC
<p>For the foreseeable future, the exploration of the high-energy frontier will be the domain of the Large Hadron Collider (LHC). Of particular significance will be its high-luminosity upgrade (HL-LHC), which will operate until the mid- 2030s. In this endeavour, for the full exploitation of the HL-LHC physics potential an improved understanding of the parton distribution functions (PDFs) of the proton is critical. </p> <p>Since its start of data taking, the LHC has provided an impressive wealth of information on the quark and gluon structure of the proton. Indeed, modern global analyses of parton distribution functions (PDFs) include a wide range of LHC measurements of processes such as the production of jets, electroweak gauge bosons, and top quark pairs. Here we provide <a href="https://arxiv.org/abs/1810.03639">quantitative projections for global PDF fits </a>that include the information expected from the High-Luminosity LHC (HL-LHC) in the LHAPDF format. These projections have been already used in particular in the <a href="https://arxiv.org/abs/1902.04070">HL-LHC CERN Yellow Reports</a>. </p> <p>The HL-LHC program would be uniquely complemented by the proposed Large Hadron electron Collider (LHeC), a high-energy lepton-proton and lepton-nucleus collider based at CERN. Here we also present PDF projections based on the expected LHeC measurements of inclusive and heavy quark structure functions. These projections are presented both for the LHeC individually, and also in connection with the HL-LHC pseudo-data.</p> <p>The list of <a href="https://lhapdf.hepforge.org/">LHAPDF sets </a>that is made available in this repository are the following:</p> <ol> <li><a href="https://zenodo.org/api/files/3f595d78-7657-42bb-939a-77db381bd185/PDF4LHC15_nnlo_hllhc_scen1_lhec.tgz">PDF4LHC15_nnlo_hllhc_scen1_lhec.tgz </a>: Based on the PDF4LHC15 global fit supplemented by HL-LHC pseudo-data in Scenario 1 and also by the LHeC pseudo-data.</li> <li><a href="https://zenodo.org/api/files/3f595d78-7657-42bb-939a-77db381bd185/PDF4LHC15_nnlo_hllhc_scen1_lhec.tgz">PDF4LHC15_nnlo_hllhc_scen2_lhec.tgz </a>: Based on the PDF4LHC15 global fit supplemented by HL-LHC pseudo-data in Scenario 2 and also by the LHeC pseudo-data.</li> <li><a href="https://zenodo.org/api/files/3f595d78-7657-42bb-939a-77db381bd185/PDF4LHC15_nnlo_hllhc_scen1_lhec.tgz">PDF4LHC15_nnlo_hllhc_scen3_lhec.tgz </a>: Based on the PDF4LHC15 global fit supplemented by HL-LHC pseudo-data in Scenario 3 and also by the LHeC pseudo-data.</li> <li><a href="https://zenodo.org/api/files/3f595d78-7657-42bb-939a-77db381bd185/PDF4LHC15_nnlo_lhec.tgz">PDF4LHC15_nnlo_lhec.tgz </a>: Based on the PDF4LHC15 global fit supplemented by the LHeC pseudo-data.</li> <li><a href="https://zenodo.org/api/files/3f595d78-7657-42bb-939a-77db381bd185/PDF4LHC15_nnlo_hllhc_scen1.tgz">PDF4LHC15_nnlo_hllhc_scen1.tgz</a> : Based on the PDF4LHC15 global fit supplemented by HL-LHC pseudo-data in Scenario 1. </li> <li><a href="https://zenodo.org/api/files/3f595d78-7657-42bb-939a-77db381bd185/PDF4LHC15_nnlo_hllhc_scen2.tgz">PDF4LHC15_nnlo_hllhc_scen2.tgz </a>: Based on the PDF4LHC15 global fit supplemented by HL-LHC pseudo-data in Scenario 2. </li> <li><a href="https://zenodo.org/api/files/3f595d78-7657-42bb-939a-77db381bd185/PDF4LHC15_nnlo_hllhc_scen3.tgz">PDF4LHC15_nnlo_hllhc_scen3.tgz </a>Based on the PDF4LHC15 global fit supplemented by HL-LHC pseudo-data in Scenario 3. </li> </ol>
STORM Project: monitoring environment conditions at Baths of Diocletian site (Rome, Italy). Dataset 2018 - 2019
<p>This dataset was created by the Engineering Ingegneria Informatica S.p.A. through a set of prototypes based on Libelium Waspmote for collecting the following parameters: </p> <ul> <li>Climate parameters (Temperature, Relative Humidity, Barometric Pressure, Luminosity, Wind direction/speed and Rainfull) using a Libelim PlugAndSense Agricolture Pro;</li> <li>Environmental Parameters (Monoxide Carbon, Oxigen, Air Polluction, Volatile Organic Compounds VOC, Carbon Dioxide, Nitric Dioxide , Hydrogen Sulfide, Sulfure Dioxide and Particle Matter PM 1, 2.5 and 10) using two nodes: PlugAndSense Smart Cities Pro and Waspmote with gases sensor board;</li> <li>Acoustic Noise Sensor and Vibrations with accelerometer, using a prototype based on Libelium Waspmote.</li> </ul> <p>The data produced by the sensors were acquired and sent to the Meshlium (mini-pc linux based) which automatically saved and sent to the STORM Platform. The dataset is composed of the data obtained from February 2018 to March 2019.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Project STORM: monitoring the masonry of Hall I, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2017 - 2019
