Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11,198
datasets available to search
ShareScore release 0.9.0
Dataset results
11,198 results for “organ”
HYPOTHALAMIC AXIS- HYPOPHYSIS- ORGAN DIANA.
<pre>This image represents the target organ pituitary hypothalamic axis, along with the respective feedback loops.</pre>
Supplementary data: The added value of Bayesian inference for estimating biotransformation rates of organic contaminants in aquatic invertebrates.
<p>Supporting information for the article "<strong>The added value of Bayesian inference for estimating biotransformation rates of organic contaminants in aquatic invertebrates.</strong>"</p> <p>This provides all the R script and .csv files for each dataset. </p>
Reference Dataset for Benchmarking Organ Doses Derived from Monte Carlo Simulations of CT Exams
<p>This reference dataset contains CT scanner x-ray source characteristics, filtration profile, de-identified patient image data and size characteristics, voxelized patient models, exam characteristics, x-ray tube current data, and organ dose results in tabular form from Monte Carlo (MC) simulations of abdominal/pelvis CT exams of pregnant patients. This dataset can be used for benchmarking MC simulation codes for CT dosimetry.</p>
DFT-optimized Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014
<p>There are two folders inside the zipped file:</p> <p>- 838 structures (without DDEC partial atomic charges)</p> <p>- 502 structures (with DDEC partial atomic charges)<br> </p> <p> </p>
Data from: Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon
<p>In the tundra, woody plants are dispersing towards higher latitudes and altitudes due to increasingly favourable climatic conditions. The coverage and height of woody plants are increasing, which may influence the soils of the tundra ecosystem. Here, we use structural equation modelling to analyse 171 study plots and to examine if the coverage and height of woody plants affect the growing-season topsoil moisture and temperature (< 10 cm) as well as soil organic carbon stocks (< 80 cm). In our study setting, we consider the hierarchy of the ecosystem by controlling for other factors, such as topography, wintertime snow depth and the overall plant coverage that potentially influence woody plants and soil properties in this dwarf-shrub dominated landscape in northern Fennoscandia. We found strong links from topography to both vegetation and soil. Further, we found that woody plants influence multiple soil properties: the dominance of woody plants inversely correlated with soil moisture, soil temperature, and soil organic carbon stocks (standardised regression coefficients = -0.39; -0.22; -0.34, respectively), even when controlling for other landscape features. Our results indicate that the dominance of dwarf shrubs may lead to soils that are drier, colder, and contain less organic carbon. Thus, there are multiple mechanisms through which woody plants may influence tundra soils.</p> <p>Kemppinen, Niittynen, Virkkala, Happonen, Riihimäki, Aalto & Luoto (2021). Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon. Ecosystems.</p> <p>These are the data from Kemppinen et al. (2021).</p>
Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa
<p>Link to scientific publication: <a href="https://doi.org/10.1016/j.scitotenv.2021.145384">https://doi.org/10.1016/j.scitotenv.2021.145384</a></p> <p>Soil organic carbon (SOC) stocks (kg C m-2) are predicted over natural areas (excluding water, urban, and cultivated) of South Africa using a machine learning workflow driven by optical satellite data and other ancillary climatic, morphometric and biological covariates. The temporal scope covers 1984-2019. The spatial scope covers 0-30cm topsoil in South Africa natural land area (84% of the country). See methodology in linked publication for details. Data are provided here at 30m spatial resolution in GeoTIFF files. There is a dataset for the long-term average SOC and trend in SOC. Each dataset is split into four files (suffix *_1, *_2 etc.) covering separate regions of South Africa for ease of download. The raster files are:</p> <ul> <li>"SOC_mean_30m..." - average of annual SOC predictions between 1984 and 2019. Values are expressed in kg C m-2</li> <li>"SOC_trend_30m..." - long-term trend in SOC derived from the Sens slope (M) across annual SOC values between 1984 and 2019. Pixel values (Y) are expressed as a percentage change over the 35 years relative to the long-term mean (X). Y = M / X * 100 * 35 years</li> </ul> <p>NB: All files are scaled by *100 and converted to floating data point to save space. To back-convert to original values, simply divide the raster values by 100.</p>
