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9,880 results for “Colombia”
PST estación Albania Guajira, Colombia
<p>Estación Albania, datos recopilados en SISAIRE IDEAM, para partículas suspendidas totales PST.</p>
Material Particulado 10 estación Albania Guajira, Colombia
<p>Estación Albania, datos recopilados en SISAIRE IDEAM, para material particulado de 10 micras.</p>
PST estación Barrancas Guajira, Colombia
<p>Estación Barrancas, datos recopilados en SISAIRE IDEAM, para partículas suspendidas totales (PST).</p>
Material Particulado de 2.5 estación Albania Guajira, Colombia
<p>Estación Albania, datos recopilados en SISAIRE IDEAM, para material particulado de 2.5 micras.</p>
Material Particulado de 10 estación Barrancas Guajira, Colombia
<p>Estación Barrancas, datos recopilados en SISAIRE IDEAM, para material particulado de 10 micras.</p>
Material Particulado de 2.5 estación Barrancas Guajira, Colombia
<p>Estación Barrancas, datos recopilados en SISAIRE IDEAM, para material particulado de 2.5 micras.</p>
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>
Textural soil data, Colombia, 0 - 100 cm
The textural soil data is a harmonized and structured data set with information related to soil particle-size fractions (PSF) such as clay, sand, and silt, as transformed data. The transformed data was obtained through additive log-ratio transformation because the PSF are compositional data. The textural soil data was fitted to five standard depths using the quadratic function with equal areas (spline). Standard depths were: 0 - 5 cm, 5 - 15 cm, 15 - 30 cm, 30 - 60 cm, and 60 - 100 cm. The original data was obtained from Sistema de Información de Suelos de Latinoamérica y el Caribe - SISLAC, a soil information system developed by the Food and Agriculture Organization of the United Nations.
Textural soil maps, Colombia, 0 - 100 cm
These are the first texture maps of Colombia, obtained from national and global digital soil mapping products. The maps were developed at five standard depths (0-5, 5-15, 15-30, 30-60, and 60-100 cm) and standardized with Additive log-ratio (ALR) transformation. The maps were harmonized at 1 square km of spatial resolution. The data packages include the following set maps: texture maps obtained through the Ensemble Machine Learning (EML) algorithms called landmap and MACHISPLIN; texture maps obtained from SoilGrids platform; residual maps of the texture of the algorithms referenced above; and finally texture maps obtained through spatial ensemble technique.
Solicitantes de patente en Colombia (2000-2018) - Según tipo
<p>Relación de las solicitudes de patente presentadas en colombia entre los años 2000 y 2018, detallando cada uno de los solicitantes relacionados y si el mismo es nacional o extranjero</p>
Solicitantes de patente en Colombia (2000-2018) - Según tipo de persona
<p>Relación de las solicitudes de patente presentadas en colombia entre los años 2000 y 2018, detallando cada uno de los solicitantes relacionados y si el mismo es persona natural, empresa o universidad.</p>
Material Particulado de 10 Estaciones en la Mina de Cerrejón, La Guajira, Colombia
<p>Datos de material particulado de 10 de las estaciones Provincial, Barrancas y Sol y Sombra, y la producción de carbón registrada desde el 2010-2019 en Cerrejón la Guajira, Colombia</p>
Base de datos, una mirada textil a la movilización social, Colombia, 2021
<p>Base de datos que reúne 118 fotografías resultado de la convocatoria pública "Una mirada textil a la movilización social”, realizada entre marzo y junio del 2023 en el marco de la investigación en curso: "Desigualdades, resiliencia comunitaria y nuevas modalidades de gobernanza en un mundo postpandemia". Proyecto liderado por Artesanal Tecnológica desde la Escuela de Estudios de Género de la Universidad Nacional de Colombia y financiado por el Ministerio de Ciencia, Tecnología e Innovación. En el marco de la convocatoria de La Plataforma Transatlántica para las Ciencias Sociales y las Humanidades (T-AP).