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5G-EMIT Project: EMF measurements of 2G/3G/4G/5G frequency bands captured with a MVG EME Spy Evolution sensor in a building behind the window in LOS to one antenna in the Esch-Belval Area, Luxembourg (2022-10-19)
<p>During the 5G-EMIT project, EMF measurements of relevant mobile frequency bands have been taken at various sites around known antennas.</p> <p>Focus was in measuring the the following standard frequency bands:</p> <ul> <li>5G 700 <ul> <li>Band 28 FDD down</li> <li>758 - 803 MHz (Luxembourg: 758 - 788 MHz)</li> </ul> </li> <li>LTE 800 <ul> <li>Band 20 FDD down<br> 791 - 821 MHz</li> </ul> </li> <li>GSM 900 <ul> <li>Band 8 FDD down</li> <li>925 - 960 MHz</li> </ul> </li> <li>GSM/LTE 1800 <ul> <li>Band 3 FDD down</li> <li>1805 - 1880 MHz</li> </ul> </li> <li>UMTS 2100 <ul> <li>Band 1 FDD down</li> <li>2110 - 2170 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 38 TDD</li> <li>2570 - 2620 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 7 FDD down</li> <li>2620 - 2690 MHz</li> </ul> </li> <li>5G 3500 <ul> <li>Band 78 TDD</li> <li>3300 - 3800 MHz (Luxembourg: 3400 - 3800 MHz)</li> </ul> </li> </ul> <p><br> Two sensor have been used: The MVG EME Spy Evolution and a sensor developed by IMEC/University of Ghent.</p> <ul> <li>The MVG Spy Evolution sensor is able to measure all of the requested frequency bands as defined by the standard.</li> <li>The IMEC/University of Ghent sensor is only able to measure the LTE 800, GSM 900, GSM/LTE 1800 bands as defined by the standard.<br> In the 5G 3500 band it is only able to measure the range of 3550-3700 MHz (3465-3785 MHz within a -5db response).</li> </ul> <p>The datasets of the ZIP file has following structure:</p> <ul> <li>measurements: <ul> <li>the files of the measurements as provided by the sensor or application</li> <li>combined file of measurements in case the measurements have been split into several parts</li> <li>potentially analysis files of the measurements </li> </ul> </li> <li>pictures: <ul> <li>pictures of the antennas</li> <li>pictures of the sensor</li> <li>pictures of the location of the sensor</li> </ul> </li> <li>Readme.txt: <ul> <li>Used time-zone in the tables</li> <li>Used units in the tables</li> <li>Calculation of the total values in the tables</li> <li>Used labels of the the frequency bands</li> <li>Used sensor (MVG EME Spy Evolution or IMEC/University of Ghent)</li> <li>Locations of the target antennas</li> <li>Potentially location of the fixed sensor</li> </ul> </li> </ul>
5G-EMIT Project: EMF measurements of 2G/3G/4G/5G frequency bands captured with a MVG EME Spy Evolution sensor in a building behind the window in LOS to one antenna in the Esch-Belval Area, Luxembourg (2022-11-08)
<p>During the 5G-EMIT project, EMF measurements of relevant mobile frequency bands have been taken at various sites around known antennas.</p> <p>Focus was in measuring the the following standard frequency bands:</p> <ul> <li>5G 700 <ul> <li>Band 28 FDD down</li> <li>758 - 803 MHz (Luxembourg: 758 - 788 MHz)</li> </ul> </li> <li>LTE 800 <ul> <li>Band 20 FDD down<br> 791 - 821 MHz</li> </ul> </li> <li>GSM 900 <ul> <li>Band 8 FDD down</li> <li>925 - 960 MHz</li> </ul> </li> <li>GSM/LTE 1800 <ul> <li>Band 3 FDD down</li> <li>1805 - 1880 MHz</li> </ul> </li> <li>UMTS 2100 <ul> <li>Band 1 FDD down</li> <li>2110 - 2170 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 38 TDD</li> <li>2570 - 2620 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 7 FDD down</li> <li>2620 - 2690 MHz</li> </ul> </li> <li>5G 3500 <ul> <li>Band 78 TDD</li> <li>3300 - 3800 MHz (Luxembourg: 3400 - 3800 MHz)</li> </ul> </li> </ul> <p><br> Two sensor have been used: The MVG EME Spy Evolution and a sensor developed by IMEC/University of Ghent.</p> <ul> <li>The MVG Spy Evolution sensor is able to measure all of the requested frequency bands as defined by the standard.</li> <li>The IMEC/University of Ghent sensor is only able to measure the LTE 800, GSM 900, GSM/LTE 1800 bands as defined by the standard.<br> In the 5G 3500 band it is only able to measure the range of 3550-3700 MHz (3465-3785 MHz within a -5db response).</li> </ul> <p>The datasets of the ZIP file has following structure:</p> <ul> <li>measurements: <ul> <li>the files of the measurements as provided by the sensor or application</li> <li>combined file of measurements in case the measurements have been split into several parts</li> <li>potentially analysis files of the measurements </li> </ul> </li> <li>pictures: <ul> <li>pictures of the antennas</li> <li>pictures of the sensor</li> <li>pictures of the location of the sensor</li> </ul> </li> <li>Readme.txt: <ul> <li>Used time-zone in the tables</li> <li>Used units in the tables</li> <li>Calculation of the total values in the tables</li> <li>Used labels of the the frequency bands</li> <li>Used sensor (MVG EME Spy Evolution or IMEC/University of Ghent)</li> <li>Locations of the target antennas</li> <li>Potentially location of the fixed sensor</li> </ul> </li> </ul>
