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35 results for “clima”
Anthropogenic emissions of CH4, N2O, F-gases and BC from GAINS, for EU-countries plus CH, NO, UK developed under the EYE-CLIMA project - March 2025 update
<p><span>As part of the EYE-CLIMA project, GAINS emission data for CH<sub>4</sub>, N<sub>2</sub>O, BC and selected F-gases (HFC-125, HFC-134a, HFC-143a, HFC-23, HFC-32 and SF<sub>6</sub></span>) were released for all EU-27 countries plus UK, Switzerland, and Norway for the period 1990 to 2020 (with exception of F-gases, from 2005 only, and BC/CH<sub>4</sub> emissions from agricultural waste burning, from 2000). Results have been documented in EYE-CLIMA deliverable D2.8 (<a href="http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf">http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf</a>), and they are publicly available at the Zenodo repository under <a href="https://doi.org/10.5281/zenodo.11032177">https://doi.org/10.5281/zenodo.11032177</a>. All data is available on a 0.1°x0.1° grid and in monthly resolution. Emissions are attributed to the respective source categories according to GNFR.</p> <p>The motivation of an update resulted from the need to extending the emission data time series to 2023. With underlying statistics and national emission data currently available till 2022 only (the latter submitted to UNFCCC only by December 2024), the historical data series also could only be established for 2022. Here we use the GAINS scenario feature to extrapolate between 2022 historical data and the first scenario point, 2025 which is based on IEA’s Word Energy Outlook 2023 (https://www.iea.org/reports/world-energy-outlook-2023). Obviously, this also means that emission results for 2023 are not any more based on robust statistics but represent an extrapolation.</p> <p>Extrapolation of spatially explicit data is only possible when the spatial resolution conveys a realistic signal. For the sector “agricultural waste burning” (files with “AWB” as sector, see notation below) spatial allocation is based on actual observation from satellites. As such data products on agricultural fires have been made available until 2022 only, no spatial or temporal signal exists for 2023. The time series provided thus has to end in 2022. No recommendation can be given to modellers, other than to either use 2022 also for 2023 (understanding that the pattern will be strikingly different) or to use a five-year average (which will remove a lot of spatial specificity).</p> <p>The updated dataset covers files as follows (internally, all files now carry version number V05):</p> <p>ALL_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.csv</p> <p>BC_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>BC_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>HFC_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>N2O_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>SF6_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>This is version 2.0 of the dataset. It extends from version 1.0 by covering into the year 2023, but also benefits from a number of additional GAINS improvements. Emissions of emitted compounds are provided as kg/m²/s. File names follow the notation developed for the H-Europe project EYE-CLIMA, i.e. species _ variable-type _ sector _ region _ method (MOD=model) _ timestep _ fromTime _ toTime _ model _ institute _ version . filetype.</p> <p>This version is available at <a href="https://doi.org/10.5281/zenodo.15536170">https://doi.org/10.5281/zenodo.15536170</a>. The generic address of the dataset is <a href="https://doi.org/10.5281/zenodo.10886780">https://doi.org/10.5281/zenodo.10886780</a>, resolving to the latest update available at Zenodo. No further updates are planned in EYE-CLIMA, so this version is expected to also reflect the final update within the project.</p> <p>Compared to version 1.0, GAINS benefitted from a number of new developments such as the following:</p> <p>*) Previously, GAINS has been available in five-year timesteps only (with the aim of allowing for scenarios at that resolution). For data version 1.0, a makeshift solution was found to convert into annual data. A recent update now allows, for historic data, to store and retrieve information on an annual basis (from 1990).</p> <p>*) The energy data were obtained from IEA’s world energy balances 2024 (July version, https://www.iea.org/data-and-statistics/data-product/world-energy-balances#documentation), extending into 2022 and extrapolated towards 2025, downscaled from IEA to GAINS sectors and sub-sectors. Additionally, the annual activity of industrial production is estimated using a linear approach, based on five-year timestep data.