<p>This dataset for monitoring the masonry of Hall I at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0, S2 and S3);</li> <li>Temperature of masonry (sensors S1, and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2017 to May 2019 and separated by month in 14 sheets. In total more than 4 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Smart Insurance datasets subset of the AEGIS project
<p>The example dataset was produced within the Smart Insurance demonstrator of the AEGIS project. It contains a sample of the SYNTHETIC data that have been created for the demonstrator purposes. </p>
Genome alignments for the project "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - Protocol optimization
<p>Genome alignments for data generated in the project "<em>Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol – Optimization of the protocol.</em>" Files names indicate unique identifiers of MOIRAI workflow runs, with the following structure: library name, dot, workflow ID (OP-WORKFLOW-CAGEscan-short-reads-v2.0.), dot, timestamp. The raw (FASTQ) data of each library is also deposited in Zenodo (<a href="https://doi.org/10.5281/zenodo.250156">10.5281/zenodo.250156</a>). Library names correspond to the following runs:</p> <ul> <li> NC33: 151007_M00528_0161_000000000-AEBDC</li> <li> NC37: 151204_M00528_0173_000000000-AEBEF</li> <li> NC38: 151211_M00528_0175_000000000-AE9PJ</li> <li> NC39: 160122_M00528_0185_000000000-AEB18</li> <li> NC42: 160302_M00528_0192_000000000-AELYK</li> </ul> <p>This data can be analysed using the "CAGEr" software package available from Bioconductor. The "multiplex_files.zip" file contains tables indicating which samples are biological replicates of each other or negative controls.</p>
Global river density, seasonal and surface water occurrence and upstream area at 250 m in the Goode Homolosine projection
<p>Several layers describing density of surface water / streams projected to the <a href="https://en.wikipedia.org/wiki/Goode_homolosine_projection">Good Homolosine projection</a>. List of layers included:</p> <ul> <li>hyd_log1p.upstream.area_merit.hydro_m = Upstream Drainage Area based on the <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro">MERIT Hydro</a>,</li> <li>hyd_river.density_gloric_p = rasterized <a href="https://www.hydrosheds.org/page/gloric">Global River Classification (GLORIC)</a> DB,</li> <li>lcv_water.occurance_jrc.surfacewater_p = Surface Water based on the JRC's <a href="https://global-surface-water.appspot.com/">Global Surface Water</a>,</li> <li>lcv_water.seasonal_probav.glc.lc100_p = Seasonal Inland Water probability based on the <a href="https://lcviewer.vito.be/">Copernicus LC100 map</a>,</li> <li>lcv_wetlands.cw_upmc.wtd_c = composite wetland (CW) map based on <a href="https://doi.org/10.1594/PANGAEA.892657">Tootchi et al. (2019)</a>,</li> <li>Goode_Homolosine_domain_250m.tif = map domain prepared by <a href="https://doi.org/10.5281/zenodo.1475152">Luís de Sousa</a>,</li> <li>tiles_GH_100km_land.gpkg = 100 km x 100 km tiling system covering the land mass,</li> </ul> <p>Important notes: Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/WaterDensity">here</a></strong>. Antartica is not included. Reprojecting maps to Goode Homolosine projection can be cumbersome and small amount of artifacts at the edges of the map can be anticipated.</p> <p>These maps were develop in connection to the <a href="http://www.OpenLandMap.org">OpenLandMap.org</a> initiative.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>hyd = theme: hydrology and water dynamics,</li> <li>log1p.upstream.area = variable: log(X+1)*10 of the upstream area,</li> <li>merit.hydro = determination method: MERIT Hydro,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: surface,</li> <li>2017 = time reference: period 2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Project files provided as supporting information to the manuscript "A deep learning approach to the structural analysis of proteins"