Supplementary data for "The subgenual organ complex in stick insects: Functional morphology and mechanical coupling of a complex mechanosensory organ"
<p>µCT-scans of the upper tibial regions of the foreleg (T1) and the midleg (T2) of <em>Ramulus artemis</em> (Westwood, 1859), <em>Carausius morosus</em> (Sinéty, 1901), and <em>Sipyloidea sipylus</em> (Westwood, 1859). For use of scans, please cite the following publication:</p> <p>Strauß, J., Moritz, L. & Rühr, P.T. (<strong>2021</strong>): The subgenual organ complex in stick insects: Functional morphology and mechanical coupling of a complex mechanosensory organ. <em>Frontiers in Ecology and Evolution (Research Topic “Evolutionary Biomechanics of Sound Production and Reception”)</em>. doi: <a href="https://doi.org/10.3389/fevo.2021.632493">10.3389/fevo.2021.632493</a>.</p> <p>All scans were performed with a commercial μCT desktop system (Skyscan 1272, Bruker microCT, Kontich, Belgium) at the Zoological Research Museum Alexander Koenig, Leibniz Institute for Animal Biodiversity, Bonn, Germany.</p> <p><strong>µCT scan settings of all samples:</strong></p> <p><em>Ramulus artemis:</em></p> <ul> <li>tube voltage = 30 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1980 ms</li> <li>binning = 1x1</li> <li>averaging = 8</li> <li>random movement = 15 px</li> <li>voxel size = 1.8 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Ramulus_artemis_T1.tif; Ramulus_artemis_T2.tif</li> </ul> <p><em>Carausius morosus:</em></p> <ul> <li>tube voltage = 29 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1900 ms</li> <li>binning = 1x1</li> <li>averaging = 5</li> <li>random movement = 15 px</li> <li>voxel size = 1.0 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Carausius_morosus_T1.tif; Carausius_morosus_T2.tif</li> </ul> <p><em>Sipyloidea sipylus:</em></p> <ul> <li>tube voltage = 29 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1900 ms</li> <li>binning = 1x1</li> <li>averaging = 7</li> <li>random movement = 15 px</li> <li>voxel size = 1.8 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Sipyloidea_sipylus_T1.tif; Sipyloidea_sipylus_T2.tif</li> </ul>
Linguistic praxeological organization of French as the schooling language
<p>Elements of the linguistic praxeological organization of French as the schooling language used to produce the framework (https://zenodo.org/deposit/4462850) used for the PEAPL project (https://blog.hepfr.ch/create/peapl/) and the framework used for the COMPER project (https://comper.fr/accueil).</p> <p>The following article will give explanations about the elaboration of the linguistic praxeological organizations: https://www.researchgate.net/publication/333244876_Francais_langue_de_scolarisation_Reflexions_sur_les_referentiels_de_competences_et_l'adaptive_learning</p>
Supporting Data: Complementary Organic Logic Gates on Plastic Formed by Self-Aligned Transistors with Gravure and Inkjet Printed Dielectric and Semiconductors
<p>The file contains the supporting data for the publication:</p> <p>S.G. Higgins, B.V.O. Muir, G. Dell'Erba, A. Perinot, M. Caironi, A.J. Campbell. Complementary Organic Logic Gates on Plastic Formed by Self-Aligned Transistors with Gravure and Inkjet Printed Dielectric and Semiconductors. doi: 10.1002/aelm.201500272. <em>Advanced Electronic Materials </em>(2015)</p> <p>See 'README.txt' for a description of the contents of the compressed file.</p>
Supporting Data: Indacenodithiophene-benzothiadiazole Organic Field-Effect Transistors with Gravure Printed Semiconductor and Dielectric on Plastic
<p>The file contains the supporting data for the publication:</p> <p>S.G. Higgins, B.V.O. Muir, M. Heeney, A.J. Campbell. Indacenodithiophene-benzothiadiazole Organic Field-Effect Transistors with Gravure Printed Semiconductor and Dielectric on Plastic. doi: 10.1557/mrc.2015.66. <em>MRS Communications</em> (2015)</p> <p>See 'README.txt' for a description of the contents of the compressed file.</p>
Supporting Data: Self-Aligned Organic Field-Effect Transistors on Plastic with Picofarad Overlap Capacitances and Megahertz Operating Frequencies