Dataset: A unified modelling framework for projecting sectoral greenhouse gas emissions
<p>Data provided includes results from the unified framework described in "A unified modelling framework for projecting sectoral greenhouse gas emissions". Contains posterior draws of emission intensities and resulting emissions for 173 countries, five main sectors up to the year 2050. Historical GHG emissions data based on <a href="https://doi.org/10.5194/essd-13-5213-2021">Minx et al (2021)</a>.</p> <p>Code for the processing of results can be found on <a href="https://github.com/oDNAudio/GHG_sector_projections">Github</a>.</p>
Project Gutenberg Self-Publishing Press: Portuguese Books
<p>A collection of 40 books written in Portuguese and published at <a href="http://self.gutenberg.org/">Project Gutenberg Self-Publishing - eBooks | Read eBooks online | Free eBooks</a>.</p> <ul> <li>The <em>gutenberg_selfpub_tagged.vrt</em> file contains all the books. The texts were tagged using Spacy large trained model for Portuguese (<a href="https://spacy.io/models/pt">https://spacy.io/models/pt</a>)</li> <li>The corpus metadata is listed in <em>gutenberg_selfpub_metadata.tsv</em></li> <li><em>gutenberg_selfpub_xml_untagged.zip</em> contains all the untagged texts.</li> </ul> <p>The XML files contain the following tags and attributes:</p> <ul> <li>chapter: n</li> <li>dedication</li> <li>text:id</li> <li>title</li> <li>author</li> <li>subtitle</li> <li>part: n</li> <li>acknowledge</li> <li>summary: lang</li> <li>publisher</li> <li>short: n</li> <li>biography</li> </ul>
Crime Reduction by the NBS during the UrbanGreenUP project in Valladolid city
<p>This indicator is included in the list of indicators <em>Challenge 8. Social Justice and Social Cohesion</em>.</p> <p>According to Oxford dictionary, crime is an action or omission which constitutes an offence and is punishable by law.</p> <p>The installation of urban furniture as a measure of protection against crime, vandalism and terrorist acts has been developing successfully since the 70s and it is called “Crime Prevention Through Environmental Design (CPTE)”. The main technique is to reduce crime opportunities through modification and manipulation of the built environment that make it more difficult (Massoomeh et al, 2016).</p> <p>Green Infrastructures can help to reduce the causes and opportunities crime and vandalism because the structures that look well cared for discourage crime and increase social justice and cohesion. It is a measure that through well planning, designing and managing of the built environment (Massoomeh et al, 2016).</p>
Measurements of type 3 non-uniqueness between 660 °C and 960 °C for 18SIB02 EMPIR project "Real-K"
<p>Dataset coming from type 3 non uniqueness investigation at INRiM, for project Real-K.</p> <p>The dataset has been collected by 5 test HT-SPRTs, inserted into a sodium heat pipe, calibrated at the fixed points of water, tin, zinc, silver and aluminium. The measurements have been performed at 760, 780, 800, 820, 840 and 860 °C.</p> <p>RPTW_Polito.xlsx are measurements taken on 6 probes at the triple point of water. </p> <p>MAP-HP-SODIO.txt contains measurements performed with 2 reference sensors in order to evaluate the uniformity among the HP wells.</p> <p>note-durante.xlsx contains notes taken during the "MAP-HP-SODIO" measurements</p> <p>760-SODI-2.txt contains the actual measurements of the 5 probes + 1 control probe (in channel 4).</p> <p>calibrations_2023.xlsx contains the calibration measurements and calculations of the test HT-SPRTs</p> <p> </p>
Project TARA Survey data
<p>The survey questionnaire and survey response data (anonymized) for Project TARA study on responsible metrics and U.S. academia. </p>
SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections
<p>For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.</p> <p> </p> <p>The SECURES-Met dataset provides variables visible in the table.</p> <table> <tbody><tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Aggregation methods</th> <th>Temporal resolution</th> </tr> </tbody><tbody> <tr> <th>Temperature (2m)</th> <td>T2M</td> <td> <p>°C</p> <p>°C</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th>Radiation</th> <td> <p>GLO (mean global radiation)</p> <p>BNI (direct normal irradiation)</p> </td> <td> <p>Wm-2</p> <p>Wm-2</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th><strong>Potential Wind Power </strong></th> <td>WP</td> <td>1</td> <td>normalized with potentially available area</td> <td>hourly</td> </tr> <tr> <th><strong>Hydro Power Potential</strong></th> <td> <p>HYD-RES (reservoir)</p> <p>HYD-ROR (run-of-river)</p> </td> <td> <p>MW</p> <p>1</p> </td> <td> <p>summed power production</p> <p>summed power production normalized with average daily production</p> </td> <td>daily</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the <strong>spatial aggregation level</strong></p> <ul> <li>NUTS0 (country-wide),</li> <li>NUTS2 (province-wide),</li> <li>NUTS3 (Austria only),</li> <li>and EEZ (Exclusive Economic Zones, offshore only).</li> </ul> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As <strong>population weighted</strong> temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only.</p> <p>The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p>