</p> <p>Esta base de datos nos permite identificar las diferentes formas en las que lo textil vistió la movilización social del 2021 en Colombia en el marco de la pandemia de Covid-19. En ella se reúnen fotografías tomadas ciudades como Bogotá, Medellín, Pasto, Popayán, Cúcuta, Bucaramanga, Mosquera y Funza por 28 personas que a través de una diversidad de dispositivos como cámaras profesionales y celulares, atestiguaron las diferentes maneras en la que lo textil durante las movilizaciones ocupó la calle y vistió los cuerpos. La base de datos revela la presencia de lo textil como indumentaria, materializada en capuchas, tapabocas, camisetas, pañoletas, piezas de denuncia como pancartas y banderas, y como acción colectiva de intervención del espacio público, como plantones y grafitis textiles, se despliegan y sitúan en diferentes escenarios que dan cuenta de la diversidad de las movilizaciones que constituyeron el estallido social en Colombia. La base de datos ofrece información sobre el contexto de las capturas, el acceso a las imágenes y la información autoral y de contacto de quienes participaron en la convocatoria. También se puede filtrar los datos por tipo de indumentaria o repertorio de acción textil desplegada en la movilización, así como por la técnica principal de las piezas.</p>
Mammals under pressure: presence data for assessing extinction of endemic, threatened, and mammals subject to use, in Colombia
<p>This is the first dataset that provides a complete compilation of mammal records based on camera traps, human observations, and specimens deposited in biological collections in Colombia. We compiled a dataset with unpublished information, including 97,943 records corresponding to 136 species, of which 38 are endemic, 92 are identified as species subject to use by humans in the literature, and 33 are categorized either as Data Deficient or threatened according to international or unofficial national assessments. The information comes from 31 out of 32 departments of Colombia and constitutes relevant input for future distribution and conservation assessments. Most records (n=96,417, 98.44%) come from non-invasive sampling methods such as camera traps. However, we highlight the contribution of museum specimens (n= 1,332), especially for small and medium-sized species, many of them with restricted distributions in the country. This dataset constitutes a joint collaborative and interinstitutional effort that serves as the basis for cooperative work to comprehensively assess the current conservation status of all mammal species in Colombia.</p>
3D FEM-based inverse model of Nevado del Ruiz - St. Isabel volcanoes (Colombia)
<p><strong>Description of model and data</strong></p> <p>The files include a FEM-based inverse model for the optimization of parameters of a pressure source responsible for surface deformation. The investigated source parameters are the position of the source center, the three semi-axis, the source strike orientation, the source dip orientation, and the source overpressure. The observations used for the inversion are ascending and descending ground velocities. The optimization is based on Least-Squares objectives using the Monte Carlo method. The file of observations needed for the computation of the Least-Squares objectives (to be uploaded in the optimization node) requires four columns (x,y,z, velocities. All in meters, UTM coordinates-UTM zone 18N, and comma-separated).</p> <p>The model takes into consideration the heterogeneous distribution of material elastic properties. The model does not provide the files for the observations and material properties (at the link: https://zenodo.org/record/5575972), but the structure for the optimization model in which new files can be uploaded for a customized model.</p> <p>The model includes the compensation for the stresses induced by the topography (edifices’ load). The file for the construction of the topographic surface is included as a .txt file (the position x,y of the points is in UTM coordinates-UTM zone 18N, the altitude z is in meters). The far-field is modeled as a hemisphere and it is located at 35 km from the center of the model, which is between the Nevado del Ruiz volcano and Santa Isabel volcano.</p> <p>The model is built with Comsol Multiphysics v 5.6 using the modules Optimization and Structural Mechanics modules, and it is provided as a Comsol .mph file.</p> <p> </p> <p>Datasets and model are results of PICVOLC project. PICVOLC has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 79381</p>
Updated distribution and conservation perspectives of marmosine opossums from Colombia
<p>These maps are the results of ecological niche modeling (via MaxEnt) and expert's opinions. Models are based on localities from recent taxonomic reviews, catalogues, museum specimens, and curated GBIF data. Models where tuned specifically for each species and evaluated in different modeling areas (see original publication for details). These maps represent the potential distribution ranges of the Marmosini species of Colombia, but were individually adjusted based on biogeographic barriers (see article for details).</p>
