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3,134 results for “Spain”
Characterisation of Social Vulnerability to the environmental hazard of heat in Logroño, and the surrounding La Rioja region in Spain, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for Logroño, and the surrounding La Rioja region, Spain. The input variables used in this dataset come from the national census data for Spain and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Dataset: The Role of News Consumption on Influencers' Facebook Pages in Threat Perception and Political Conservatism During Times of COVID-19: A Comparative Study between the USA, Spain, and Egypt
<p>Este archivo ofrece los datos en bruto de una encuesta examina el impacto del consumo de noticias en las páginas de Facebook de los influencers en la motivación del conservadurismo político durante amenazas como el terrorismo o las pandemias. Muestra: N=1309, jóvenes de entre 18 y 35 años en Estados Unidos, España y Egipto. Trabajo de campo realizado entre el 10 de agosto de 2021 y el 5 de septiembre de 2021.</p> <p><span>Dataset correspondiente al proyecto El rol de la ciudadanía en la comunicación política digital CI-COMPOL (PID2020-119492GB-I00) financiado por MCIN/AEI/10.13039/501100011033/. IP: Andreu Casero-Ripollés, Departamento de Ciencias de la Comunicación, Universitat Jaume I de Castellón</span></p>
Members and Destinations of Spain's Judiciary (2005-2023)
<p>This document contains the documentation of the dataset <em>Members and Destinations of Spain’s Judiciary (2005-2023)</em>, created at the University of Barcelona. The work is part of the I+D+i project PREFJUDIPOL: <em>Preferences, career, and territory. The politics of judicial inequality in Spain</em> (PID-2020-113871RB-I00), funded by MICIU/AEI/10.13039/501100011033/.</p> <p>The data have been used to produce the following paper:</p> <ul> <li>Vallbé, Joan-Josep and Ramírez-Folch, Carmen and Lozano, Luis Mario, Glass ceiling or merit? The politics of judicial promotion in a civil law system (July 26, 2024). Pre-print version available at <a href="https://ssrn.com/abstract=">SSRN</a>.</li> </ul>
1600 years of modelled energy production and demand for European Countries (Norway, France, Italy, Spain, and Sweden)
<h3>Citation</h3> <p>When using this dataset, please cite the following paper: van der Most et al. Temporally compounding energy droughts in European electricity systems with hydropower, 10 January 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3796061/v1].</p> <h3>Description</h3> <p>This dataset contains daily renewable energy production and demand data used in the study "Temporally compounding energy droughts in European electricity systems with hydropower". The dataset includes production data for various renewable energy sources (offshore wind, onshore wind, solar photovoltaics, run-of-river, and hydropower reservoir inflow) and electricity demand. It was generated wit the use of 1600 years of climate model data and a daily renewable electricity production and demand modelling framework. The study focuses on five European countries with significant hydropower capacities: Norway, France, Italy, Spain, and Sweden.</p> <h3>Content</h3> <ul> <li> <p><strong>Energy Production Data</strong>:</p> <ul> <li>Offshore and Onshore Wind Power: Derived from 10 m wind speed data extrapolated to hub height, using power law equations and cubic power curves.</li> <li>Solar Photovoltaics (PV): Based on solar irradiance and temperature-dependent cell efficiency calculations.</li> <li>Hydropower: Includes inflow data for run-of-river and reservoir hydropower systems modelled with routed runoff data</li> <li>Hydropower dispatch is modelled at the national level using a linear optimization approach that aims to minimize the difference between demand and the sum of all renewable energy production over a year, directing the solution to following the load curves.</li> </ul> </li> <li> <p><strong>Energy Demand Data</strong>:</p> <ul> <li>Daily load data from ENTSO-E tranparancy fitted using a logistic smooth transmission regression approach to national mean, population-weighted daily near-surface temperatures from ERA5 reanalysis data.</li> <li>Demand curves account for weekdays and weekends but exclude cultural and socio-economic factors such as holidays.</li> </ul> </li> </ul> <h3>Methodology</h3> <p>The dataset is generated using the KNMI Large Ensemble Time Slice (KNMI-LENTIS) dataset, which includes 160 sets of 10-year physical climate model simulations of present-day climate (2000-2009). The simulations are conducted with the EC-Earth3 global climate model. The energy production and demand data are modeled to assess the impact of meteorological drivers on energy systems, with a focus on identifying periods of high residual loads (energy droughts). The model set-up has been validated with the use of ERA5 data in previous work. </p> <h3>Usage</h3> <p>This dataset is intended for researchers and policymakers interested in studying the impact of climate variability on renewable energy systems. It provides insights into how different meteorological conditions can lead to energy droughts and offers a basis for developing strategies to enhance the resilience of energy systems.</p> <p> </p>