</p> <p>*) Agricultural statistics were retrieved from Eurostat (and from FAO globally) and extended to 2022, extrapolated towards 2025.</p> <p>*) Interpretation of GAINS data was reconfirmed and updated in consultations with national experts of multiple EU countries. While the process resulted in revised emission projections to be used in the Clean Air Outlook 4 (see <a title="Protected by Check Point: https://environment.ec.europa.eu/topics/air/clean-air-outlook_en" href="https://protect.checkpoint.com/v2/r02/___https:/environment.ec.europa.eu/topics/air/clean-air-outlook_en___.YzJlOmlpYXNhOmM6bzoyYzdiNDRhNDI4Njc3ZjI5MGFjMTU1N2I2OWVmNzM2ZTo3OjE5OTM6ZTFiY2IzMDMxZGViNGE0MjI0ODRmNWQ4NzA3ZDY3Njc4M2U2NzUxNmEwNzQ0ODViNDBhODc1NmNhZmMzY2FlMjpoOkY6Tg"><span lang="EN-GB">https://environment.ec.europa.eu/topics/air/clean-air-outlook_en</span></a><span lang="EN-GB">). While the details of improvements on the individual aspects cannot be disclosed, they are useful to describe historic data most adequately, and have been integrated also in this assessment. That not only leads to changes in absolute emissions for a given year, but also affects trends that now are more plausible and confirmed through the exchange with the national experts.</span></p> <p><span lang="EN-GB">*) Technical adjustments have improved the precision of temporal allocation of emissions and the conversion of grid sizes to actual area.</span></p>
Particle size and velocity distributions from a Thies Clima 3D Stereo disdrometer installed at the Casale Calore site in L'Aquila (Italy), monthly netCDF archive
<p>Disdrometric data from a Thies Clima 3D Stereo disdrometer, with 22 size classes and 20 velocity classes, located at the instrumented site of Casale Calore in L'Aquila (Italy, 42.3831 N, 13.3148 E, 683 m a.s.l.), managed by the University of L'Aquila and the Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS). </p> <p>Mid values and widths of the classes and instrument ancillary data are provided. One-minute spectra are aggregated every 5 minutes and saved in monthly netCDF files.</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00">https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure:</p> <h2><strong>File "LAQ_3DS_202301_5min.nc"</strong></h2> <pre><strong> dimensions</strong>: <em>diameter </em>= 22; <em>velocity </em>= 20; <em>n_image </em>= 20; <em>y_image </em>= 12; <em>x_image </em>= 12; <em>time </em>= UNLIMITED; // (8741 currently) <strong>variables</strong>: long <em>time_UTC</em>(time=8741); :description = "Measurement time. Timestamp indicates the end of the observation interval, e.g. 01-Mar-2020 00:05:00 represents the particle counts registered between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>diameters</em>(diameter=22); :description = "Mid values of the size classes"; :units = "mm"; float <em>velocities</em>(velocity=20); :description = "Mid values of the velocity classes"; :units = "m s^-1"; float <em>diameters_width</em>(diameter=22); :description = "Width of the size classes"; :units = "mm"; float <em>velocities_width</em>(velocity=20); :description = "Width of the velocity classes"; :units = "m s^-1"; int <em>spectrum</em>(diameter=22, velocity=20, time=8741); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over 5 minutes."; :units = "counts"; :_ChunkSizes = 22U, 20U, 1U; // uint float <em>PSD</em>(diameter=22, time=8741); :description = "Particle size distribution, 5 minutes interval, normalized by the observed volume."; :units = "m^-3 mm^-1"; :_ChunkSizes = 22U, 1U; // uint double <em>monthlySpectrum</em>(diameter=22, velocity=20); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over the entire month."; :units = "counts"; double <em>monthlyPSD</em>(diameter=22); :description = "Particle size distribution for the whole month, normalized by the observed volume."; :units = "m^-3 mm^-1"; int <em>images</em>(x_image=12, y_image=12, n_image=20, time=8741); :description = "Images of samples of the detected precipitating particles. Images are 48x12 pixel maximum, for a max of 4 stacked 12x12 images. Most of the time less than 4 images are provided."; :units = "0-255 pixel values"; :_ChunkSizes = 12U, 12U, 20U, 1U; // uint int <em>image_count</em>(time=8741); :description = "How many images are registred by the instrument in the minute."; :units = "0-4 count"; :_ChunkSizes = 1024U; // uint