<p><strong>README file to the project files provided as supporting information to the manuscript “A deep learning approach to the structural analysis of proteins”</strong></p> <p>Dec. 30, 2018</p> <p>Authors: Marco Giulini and Raffaello Potestio</p> <p>==================================</p> <p>The dataset contains the following files:</p> <p> </p> <p>- datasets.zip: archive containing five .csv files, namely:</p> <p> - decoys_cm.csv : all the data for 10728 protein decoys, training set</p> <p> - evaluation_cm.csv : all data for 146 proteins in the evaluation set</p> <p> - random_CG.csv : 1200 Coulomb matrices. 100 CG models for each protein with 120 amino acids</p> <p> - 1e5g_centered_sphere.csv : 100 CG models in which the central atoms in 1e5g are not removed</p> <p> - 1e5g_random_sphere.csv : 10 CG models for 10 different (random) locations for the sphere that includes atoms that have to be retained. 100 CG models in total</p> <p> </p> <p>- decoys_labels.lab containing the labels associated to the 10728 decoys present in the training set</p> <p>- evaluation_labels.lab containing the labels associated to the 146 pdb files in the evaluation set</p> <p>- random_CG_labels.lab containing the labels associated to the 6 proteins with 120 amino acids</p> <p>- network_development_training: a python script that performs cross validation and full training of the model</p> <p>- saved_networks.zip FOLDER containing 10 networks: the architecture is included in .json files while weight parameters are inside .hs files</p> <p> </p> <p>- pdb_files.zip FOLDER containing the PDB files that have been employed in the project, namely:</p> <p> - pdb_files_len100 : pdb files with 100 amino acids</p> <p> - pdb_files_len101-110 : pdb files with a number of amino acids between 101 and 110</p> <p> - decoys : decoys of length 100 extracted from the above folder: name syntax == PDBNAME_decoy_STARTRES_ENDRES.pdb</p> <p> EXAMPLE 6gsp.pdb will give rise to 6gsp_decoy_0_100.pdb , 6gsp_decoy_1_101.pdb , 6gsp_decoy_2_102.pdb , 6gsp_decoy_3_103.pdb , 6gsp_decoy_4_104.pdb</p> <p> - pdb_files_len100 : 6 pdb files with 120 amino acids</p> <p> </p>
Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence
<p>This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication:</p> <p>Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. <em>GeoHealth</em>. </p> <p>The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario. </p> <p>Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3. </p>
PERCEIVE: WP3: Effectiveness of communication strategies of EU projects
<p>This data set contains all the relevant data referred to PERCEIVE WP3, as tasks within WP3 are logically connected. Data analyzed within WP3, but collected in WP5, are included in the data set “<em>PERCEIVE: WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels</em>” (<a href="http://doi.org/10.5281/zenodo.1038041">http://doi.org/10.5281/zenodo.1038041</a>). Data analyzed in Task3.1 are mostly based on transcripts already included in the data set “<em>PERCEIVE. WP1. Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries. Task1.2. Focus group with Cohesion Policy practitioners</em>” (focus groups and other interviews).</p> <p>Task3.2 data are the results of a European wide online survey (at the moment this document is being compiled, the website of the survey is no longer online) targeting policy communicators and focused on three strategic aspects of communicating policy: a) factors of success and barriers, b) support from central institutions and c) communication mix and storytelling.</p> <p>Task3.3 data regard the analysis of social media communication from EU communication offices at both local and European level. Data covers a sentiment analysis performed on the Facebook homepages of Local Management Authorities (LMA) of PERCEIVE case study regions as well as twitter networks and timelines for international accounts and hashtags. </p> <p>Task3.4 data contain elements used in the statistical modeling of communication efforts (see Deliverable 3.4, <a href="http://doi.org/10.6092/unibo/amsacta/6111">http://doi.org/10.6092/unibo/amsacta/6111</a> or <a href="http://doi.org/10.5281/zenodo.1318144">http://doi.org/10.5281/zenodo.1318144</a>). Data cover the algorithmic clustering of topics detected in Task5.3, data derived from the PERCEIVE survey, the code (R programming environment) used to run regression analyses, as well as the results of the analyses themselves.</p> <p>Task3.5 data refer to secondary publicly available data. Namely the collection of the PANORAMA magazine available at INFOREGIO, the Directorate of Regional Policy web portal (<a href="https://ec.europa.eu/regional_policy/en/information/publications/panorama-magazine/">https://ec.europa.eu/regional_policy/en/information/publications/panorama-magazine/</a>), and Eurobarometer data on “awareness” available at the Open Data Portal of the EC (<a href="http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm">http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm</a>). The textual content of PANORAMA magazine has been content analyzed and the results are made available as a .csv table of concepts’ frequencies per magazine issue.</p>