<p>The file contains the supporting data for the publication:</p> <p>S.G. Higgins, B.V.O. Muir, G. Dell'Erba, A. Perinot, M. Caironi, A.J. Campbell. Self-Aligned Organic Field-Effect Transistors on Plastic with Picofarad Overlap Capacitances and Megahertz Operating Frequencies. doi: 10.1063/1.4939045. <em>Applied Physics Letters</em> (2016)</p> <p>See 'README.txt' for a description of the contents of the compressed file.</p>
Data licences and organization type of contributors to the Global Biodiversity Information Facility as of 19 January 2016
<p>Data from the Global Biodiversity Information Facility were extracted using R (version 3.2.0) on 9 July 2015 using the rgbif package (version 0.9.0) (Chamberlain, S., Ram, K., Barve, V. & Mcglinn, D. (2015) Package ‘rgbif’: Interface to the Global 'Biodiversity' Information Facility 'API' http://cran.r-project.org/web/packages/rgbif/rgbif.pdf). The ‘rights’ statements was extracted for all occurrence datasets with one or more observations. A total of 12,458 datasets were extracted, but only about 11% of the datasets have an explicit data-useage-rights statement at the dataset level. However, some datasets use the occurrence level ‘rights’ and ‘accessRights’ fields. To extract these data the rights information was obtained from the first record of each dataset where a rights statement was missing at the dataset level.</p> <p>The datasets were categorized into 13 different types depending on the origin of the observations.</p> <ol> <li>Biodiversity Information Facility or data centre</li> <li>Botanical Garden or Herbarium</li> <li>Citizen science</li> <li>Commercial</li> <li>Data publisher</li> <li>Educational</li> <li>Government</li> <li>Museum</li> <li>Network</li> <li>Parks Authority or Nature Reserve</li> <li>Research institution</li> <li>Society</li> <li>Foundations</li> </ol>
Strongly Enhanced Cooperative Surface Propensity of Atmospherically Relevant Organic Molecular Ions in Aqueous Solution - data
<p>Dataset pertaining to the manuscript "Boosting aerosol surface effects: strongly enhanced cooperative surface propensity of atmospherically relevant organic molecular ions in aqueous solution", published in <a href="https://doi.org/10.5194/acp-25-3503-2025">Atmos. Chem. Phys., 25, 3503–3518, 2025</a>. Using liquid-jet photoelectron spectroscopy, we investigate the surface propensity of various carbonaceous species in aqueous solution. We cover a range of substances relevant to atmospheric climate models. Here we give the data of Fig.s 1-3 of our manuscript in numeric form, and document the underlying photoemission spectra including all relevant metadata.</p> <p>Experimental data are documented in the NeXus format (extension .nxs). For a description see:<br>The NeXus Data Format definition (v2024.02), https://manual.nexusformat.org/index.html<br>NXmpes expansion for FAIRmat data (v.2024.07), https://fairmat-nfdi.github.io/nexus_definitions/classes/contributed_definitions/NXmpes.html<br>NXmpes_liquid expansion to NXmpes (v.2024.07), https://fairmat-nfdi.github.io/nexus_definitions/mpes-liquid/classes/contributed_definitions/NXmpes_liquid.html</p> <p>The following files are provided:<br>'Data Collection_Core.nxs' - Photoemission data, core level spectra<br>'Data Collection_Valence.nxs'<strong> </strong> - Photoemission data, valence spectra</p> <p>Ascii data of figures 1a, 2 and 3:<br>'Figure 1 data.txt'<br>'Figure 2 data.txt'<br>'Figure 3 data.txt'</p> <p>Contact person for questions regarding this data set: Uwe Hergenhahn, uhe@fhi.mpg.de . If you use these data for your scientific work we kindly ask you to send us a copy of your published results.</p> <p>Acknowledgements: We acknowledge DESY (Hamburg, Germany), a member of the Helmholtz Association HGF, for the provision of experimental facilities. Parts of this research were carried out at PETRA III, and we would like to thank Moritz Hoesch and his team for assistance in using beamline P04. Beamtime was allocated for proposal I-20220937 EC. Harmanjot Kaur and Bernd Winter acknowledge the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 883759, AQUACHIRAL). Stephan Thürmer acknowledges support from JSPS KAKENHI (grant no. JP20K15229) and ISHIZUE 2024 of Kyoto University. Florian Trinter acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project 509471550, Emmy Noether Programme. Florian Trinter and Bernd Winter acknowledge support by the MaxWater initiative of the Max-Planck-Gesellschaft. Olle Björneholm acknowledges