Map of Soil Organic Carbon: Region of Murcia (Spain)
This data package contain four soil organic carbon (SOC) maps resulted from the best data-model agreement of the analysis carried out in the frame of the Ph.D. Thesis ‘MODELING ORGANIC CARBON FOR QUANTIFICATION OF RESERVOIRS IN TERRESTRIAL ECOSYSTEMS AT THE NATIONAL LEVEL’ (Pilar Durante). Theses maps correspond to the estimates of SOC concentration (SOCc, g/kg) and SOC stocks (SOCs, tC/ha), and their associated spatially explicit uncertainties maps, for the Region of Murcia at 0-30 cm and 100 m spatial resolution. To achieve this, we evaluated four different digital soil mapping (DSM) approaches to estimate SOCc and SOCs for the Region of Murcia (11,313 km2), a topographic and climatic complex area in southern Iberian Peninsula, at three spatial resolutions (100m, 250m, 1000m). Using a local SOC database (255 soil profiles), we founded that a Quantile Regression Forest (QRF) approach had the best data-model agreement at 100 m spatial resolution, with the best balance of accuracy, external validation, and interpretability. The QRF model showed a mean SOCc of 12.18 g/kg with an overall uncertainty of 10.54 g/kg and an accuracy percentage of 79%; meanwhile the mean SOCs was 27,572 GgC with an uncertainty of 0.016 GgC. The analysis showed that using local environmental covariates and local soil information to predict SOC within this region resulted in a relative improvement between ~40% (for SOCc) and ~65% (for SOCs) when compared with SOC products derived from national and global databases. Our results provided evidence that large discrepancy exists between national and global estimates for reporting SOC at a local scale. Consequently, local-to-regional efforts are needed to better describe SOC spatial variability to reduce uncertainty and improve the assessment of soil resources.
Temporal patterns of leaf litter inputs into a stream over a four-year period (2011-2014), Arbúcies, Catalonia, Spain.
Data based on estimations of leaf litter inputs from riparian trees into a stream reach over a 4 years period (2011-2014). Data was collected in Arbucies, Barcelona is a forested stream with no human pressure (i.e., pristine). Data contains values from 4 riparian tree species: AL (alder), AS (ash), BL (Black Locust) and BP (Black Poplar). Units are in mg. Estimations were extracted from sampling leaf litter input into the stream during the study period (30 samplings per year) and fitting Gaussian-type models (P<0.001, r2>0.60). Data also includes daily-basis discharge flow estimations based on discrete measure of flow using salt dilution technique and water level sensor data.
Outer Port of Punta Langosteira (Spain) ship movement dataset: 2021 - 2022
<p>This dataset contains the movements of 21 ships recorded in the Outer Port of Punta Langosteira (A Coruña, Spain) from 2021 until 2022.</p>
Parish church ("Église Saint-Brice"). Painting "The Archduke Isabel of Spain giving her jewellery to the basilicum Saint-Martin of Halle. Gaspar de Crayer. 2
<u>File Name</u>: PM_142526_B_Tournai <br><u>Sublocation</u>: Église Saint-Brice <br><u>Location</u>: Tournai <br><u>Province</u>: Hainaut <br><u>Country</u>: Belgium <br><u>Header</u>: Tableau de Gaspar de Craeyer, "l'archiduchesse Isabelle d'Espagne, qui donne ses bijoux à la basilique Saint-Martin à Hal" <br><u>Description</u>: Parish church ("Église Saint-Brice"). Painting "The Archduke Isabel of Spain giving her jewellery to the basilicum Saint-Martin of Halle. Gaspar de Crayer. <br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: pmrmeaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert,pmrmaeyaert@gmail.com <br><u>Keywords</u>: Cultural heritage; Cultural heritage|Techniques|Painting; Europe|Belgium; Europe|Belgium|Hainaut; Europe|Belgium|Hainaut|Tournai; Cultural heritage|Techniques <br><u>Date of Generation</u>: 2022-01-09T14:58:53+02:00
Parish church ("Église Saint-Brice"). Painting "The Archduke Isabel of Spain giving her jewellery to the basilicum Saint-Martin of Halle. Gaspar de Crayer.