Toolkit on Open Access for Research Project Coordinators
<p>The materials in this toolkit were created by Romain Féret as a resource for training on how to help project coordinators to comply with their open access requirements. The slides of the training are available on Zenodo at 10.5281/zenodo.3381783. This training day took place on Wednesday the 5th of June 2019, at the University of Lille. It was organized with the support of Couperin as a part of its activities in the project OpenAIRE-Advanced.</p> <p>The tutorials are divided into two folders. The ‘Coordinator’ folder contains documents that can be sent directly to the researchers, while the ‘Support staff’ folder contains tutorials for support staff (librarians, project managers) who help the coordinators to manage their project. Each tutorial is in .pdf and .docx format for easy reuse and modification. Each document is available in French and in English.</p>
Model outputs: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)
<p>This dataset contains the fire model outputs of emissions for 34 species (elements, compounds, and classes of compounds) as described in the following:</p> <p>Li, F., Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S.: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP), <em>Atmos. Chem. Phys. Discuss.</em>, https://doi.org/10.5194/acp-2019-37, accepted pending technical corrections, 2019.</p> <p>See Readme for more information.</p>
Extended data of the project "A survey exploring biomedical editors' perceptions of editorial interventions to improve adherence to reporting guidelines"
<p>Figure S1: Survey questionnaire</p> <p>Table S2: Barriers, facilitators and possible improvements of the interventions included in the survey</p>
Citizen Science projects in Argentina
<p>Citizen science activities recognized in Argentina.</p> <p>The code for the Actions column are: </p> <ul> <li>e -Collect <p>c - Hypothesis design</p> <p>d - Design collection strategies</p> f - Sample analysis</li> <li>g - Data analysis <p>h - Generate conclusions</p> <p>j - Generate new questions</p> k - Digitalization <p>i - Disseminate conclussions</p> </li> </ul>
ENABLE.EU H2020 project dataset and questionnaire from a survey of households on energy use and energy choices
<p>The ZIP archive includes the anonymized micro-data (survey results) and the respective questionnaire from the survey of households in eleven countries, conducted as part of the H2020 project "<a href="http://www.enable-eu.com">Enabling the Energy Union through understanding the drivers of individual and collective energy choices in Europe</a>" (ENABLE.EU). </p> <p>The countries are: Bulgaria, France, Germany, Hungary, Italy, Norway, Poland, Serbia, Spain, Ukraine, and the United Kingdom.</p> <p>The dataset consists of 11 267 completed questionnaires (cases). </p> <p>The ZIP archive includes the following files:<br> • ENABLE.EU survey questionnaire for households in PDF format;<br> • ENABLE dataset from the survey of households in SAV format for IBM SPSS;<br> • ENABLE dataset from the survey of households in DTA format for STATA (the dataset is produced by simple export from SAV format and could contain some differences due to export limitations; If possible, we recommend to use the SAV-SPSS format);<br> • ENABLE dataset from the survey of households in XLSX format for Microsoft Excel, which includes also corresponding tables for the labels of questions and answers.</p> <p>For more information about the survey methodology and survey results please see: "D4.1 Final report on comparative sociological analysis of the household survey results" under the section <a href="http://www.enable-eu.com/downloads-and-deliverables/">Downloads / Deliverables</a> at the ENABLE.EU web-site. </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.