int <em>precip_type</em>(n_image=20, time=8741); :description = "Precipitation type as classified by the instument based on shape, size, velocity and presence of water, according to the following table with 11 entries (0-10): 0-reserved value, 1-false positive, 2-rain or graupel, 3-drizzle, 4-drizzle with rain, 5-rain, 6-rain with snow, 7-snow, 8-ice prisms, 9-graupel, 10-hail."; :units = "0-10 code"; :_ChunkSizes = 20U, 1U; // uint int <em>particle_diam</em>(n_image=20, time=8741); :description = "Main diameter of the particles shown in the images."; :units = "mm"; :_ChunkSizes = 20U, 1U; // uint //<strong> global attributes</strong>: :<em>title </em>= "Thies Clima 3D Stereo disdrometer data, aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "Particle counts diveded in 22 size classes and 20 velocity classes. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Jan 2023"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome, l.baldini@isac.cnr.it"; :<em>source </em>= "TC 3DS disdrometer at MZS (Antarctica)"; :<em>location </em>= "Mario Zucchelli Station (74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "23-Oct-2024 11:13:22 UTC"; :<em>coverage </em>= "Monthly coverage (Jan 2023): 100 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created from raw TC telegram TDD 163, aggregated to 5min temporal resolution with a sum of the 1-minute counts if least 3 out of 5 are not NaN."; </pre> <p> </p> <p> </p>
Global scale leaf broadband optical properties derived from CliMA Land and associated CESM simulations
<p>Leaf level broadband reflectance and transmittance computed from leaf traits.</p> <ul> <li>clm_refl_tran_1m_weighted.nc: monthly data (144*96 pixels)</li> <li>surfdata_CMIP6_fluspect_v3.nc: surface data to run CESM (144*96 pixels)</li> </ul> <p>Global scale simulation results</p> <ul> <li>research_data_coupled_future_v2.nc: CESM coupled future simulations</li> <li>research_data_coupled_history_v2.nc: CESM coupled historical simulations</li> <li>research_data_uncoupled_history_v2.nc: CESM uncoupled future simulations</li> <li>research_data_uncoupled_ssp_v2.nc: CESM uncoupled SSP245 and SSP585 simulations</li> </ul> <p>Code changes</p> <ul> <li>SurfaceAlbedoMod.F90: modified CLM module</li> <li>Julia-and-Python-Code.tar.gz: code used for processing the data and plot the figures</li> </ul>
Pesquisas na área de Clima Espacial
<p>Entrevista em Português sobre pesquisas em andamento sobre Clima Espacial e previsão de explosões solares.</p> <p>Interview in Portuguese about ongoing research on Space Weather and solar flare forecasting.</p>
Linked collectors and determiners for: ECT herbarium - Embrapa Clima Temperado - Herbário Virtual REFLORA.
Natural history specimen data linked to collectors and determiners held within, "ECT herbarium - Embrapa Clima Temperado - Herbário Virtual REFLORA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e81d05a6-541c-4cda-9650-a4aac9d35e3b">https://bionomia.net/dataset/e81d05a6-541c-4cda-9650-a4aac9d35e3b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e81d05a6-541c-4cda-9650-a4aac9d35e3b">https://gbif.org/dataset/e81d05a6-541c-4cda-9650-a4aac9d35e3b</a>. Formatted as a Frictionless Data package.
Clima organizacional de una institución educativa de ventanilla según la perspectiva de los docentes
<p>La matriz de datos contiene las siguientes variables: Edades, Género, Nivel donde se desempeña el docente, Dimensión estructura, Dimensión recompensa, Dimensión relaciones, Dimensión identidad, Clima organizacional.</p>
Relación entre clima institucional y desempeño docente en I.E. 4021 del distrito de Ventanilla - Callao
<p>La matriz de datos contiene las siguientes variables: Edad de alumnos, Sexo de alumnos, Grado de instrucción de alumnos, Promedio de dimensión 1 de clima institucional - alumnos, Promedio de dimensión 2 de clima institucional - alumnos, Promedio de dimensión 3 de clima institucional - alumnos, Promedio final de clima institucional - alumnos, Promedio de dimensión 1 de desempeño docente - alumnos, Promedio de dimensión 2 de desempeño docente - alumnos, Promedio de dimensión 3 de desempeño docente - alumnos, Promedio de dimensión 4 de desempeño docente - alumnos, Promedio final de desempeño docente - alumnos, Edad de docente, Sexo de docentes, Especialidad de docentes, Promedio de dimensión 1 de clima institucional - docentes, Promedio de dimensión 2 de clima institucional - docentes, Promedio de dimensión 3 de clima institucional - docentes, Promedio final de clima institucional - docentes, Promedio de dimensión 1 de desempeño docente - docentes, Promedio de dimensión 2 de desempeño docente - docentes, Promedio de dimensión 3 de desempeño docente - docentes, Promedio de dimensión 4 de desempeño docentes - docentes, Promedio final de desempeño docente - docentes.</p>