<u>File Name</u>: PM_142518_B_Tournai <br><u>Sublocation</u>: Église Saint-Brice <br><u>Location</u>: Tournai <br><u>Province</u>: Hainaut <br><u>Country</u>: Belgium <br><u>Header</u>: Tableau de Gaspar de Craeyer, "l'archiduchesse Isabelle d'Espagne, qui donne ses bijoux à la basilique Saint-Martin à Hal" <br><u>Description</u>: Parish church ("Église Saint-Brice"). Painting "The Archduke Isabel of Spain giving her jewellery to the basilicum Saint-Martin of Halle. Gaspar de Crayer. <br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: pmrmeaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert,pmrmaeyaert@gmail.com <br><u>Keywords</u>: Cultural heritage|Techniques|Painting; Europe|Belgium; Europe|Belgium|Hainaut; Europe|Belgium|Hainaut|Tournai; Cultural heritage|Techniques; Cultural heritage <br><u>Date of Generation</u>: 2022-01-09T14:45:51+02:00
Distancia-Covid Individual Contact Estimates for Spain
<p>Individual estimates of age-specific contact patterns in Spain during the Covid-19 pandemic. This data was generated from the CSIC Distancia-Covid survey (https://distancia-covid.csic.es/). It includes estimated numbers of coresidents and non-coresident contacts for each individual represented in the Spanish Labor Force Survey during 2020 and 2021. These estimates do not relate to any identifiable person; rather they provide information about the overall distribution of contacts across the population. These individual estimates have been used to calculate the mean age-specific contacts provided in <a href="https://doi.org/10.5281/zenodo.5983902">https://doi.org/10.5281/zenodo.5983902</a>. </p> <p>This dataset contains the following files:</p> <ul> <li>distancia_covid_individual_contact_estimates_metadata_dictionary.csv: Variable definitions</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave1.csv.gz: Estimates of coresidents during wave 1 of the Distancia-Covid survey (14 May 2020 through 10 June 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave2.csv.gz: Estimates of coresidents during wave 2 of the Distancia-Covid survey (24 July 2020 through 31 August 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave3.csv.gz: Estimates of coresidents during wave 3 of the Distancia-Covid survey (14 December 2020 through 10 January 2021)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave1.csv.gz: Estimates of non-coresident contacts during wave 1 of the Distancia-Covid survey (14 May 2020 through 10 June 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave2.csv.gz: Estimates of non-coresident contacts during wave 2 of the Distancia-Covid survey (24 July 2020 through 31 August 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave3.csv.gz: Estimates of non-coresident contacts during wave 3 of the Distancia-Covid survey (14 December 2020 through 10 January 2021)</li> <li>CITATION.cff: Citation file.</li> </ul> <p> </p>
Distancia-Covid Contact Estimates for Spain
<p>Estimates of age-specific contact patterns in Spain during the Covid-19 pandemic. This data was generated from the CSIC Distancia-Covid survey (https://distancia-covid.csic.es/). It includes estimated mean numbers of coresidents and non-coresident contacts by age group during 2020 and 2021, for all of Spain and disaggregated by autonomous community. (See `data/distancia_covid_contact_estimates_spain_metadata_dictionary.csv` for variable descriptions.) This repository also includes the survey instrument used in each wave.