support from the Swedish Research Council (VR) through project 2023-04346 and the Swedish Foundation for International Cooperation in Research and Higher Education (STINT) through project 202100-2932. Ricardo Marinho, Joel Pinheiro, and Arnaldo Naves de Brito acknowledge support from the Swedish–Brazilian collaboration STINT-CAPES (process no. 88881.465527/2019-01). Arnaldo Naves de Brito acknowledges support from FAPESP (the São Paulo Research Foundation, process no. 2017/11986-5), Shell and ANP (Brazil’s National Oil, Natural Gas and Biofuels Agency), and CNPq-Brazil (process no. 401581/2016-0). Harmanjot Kaur and Shirin Gholami acknowledge support by the IMPRS for Elementary Processes in Physical Chemistry.</p> <p>Financial support: This research has been supported by the European Research Council, Horizon Europe (grant no. 883759); the Japan Society for the Promotion of Science (grant no. JP20K15229); the Deutsche Forschungsgemeinschaft (grant no. 509471550); the Vetenskapsrådet (grant no. 2023-04346), the Swedish Foundation for International Cooperation in Research and Higher Education (grant no. 202100-2932); the Fundação de Amparo à Pesquisa do Estado de São Paulo (grant no. 2017/11986- 5); and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (grant no. 401581/2016-0).</p> <p>Version history:<br>1 - initial release<br>2 - numbering of figures adapted to published version, photoemission data added.</p>
Alteromonas Digital Organism Databases
<p>This is the home of the database for the <i>Alteromonas</i> Digital Organism, one of C-CoMP's collaborative efforts. This database was created in anvi'o (anvio-dev) primarily by Michelle DeMers (Massachusetts Institute of Technology) and Rogier Braakman (Massachusetts Institute of Technology), with significant help from members of the Meren Lab (A. Murat Eren, Iva Veseli, and Matthew Schechter) and Moran Lab (Zac Cooper and Mary Ann Moran). This version upload consists of:</p><p>Alteromonas_Pangenome_v2.1.1.md: A reproducible workflow that details the additions made to this version of the pangenome since v2.1.0.</p><p>Alteromonas2.1.1dbs.tar.gz: Collection of all updated contigs databases and genomes storage database.</p><p>external-genomes-v2.txt: Text file consisting of a list of the genomes used in this digital organism with ID, strain, and source information.</p><p>Alteromonas2.1.1pangenome.db.tar.gz: Compressed file containing the <i>Alteromonas</i> pangenome (digital organism).</p><p>Alteromonas2.1.1pangenomefiles.tar.gz: Compressed directory containing any information that anvi'o created when forming the pangenome.</p><p>Alteromonas2.1.1ANI.tar.gz: Compressed directory containing all output files from assessing genome similarity.</p><p>bayesian_2_1_concatenated_proteins*: Concatenated core protein files made from bayesian core gene sets.</p><p>RAxML_*boots: New RAxML tree artifacts produced for this version of the pangenome, with bootstrap values. Includes midpoint rooted tree.</p><p>layer_orders.txt: Tab-delimited file used to import newick tree into the pangenome.</p><p>view.txt: Tab-delimited file containing isolate and strain names.</p>
SONAR -- experimental redox potentials for organic compounds undergoing 2-electron/2-proton transfer reactions
<p>reference data for the demo-compounds used as input for predicting redox potentials by a trained model </p><p>The file</p><ul><li>lists redox potentials and oxidized/reduced form for organic molecules undergoing a two-electron/two-proton reduction reaction (M + 2 e- + 2 H+ --> MH2)</li><li>contains data for 25 organic compounds compiled from various sources in literature</li><li>uses "|" as a separator</li><li>column names and explanations<ol><li><strong>ID</strong>: abbreviated trivial names e.g. for labelling</li><li><strong>orig redox potential [V]:</strong> original values reported in respective reference</li><li><strong>solvent</strong>: total formula, water (H2O) throughout</li><li><strong>pH</strong>: pH value of electrolyte solution. If not reported, inferred from the concentration of supporting electrolyte</li><li><strong>supporting_electrolyte</strong>: if spefified: total formula, if available; concentration</li><li><strong>SMILES_ox</strong>: molecular structures encoded as (manually assigned) SMILES strings for the oxidized species (M)</li><li><strong>SMILES_red</strong>: molecular structures encoded as (manually assigned) SMILES strings for the reduced species (MH2)</li><li><strong>ref_electrode:</strong> reference electrode the originally reported half cell potential refers to. If not specified, RHE was used as default</li><li><strong>redox potential vs SHE [V]</strong>:<ul><li>In case of missing information, reversible hydrogen electrode (RHE at pH = 0) was assumed, which corresponds to SHE</li><li>In case of conflicting entries (SHE and pH != 0), we assumed the pH should be accounted for and replaced "RHE" as reference electrode instead of "SHE". "NHE" was treated like "RHE".