La percepción del clima de aula en estudiantes de educación secundaria de una institución educativa del Callao
<p>La matriz de datos contiene las siguientes variables: Número, Edad, Sexo, Grado y 28 ítems.</p>
Relación entre clima y compromiso institucional en docentes de las instituciones de educación inicial de la red N° 08 de la región Callao
<p>La matriz de datos contiene las siguientes variables: Edad, Condición laboral, Grado académico, Tiempo de servicio, Clima institucional, Compromiso institucional y 66 casos.</p>
Clima de aula percibido por estudiantes de primero a quinto de secundaria en una institución educativa del Callao
<p>La matriz de datos contiene las siguientes variables: Identificación del estudiante, Sexo, Edad, Grado de estudio, Contexto imaginativo, Contexto interpersonal, Contexto regulativo, Contexto instruccional y 150 casos.</p>
Clima de clase y rendimiento académico de alumnos del cuarto de secundaria del taller industria del vestido en Ventanilla
<p>La matriz de datos incluye las siguientes variables: Sexo, Edad, Implicación, Afiliación, Ayuda, Relaciones, Tareas, Competitividad, Autorrealización, Organización, Claridad, Control, Estabilidad, Innovación, Cambio, Rendimiento académico, Nivel de Relaciones, Nivel de Autorrealización, Nivel de Estabilidad, Nivel de Cambio, Nivel de Rendimiento Académico.</p>
Clima organizacional según la percepción de los docentes de una institución educativa de la región Callao
<p>La matriz de datos incluye las siguientes variables: Edades, Tiempo de servicio, Dimensión estructura, Dimensión recompensa, Dimensión relaciones, Dimensión identidad, Clima organizacional.</p>
Estilo de liderazgo directivo y clima organizacional en una institución educativa del distrito de Ventanilla - región Callao (docentes)
<p>La matriz de datos contiene las siguientes variables: Edades, Género, Liderazgo autocrático, Liderazgo democrático, Liderazgo liberal, Estilo de liderazgo directivo, Grado de identificación del personal, Grado de integración del personal, Nivel de motivación del personal, Clima organziacional,</p>
Estilo de liderazgo directivo y clima organizacional en una institución educativa del distrito de Ventanilla - región Callao
<p>La matriz de datos contiene las siguientes variables: Liderazgo liberal, Liderazgo autocrático, Liderazgo democrático, Estilo de liderazgo directivo, Clima organizacional y 30 casos.</p>
Estilo de liderazgo directivo y clima organizacional en una institución educativa del distrito de Ventanilla - región Callao (padres)
<p>La matriz de datos contiene las siguientes variables: Liderazgo liberal, Liderazgo autocrático, Liderazgo democrático, Estilo de liderazgo directivo, Clima organizacional y 30 casos.</p>
Estilo de liderazgo directivo y clima organizacional en una institución educativa del distrito de Ventanilla - región Callao (alumnos)
<p>La matriz de datos contiene las siguientes variables: Liderazgo liberal, Liderazgo autocrático, Liderazgo democrático, Estilo de liderazgo directivo, Clima organziacional.</p>
Percepción del clima escolar en estudiantes del cuarto al sexto de primaria de una institución educativa del Callao
<p>La matriz de datos incluye las siguientes variables: Grado, Sección, Edad, Género, Clima, 29 ítems y 230 casos.</p>
Clima social familiar de estudiantes de sexto grado de primaria de la Red 7 Callao
<p>La matriz de datos contiene las siguientes variables: Género, Edad, Institución educativa, Sección y 174 casos.</p>
Relación entre el clima institucional y desempeño docente en instituciones educativas de la Red N° 1 Pachacútec - Ventanilla
<p>La matriz de datos contiene las siguientes variables: Sexo, Edad, Clima institucional, Comunicación, Motivación, Confianza, Participación, Desempeño docente, Capacidades pedagógicas, Emocionalidad, Responsabilidad en el desempeño de sus funciones, Relaciones interpersonales y 100 casos.</p>
Relación entre clima social familiar y autoestima en estudiantes de secundaria de una institución educativa del Callao
<p>La matriz de datos contiene las siguientes variables: Edad, Género, Autoestima, Relaciones, Desarrollo, Estabilidad, Clima social familiar y 150 casos.</p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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