</p> <p><em>File Descriptions</em></p> <p>- distancia_covid_contact_estimates_spain_metadata_dictionary: Data dictionary<br> - distancia_covid_contact_estimates_spain.csv: Contact estimates<br> - distancia_covid_instrument_wave_1.xlsx: Survey instrument used in Wave 1<br> - distancia_covid_instrument_wave_2.xlsx: Survey instrument used in Wave 2<br> - distancia_covid_instrument_wave_3_4.xlsx: Survey instrument used in Waves 3 and 4<br> - CITATION.cff: Citation information </p> <p>This data is also hosted in the <a href="https://github.com/Distancia-COVID/Distancia-Covid-Open-Data">Distancia-Covid-Open-Data</a> GitHub repository. This version corresponds with:</p> <p><a href="https://github.com/Distancia-COVID/Distancia-Covid-Open-Data/releases/tag/v1.2.0">https://github.com/Distancia-COVID/Distancia-Covid-Open-Data/releases/tag/v1.2.0</a></p>
DS_Wave_Mutriku: Wave resource at Mutriku (Spain)
<p>Data obtained from the RBR virtuoso pressure sensor deployed in Mutriku: i) winter 2016-2017; ii) Spring 2018.</p> <p>Sensor is located at 10 m water depth, 200 m off the shoreline plant (43º18'52"N, 2º22'34").</p> <p> </p>
Genome drafts of Lotmaria passim strains C2 and C3 isolated from honeybees in Spain
<p>Lotmaria passim is a highly prevalent parasite of honeybees. Herein is reported the draft genome sequences of L. passim C2 and C3 strains of 27.15 Mbp and 26.94 Mbp, respectively. The genomes were sequenced using Illumina MiSeq platform and will allow for further comparative and functional genomics studies.</p>
Discretionary Appointments to Spain's Judiciary (1976-2023)
<p>This document contains the documentation of the dataset <em>Discretionary Appointments to Spain’s Judiciary (1969-2023)</em>, created at the University of Barcelona. The work is part of the I+D+i project PREFJUDIPOL: <em>Preferences, career, and territory. The politics of judicial inequality in Spain</em> (PID-2020-113871RB-I00), funded by MICIU/AEI/10.13039/501100011033/.</p> <p>The data have been used to produce the following paper:</p> <ul> <li>Vallbé, J.-J.; Lozano Martín, L. M. “Strategic Manipulation of Courts in Civil Law Systems”. Manuscrito en proceso de peer review. [Actualmente, en <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4997065">Social Science Research Network -SSRN</a>.</li> </ul> <p>Please contact the authors for any enquires or doubts.</p>
3DPC of a gypsum slope in Finestrat, Alicante (Spain)
<p>3DPC of a gypsum slope in Finestrat, Alicante (Spain).</p> <p>Two different ground-based LiDARs were used during a 5-year time-span: (a) an Ilris-3D and a Ilris 3D long range were used for the first and the second field surveys, respectively, and (b) a Leica C10 laser scanner was used for the third and fourth field surveys.</p> <p> </p> <table> <thead> <tr> <th scope="col">Parameters</th> <th scope="col">2011 February</th> <th scope="col">2012 August</th> <th scope="col">2014 July </th> <th scope="col">2016 January</th> </tr> </thead> <tbody> <tr> <td>Relative time span (days) </td> <td> 0</td> <td>553 </td> <td>1256 </td> <td>1822</td> </tr> <tr> <td>LiDAR model </td> <td>Ilris 3D</td> <td>Ilris 3D (long range) </td> <td>Leica C10 </td> <td>Leica C10 </td> </tr> <tr> <td>Mean distance of scanning (m) </td> <td>276</td> <td>276</td> <td>120</td> <td>133</td> </tr> <tr> <td>Acquisition velocity (points/s)</td> <td> 2500 </td> <td>10,000 </td> <td> 50,000</td> <td>50,000</td> </tr> </tbody> </table> <p>The datasets were used for this paper:</p> <p>Tomás, R., Abellán, A., Cano, M. et al. A multidisciplinary approach for the investigation of a rock spreading on an urban slope. Landslides 15, 199–217 (2018). https://doi.org/10.1007/s10346-017-0865-0</p>
DHP images collected from Alto Tajo and Cuellar in Spain.