</li><li>In case the reference electrode was other than SHE, NHE or RHE, a respective offset was added. This was the case once for Ag/AgCl (assuming saturated solution, offset = 0.210, see respective reference)</li><li>Finally, the potential values were transferred to SHE according to: E(SHE) = E(RHE) + 0.05913 * pH</li><li>CAVEAT: Lacking information about individual pKa values, no other correction was made.</li></ul></li><li><strong>reference</strong>: orginal source</li></ol></li></ul>
Database to: Cover crops affect pool specific soil organic carbon in cropland – A meta‐analysis
<p>Database to a meta-analysis studying the effects of cover crops on the mineral-associated organic carbon pool (MAOC), the particulate organic carbon pool (POC) and the microbial biomass carbon pool (MBC). Consists of:<br>1. information on the database<br>2. legend<br>3. list of included studies, all extracted data necessary for response ratio calculation and moderator analysis, and additional information</p>
Census of the Ecosystem of Decentralized Autonomous Organizations
<p>The dataset includes data from various Decentralized Autonomous Organizations (DAOs) platforms, namely Aragon, DAOHaus, DAOstack, Realms, Snapshot and Tally. DAOs are a new form of self-governed online communities deployed in the blockchain. DAO members typically use <em>governance tokens</em> to participate in the DAO decision-making process, often through a voting system where members submit proposals and vote on them.</p> <p>The description of the methods used for the generation of data, for processing it and the quality-assurance procedures performed on the data can be found here:<br><a href="https://doi.org/10.1145/3589335.3651481">https://doi.org/10.1145/3589335.3651481</a></p> <ul> <li>Recommended citation for this dataset:<br>Peña-Calvin, A., Arroyo, J., Schwartz, A., & Hassan, S. (2024). Concentration of Power and Participation in Online Governance: the Ecosystem of Decentralized Autonomous Organizations. Companion Proceedings of the ACM Web Conference, 13–17, 2024, Singapore, doi: <a href="https://doi.org/10.1145/3589335.3651481">https://doi.org/10.1145/3589335.3651481</a></li> </ul> <p>The dataset comprises three CSV files: deployments.csv, proposals.csv, and votes.csv, each containing essential information regarding DAOs deployments, their<br>proposals, and the corresponding votes.</p> <ul> <li>The file deployments.csv provides insights into the general aspects of DAO deployments, including the platform it is deployed in, the number of proposals, unique voters, votes cast, and estimated voting power.</li> <li>The proposals.csv file contains comprehensive information about all proposals associated with the deployments, including their date, the number of votes they received, and the total voting power voters employed on that proposal.</li> <li>In votes.csv, data regarding the votes cast for the deployment proposals is recorded. It includes the voter's blockchain address, the vote's weight in voting power, and the day it was cast.</li> </ul>
Polarized, color-selective and semi-transparent organic photodiode of aligned merocyanine H-aggregates
<p>Data to report <a href="https://doi.org/10.1039/D4TC00678J">https://doi.org/10.1039/D4TC00678J</a>:</p> <p><span><span>Highly anisotropic thin films of H-type coupled dipolar merocyanines </span></span><span><span>with large dichroic ratios of over 50 </span></span><span><span>were </span></span><span><span>deposited by solution shearing. These layers were incorporated into simultaneously color- and polarization-selective organic photodiodes. Using a transparent non-fullerene acceptor, polarization-sensitive planar-heterojunction devices with an average visible transmittance of 93% were obtained.</span></span></p> <p> </p>
Data and Code for "Does Organic Farming Jeopardize Food Security of Farm Households in Benin?"