Data files for: Meteorological factors in the production of Gigantic Jets by tropical thunderstorms in Colombia
<p>Data includes:</p> <ul> <li>Gigantic jet locations and times</li> <li>Vertical profiles for GJ and null cases</li> <li>CSV files with meteorological variables per GJ event and null case</li> </ul>
Dataset HC-Pairs Chile, Colombia and Spain
<p>This dataset contains data from the Health Care Providers’ Pain and Impairment Relationship Scale (HC-PAIRS) in health professionals and university students from Chile, Colombia, and Spain. Data from Colombia and Chile was collected between August, 2021, and April, 2022. Data for Spain was collected between September and November 2011.</p> <p>Demographic variables and responses to items on the Health Care Providers’ Pain and Impairment Relationship Scale (HC-PAIRS) are included. Although the language of the file is originally Spanish, the variable names and value labels have been translated into English for easier understanding.<br>Data and codebooks are provided in csv format, following the FAIR principles.<br>Three files are provided:<br>1. HC-Pairs data, with the data related to sample characteristics and the answers to the questionnaire items in the three countries.<br>2. Database codebook of variables, with information of the labels of the variables of the HC-Pairs data file.<br>3. Variables values codebook, with the labels of the values of the variables in the HC-Pairs data file.</p>
Data set and scripts - Influence of Festive Periods on Road Safety: Multidimensional Analysis (Road Accidents in Colombia 2017-2021)
<p>This dataset comprises historical information about road accidents in Colombia from 2017 to 2021, titled 'Road Accidents 2017-2021', containing 18,600 records of accident events on roads managed by the National Roads Institute (INVÍAS, 2021). The dataset includes 41 descriptors and was last updated on July 15, 2022. It has been published under the Open Data initiative (Law 1712 of 2014 on Transparency and Access to National Public Information).</p> <p>In addition to accident information, the dataset integrates a database with holiday dates and road identifiers, ensuring data coherence and quality for data analysis purposes. Statistical analysis is conducted through exploratory data analysis focusing on the years 2017 to 2021, utilizing Python (version 3.10) within the Jupyter Notebooks execution environment and specialized libraries (Pandas, NumPy, Matplotlib, and Seaborn), due to their ease of application for this dataset. After data normalization, the dataset comprises 18,554 records, with 46 excluded due to inconsistent data formats.</p>
Dataset from "Natural capital accounting reveals ecosystems' role in water and energy security in Colombia's Sinú Basin"
<p>The data archived here are associated with the publication titled "Natural capital accounting reveals ecosystems' role in water and energy security in Colombia's Sinú Basin", available at: <a href="https://doi.org/10.1038/s43247-025-02254-9">https://doi.org/10.1038/s43247-025-02254-9</a>. The files within "Sinu_SDR_inputs.zip" and "Sinu_SWY_inputs.zip" were prepared and run in <a href="http://releases.naturalcapitalproject.org/?prefix=invest/3.12.0/">InVEST version 3.12.0</a>. "SDR" refers to the InVEST Sedimnet Delivery Ratio (SDR) model and "SWY" refers to the InVEST Seasonal Water Yield (SWY) model. Results of these model runs are found within "Sinu_SDR_results.zip" and "Sinu_SWY_results.zip" for the SDR and SWY models, respectively. These models were calibrated using observed data on average monthly water flows (from 1959 to 1992) and average annual sediment loads (from 1972 to 1992) from gauge stations on Colombia's Sinú River. Those observed data were obtained from Colombia's Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) hydrometeorological monitoring network <a href="http://dhime.ideam.gov.co/atencionciudadano/">webportal</a> and are summarized in the files included here, "MeanMonthlyObservedFlowsXgaugeStation.csv" for monthly water flows and "annualObservedSedimentXgaugeStation.csv" for annual sediment loads. "EcosystemTypeTable.xlsx" is the table of ecosystem values. "Cuenta_Sinu_SankeyData_v2_paper.xlsx" contains the Sankey and accounts tables.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.