<p>Digital Hemispherical Photography images taken in 33 30 x 30 m plots across two sites in Spain. Images were taken on a 10 m grid, making 16 locations per plot (see Flynn et al., 2022 for details). At each location, DHP images were captured with three exposure settings (automatic and ± one stop exposure compensation), levelling a Canon EOS 6D full frame DSLR sensor with a Sigma EX DG F3.5 fisheye lens, mounted on a Vanguard Alta Pro 263AT tripod. For each RGB image, the blue band was extracted, as this best represents sky/ vegetation contrast. For each plot, an exposure setting was chosen based on visual assessment and pixel brightness histograms of four images indicative of the whole plot. Automatic thresholding was carried out using the Ridler and Calvard method (1978), creating a binary image of sky and vegetation.</p>
An annotated compilation of chronometric dates for the Middle-Upper Palaeolithic Transition (45-30 ka BP) in northern Iberia (Spain). Source Data.
<p>This repository contains the files of the chronometric dates framed between 45-30 ka BP in Northern Iberia.</p>
Time Series of Water Levels in a Coastal Barrier-Lagoon System, NW Spain (2009-2012)
<p>This repository contains the data recorded by water-level loggers (survey-pressure transducers) deployed in a barrier-lagoon coastal system, which were used in the study by</p> <p><strong>R. González-Villanueva, M. Pérez-Arlucea, and S. Costas titled 'Lagoon Water-Level Oscillations Driven by Rainfall and Wave Climate,' published in Coastal Engineering, Volume 130, 2017, Pages 34-45, ISSN 0378-3839, available at <a href="https://doi.org/10.1016/j.coastaleng.2017.09.013">https://doi.org/10.1016/j.coastaleng.2017.09.013</a></strong></p> <p>The repository consists of three text files:</p> <ol> <li><strong>lagoon_water_level.txt</strong></li> <li><strong>sea_level.txt</strong></li> <li><strong>phreatic_level.txt</strong></li> </ol> <p>Each file includes a header with metadata and information for each column in the data file, as follows:</p> <ul> <li><strong>pt_id</strong>: ID of the individual record</li> <li><strong>pt:</strong> instrument used</li> <li><strong>lat</strong>: Latitude in WGS84</li> <li><strong>long</strong>: Longitude in WGS84</li> <li><strong>units</strong>: Indicates the measurement unit for the water level recordings</li> <li><strong>temporal resolution</strong>: Indicates the time interval between two consecutive measurements</li> <li><strong>column 1</strong>: Description of the data contained in column 1</li> <li><strong>column 2</strong>: Description of the data contained in column 2</li> <li><strong>column n</strong>: Description of the data contained in column n</li> </ul>
Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain)
<h2><span lang="EN-US">Dataset name</span></h2> <p><span lang="EN-US">Small_Scale_Fishery_Data_2023_v2 </span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Title</span></h2> <p><span lang="EN-US">Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain). </span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Description </span></h2> <p><span lang="EN-US"> This dataset was created for the Fish2Sustainability research project, which aims to evaluate how small-scale fisheries (SSF) contribute to Sustainable Development Goals (SDGs). The dataset includes 60 case studies across eight countries and was developed using a rapid appraisal framework. The framework includes a four-step process: </span></p> <p><span lang="EN-US"> 1. Identifying specific SDG targets influenced by SSF;</span></p> <p><span lang="EN-US"> 2. Extracting relevant variables from UN indicators;</span></p> <p><span lang="EN-US"> 3. Gathering expert input via a questionnaire to score these variables;</span></p> <p><span lang="EN-US"> 4. Creating composite indicators to measure SSF performance against SDGs.</span></p> <p><span lang="EN-US"> The dataset contains raw data from step 3, case study details, variable scores, and comments from data collectors (contributing authors). The dataset is valuable for researchers interested in small-scale fisheries and socio-ecological systems. By incorporating expert judgments from individuals with expertise in SSF, particularly in data-poor contexts, the dataset offers a wealth of knowledge for conducting comparative analyses across different contexts.