<p>This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "<a href="https://doi.org/10.1016/j.foodpol.2024.102622" target="_blank" rel="noopener">Does Organic Farming Jeopardize Food Security of Farm Households in Benin?</a>" authored by Ghislain B.D. Aïhounton and Arne Henningsen and published in the journal Food Policy (Volume 124, April 2024, 102622, DOI: 10.1016/j.foodpol.2024.102622).</p> <p>We conducted the empirical analysis with the "R" statistical software (version 4.3.3) using the add-on packages "AER" (version 1.2.12), "DescTools" (version 0.99.54), "lmtest" (version 0.9.40), "moments" (version 0.14.1), "sandwich" (version 3.1.0), "stargazer" (version 5.2.3), and "xtable" (version 1.8.4) that are all available at CRAN.</p> <p>This replication package contains the following files:</p> <p>* README<br>This file.</p> <p>* R/dataBenin.csv<br>A CSV file that contains the (unprepared) data set. The variables in this file are described in file R/Variables.csv. This CSV file is imported by R script PrepareDataFoodNutrition.R.</p> <p>* R/Variables.csv<br>A CSV file that describes the variables in the (unprepared) data set (file R/dataBenin.csv).</p> <p>* R/PrepareData.R<br>An R script that imports the (unprepared) data set (file R/dataBenin.csv), calculates additional variables and add theses variables to the data set, removes observations that should not be used in the empirical analysis, and saves the prepared data set as CSV file (R/dataFoodNutrition.csv).</p> <p>* R/dataPrepared.csv<br>A CSV file that contains the (prepared) data set used in the empirical analysis. This CSV file is created by the R script R/PrepareDataFoodNutrition.R. It is imported by the R scripts R/DescriptiveTab.R, FoodNutritionImpact.R, and GridSearchFoodSecurity.R.</p> <p>* R/DescriptiveTab.R<br>An R script that imports the prepared data set (file R/dataFoodNutrition.R) and creates Table 1 of the paper ("Descriptive statistics", file paper/tables/DescriptiveStat.tex) as LaTeX file.</p> <p>* R/Estimations.R<br>An R script that imports the prepared data set (file R/dataFoodNutrition.R), conducts all the analyses presented in the paper, creates Tables 2 and 3 of the paper ("OLS and IV regression results of the conditional associations between organic farming and outcomes" and "OLS and IV regression results of the conditional associations between organic farming and mediating outcomes", LaTeX files paper/tables/estMainReg.tex and paper/tables/estMedReg.tex), creates Figures 1 and 2 of the paper ("Estimated conditional associations of organic farming with outcomes" and "Estimated conditional associations of organic farming with mediating outcomes", 12 PDF files paper/figures/*.pdf), and 45 Tables that are included in the Supplementary Information: 36 tables with detailed regression results (LaTeX files paper/tables/tabels/est*.tex), one table with results of the first-stage probit regression (LaTeX file paper/tables/tabels/estProbit.tex), 6 tables with detailed regression results of estimations for testing the exogeneity of the instrument as suggested by Di Falco et al. (2011) (LaTeX files paper/tables/tabels/estOLS*Falco.tex), and 2 tables with coefficient bounds obtained as suggested by Oster (2019) (LaTeX files paper/tables/tabels/Oster*.tex).</p> <p>* R/GridSearch.R<br>An R script that re-runs our regression analyses with different units of measurement of IHS-transformed variables and calculates various indicators that can can be used to assess the appropriateness of different units of measurement as suggested by Aihounton and Henningsen (2021) and that creates 28 Tables that are included in the Supplementary Information (LaTeX files paper/tables/tabels/grid*.tex).</p> <p>* R/functions/calcOsterBounds.R<br>An R script that defines the R function calcOsterBounds() that calculates coefficient bounds using the method suggested by Oster (2019). This function is used by the R script R/FoodNutritionImpact.R.