</span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Authors </span></h2> <p><span lang="EN-US">Léopold, M.1, Bitoun, R.E.2, Beckensteiner, J.3, Chuenpagdee, R.4, Fondo, E.N.5, Akintola, S.L.6, Bach, P.7, Frangoudes, K.8, Gaibor, N.9, Gutierrez-Cala, L.10, Massey, Y.7, Randrianandrasana, R.11, Razanakoto, T.11, Saavedra-Díaz, L.M.10, Schreiber Arias, M.12,13, Salas, S.14, Devillers, R.2,4 </span></p> <h3><span lang="EN-US">Affiliations </span></h3> <p><span lang="EN-US">1 ENTROPIE (IRD, University of La Reunion, CNRS, University of New Caledonia, Ifremer), c/o IUEM, Plouzané, France </span></p> <p><span lang="EN-US">2 Espace-Dev (IRD, Univ. </span>Montpellier, Univ. Guyane, Univ. La Réunion, Univ. Antilles, Univ. Nouvelle Calédonie), Montpellier, France</p> <p>3 AMURE (Ifremer, UBO, CNRS), Plouzané, France</p> <p><span lang="EN-US">4 Department of Geography, Memorial University of Newfoundland, St. John’s, NL, Canada</span></p> <p><span lang="EN-US">5 Kenya Marine and Fisheries Research Institute, Mombasa, Kenya</span></p> <p><span lang="EN-US">6 Department of Fisheries, Faculty of Science, Lagos State University, Nigeria</span></p> <p><span lang="EN-US">7 MARBEC, University of Montpellier, CNRS, Ifremer, IRD, Sète, France</span></p> <p>8 Université de Bretagne Occidentale: Brest, France</p> <p>9 Instituto Público de Investigación de Acuicultura y Pesca (IPIAP), Universidad del Pacifico (UPAC), Guayaquil, Ecuador</p> <p>10 Grupo de Investigación en Sistemas Socioecológicos para el Bienestar Humano (GISSBH), Programa de Biología, Universidad del Magdalena, Colombia</p> <p>11 Centre d’Etudes et de Recherches Economiques pour le Développement (CERED), Université d’Antananarivo, Madagascar</p> <p><span lang="EN-US">12 EqualSea Lab, Universidad Santiago de Compostela, A Coruña, Spain</span></p> <p><span lang="EN-US">13 School of Global Studies, University of Gothenburg, Gothenburg, Sweden</span></p> <p>14 Centro de Investigación y de Estudios Avanzados (CINVESTAV), IPN, Unidad Mérida, Mexico </p> <h2><span lang="EN-US">Method </span></h2> <p><span lang="EN-US">Case studies were selected in eight countries by national SSF experts, based on specific criteria and research priorities. Case studies were not selected to represent the full diversity of SSF globally or even nationally. Instead, they were chosen to capture a range of fisheries that could showcase different contributions to SDGs. SSF were defined based on various characteristics, such as resources harvested, gear used, and location of the fishery. </span><span lang="EN-US"> </span></p> <h3><span lang="EN-US">Geographical Coverage </span></h3> <p><span lang="EN-US">60 small-scale fisheries located in seven countries are documented in the data:</span></p> <ul> <li><span lang="EN-US">Colombia (4 case studies) – Pacifico: La Guajira, San Andrés y Providencia; Caribe: Chocó, Cauca, Valle del Cauca, Nariño.</span></li> <li><span lang="EN-US">Ecuador (3) – Region: Esmeraldas, Manabi, Guayas, El Oro.</span></li> <li><span lang="EN-US">France (2) – Region: Bretagne, Occitanie.</span></li> <li><span lang="EN-US">Kenya (22) – County: Kilifi, Kwale, Lamu, Mombasa, Tana River.</span></li> <li><span lang="EN-US">Madagascar (20) – Region: Analanjirofo, Anosy, Atsimo Andrefana, Boeny, Diana, Menabe, Vatovavy Fitovinany.</span></li> <li><span lang="EN-US">Mexico (2) – State: Baja California Sur, Campeche, Yucatan.</span></li> <li><span lang="EN-US">Nigeria (6) – State: Bayelsa, Cross River, Lagos, Ondo, Ogun. </span></li> <li><span lang="EN-US">Spain (1) – State: Galicia.