</p> <p>* R/functions/calcSemiElaOrg.R<br>An R script that defines the R function calcSemiElaOrg() that calculates the semi-elasticity of various log-transformed or IHS-transformed variables with respect to the dummy variable for organic farming. This function is used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/createFormula.R<br>An R script that defines the R function createFormula() that creates the regression formulas for the various empirical analyses that are presented in the paper. This function is used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/functionsTables.R<br>An R script that defines various R functions that are used to create tables in LaTeX format. These functions are used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/predR2.R<br>An R script that defines the R function predR2() that calculates the predictive R-squared value. This R script has been obtained from the replication package of the article:<br>Aïhounton, G. B. D. and Henningsen, A. (2021). Units of measurement and the inverse hyperbolic sine transformation. The Econometrics Journal, 24(2):334–351. https://doi.org/10.1093/ectj/utaa032<br>The function consists of a slightly modified version of the code that is available at: https://tomhopper.me/2014/05/16/can-we-do-better-than-r-squared/ This function is used by the R script R/GridSearchFoodSecurity.R.</p> <p>* paper/figures/*.pdf<br>12 LaTeX files that are the (sub)figures in Figures 1 and 2 of the paper ("Estimated conditional associations of organic farming with outcomes" and "Estimated conditional associations of organic farming with mediating outcomes"). These 12 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/DescriptiveStat.tex<br>A LaTeX file that creates Table 1 of the paper ("Descriptive statistics"). This file is created by the R script R/DescriptiveTab.R.</p> <p>* paper/tables/estMainReg.tex<br>A LaTeX file that creates Table 2 of the paper ("OLS and IV regression results of the conditional associations between organic farming and outcomes"). This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/estMedReg.tex<br>A LaTeX file that creates Table 3 of the paper ("OLS and IV regression results of the conditional associations between organic farming and mediating outcomes"). This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/est*.tex<br>36 LaTeX files that create 36 tables that are included in the Supplementary Information and present detailed regression results. These 36 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/estProbit.tex<br>A LaTeX files that creates a table that is included in the Supplementary Information and presents the results of the first-stage probit regression. This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/estOLS*Falco.tex<br>6 LaTeX files that create 6 tables that are included in the Supplementary Information and present detailed regression results for testing the exogeneity of the instrument as suggested by Di Falco et al. (2011). These 6 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/Oster*.tex<br>2 LaTeX files that create 2 tables that are included in the Supplementary Information and present coefficient bounds obtined as suggested by Oster (2019). These 2 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/grid*.tex<br>28 LaTeX files that create 28 tables that are included in the Supplementary Information and present various indicators for assessing the appropriateness of different units of measurement of IHS-transformed variables as suggested by Aihounton and Henningsen (2021). These 28 files are created by the R script R/GridSearchFoodSecurity.R</p>
Indicadors world ealth organization
<p>Es tracta d'un dataset de la pagina "https://data.who.int/indicators". </p> <p>S'ha generat mitjançant scraping, conte el detall dels indicadors aixi com les fitxes de cadascun dels indicadors.</p> <p> </p> <p> </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.