</span> </li> </ul> <h3><span lang="EN-US">Data Collection </span></h3> <p><span lang="EN-US">Data collection took place from November 30, 2022, to July 3, 2023, spanning approximately seven months. The data presented serve as a snapshot of the conditions within a specific small-scale fishery during the assessment period. To consider the evolution of trends such as exports, economic growth, and income, we considered any relevant variables over the past decade. </span></p> <p><span lang="EN-US">Data collection approaches varied depending on the context, and data collectors received training to ensure survey consistency. We used primary data sources such as interviews, observations, and measurements whenever possible. In cases where resources were limited, we preferred secondary sources such as existing datasets and literature. Our methods were standardized, but data collectors could adjust them based on their resources. We primarily used direct observation, focus groups, and interviews to collect data. Scoring in interviews and focus groups was done directly or through group analysis by interviewers. Disagreements were resolved through additional interviews or group discussions, with secondary data used if needed. Please refer to the methods in : </span></p> <p><strong><span lang="EN-US">Bitoun et al., (2024). A methodological framework for capturing marine small-scale fisheries’ contributions to the sustainable development goals. Sustainability Science, 19(4), 1119–1137. https://doi.org/10.1007/s11625-024-01470-0. </span></strong><span lang="EN-US"><strong> </strong> </span></p> <h3><span lang="EN-US">Ethics </span></h3> <p><span lang="EN-US">Participants had the option to join of their own accord, were fully briefed on the research goals, and were given the opportunity to review interview guidelines before proceeding. Depending on the circumstances, interviews could last 45 minutes to 4.5 hours. Participants were guaranteed confidentiality and anonymity in the handling and reporting of their data.</span><span lang="EN-US"> </span></p> <h3><span lang="EN-US">Suggested citation</span></h3> <p><span lang="EN-US">Léopold, M., Bitoun, R., & Devillers, R. (2023). Qualitative Data on 61 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain) (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16077739</span></p> <h2><span lang="EN-US">Data Files</span></h2> <p><span lang="EN-US">The dataset includes the following:</span></p> <ul> <li><span lang="EN-US">The raw dataset (.xls format).</span></li> <li><span lang="EN-US">A data dictionary describing and defining each dataset column (.xls format).</span></li> </ul>
Soil organic carbon and associated uncertainty at 90 m resolution for peninsular Spain
Soil organic carbon (SOC) must be quantified and monitored to assess soil management practices, adapt policies, and evaluate environmental impacts. However, due to SOC spatial variability, soil surveys become a very challenging task because of the high costs of acquiring data, operational complexity, and updating. Digital soil mapping based on machine learning approaches in combination with remote sensing techniques have enabled soil carbon spatial distribution to be significantly improved, even with limited soil samples. A legacy soil database of 8,361 georeferenced profiles and a selection of environmental data-driven covariates intimately related to soil-forming factors (e.g., biota, climate, parent material) were used to generate SOC maps. Modeling of data was based on three supervised learning approaches: quantile regression forest, ensemble machine learning and auto-machine learning. For the final SOC spatial distribution maps, each pixel was assigned the prediction from the most accurate model, i.e., lowest uncertainty. We applied this modeling technique to generate cost-effective, high-resolution maps (90 m pixel resolution) of SOC distribution, and its associated spatially explicit uncertainty, in peninsular Spain. These maps showed 15.7 g.kg-1 mean SOC concentration at 0-30 cm and 3.6 g.kg-1 at 30-100 cm depth. The total SOC stock at its effective depth was 3.8 Pg C, storing the 74% in the upper 30 cm (2.82 Pg C). The correlation between SOC observed and predictions final values showed R2=0.68 for SOCc and R2=0.54 for SOCs at the upper 30cm. The methodology proposed in this study aims to improve benchmark SOC estimates in support of the National GHG Emissions Inventory Report
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.