Interaction matters: Bottom-up driver interdependencies alter the projected response of phytoplankton communities to climate change, links to model results
<p>This dataset provides the output of ten model simulations with the global ocean biogeochemical model FESOM-REcoM necessary to reproduce the findings of Seifert et al. (2023). In addition to information on the mesh, the dataset contains 5-year means of global phytoplankton biomass, chlorophyll, net primary production, growth rates, limitations, carbonate system parameters (dissolved inorganic carbon, CO2 partial pressure, total alkalinity), temperature, photosynthetically active radiation, and mixed layer depths.</p> <p>File names refer to the figures in the paper where the respective data are used. See “readme” for detailed information on the dataset and separate files.</p>
AI4DI Animated Project Introduction Video
<p>This animated video depicts the ECSEL JU project's AI4DI and how it addresses the needs of 5 diverse industrial sectors: Industrial Machinery, Automotive, Semiconductor, Food and Beverage, and Transportation</p>
Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study'
<p>Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study' consisting of (1) the original and complete dataset ('Data_Brain-IT-Reliability-of-HRV-during-Exergaming_for-publication'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections
<p>These files are associated with the article "The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections". </p> <p>1. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/soil_hyper_albedo_RF_int.nc">soil_hyper_albedo_RF_int.nc</a> - hyperspectral soil albedo </p> <p>2. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/lai_hyper_albedo_RF_int.nc">lai_hyper_albedo_RF_int.nc</a> - hyperspectral surface albedo</p> <p>3. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/atmos_F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_diff_red_dir.21_50.tgz">atmos_F2000climo_clm5sp.21_50-F2000climo_clm5sp_bl ...</a> - diagnostic results of the atmospheric model CAM between broadband and hyperspectral simulations. </p> <p>4. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_diff_red_dir.21_50.tgz">F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_dif ...</a> - diagnostic results of the land model CLM between broadband and hyperspectral simulations. </p>
GENeSYS-MOD pathways developed by the openENTRANCE project
<p>This dataset contains quantitative pathways developed with the GENeSYS-MOD 3.1 model in the openENTRANCE project.</p> <p>You can find additional information, including all model results and native input files, at the Supplementary Material for Deliverable D3.2 of the openENTRANCE project at <a href="https://doi.org/10.5281/zenodo.7760651">https://doi.org/10.5281/zenodo.7760651</a>.</p> <p>The data file follows the IAMC data format and the conventions established by the openENTRANCE project. See <a href="https://github.com/openENTRANCE/openentrance">https://github.com/openENTRANCE/openentrance</a> for details.</p> <p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information about the openENTRANCE project and other datasets.</p>
Outdoor air temperature dataset - IN-HALE Project
<p>Dataset for network IN01 in project IN-HALE PROJECT</p> <p>IN-HALE project aims to identify the thermal summer conditions inside the residences of residents over 65 years of age and to identify the individual determinants of exposure to heat, as well as the barriers (material and/or immaterial) for thermal adaptation. To this end, we will monitor the indoors thermal environment of 20 dwellings, through visits of objective assessment of the thermal behaviour of the dwellings and using the installation of thermal dataloggers during the summer period.</p> <p> </p>
Spatially resolved mock observations of stellar kinematics: full radiative transfer treatment of the simulated galaxies of the Auriga project
<p>We present realistic mock spectroscopic observations for state-of-the-art hydrodynamical simulations, using high spectral resolution stellar population models (FSPS) and full radiative transfer treatment with SKIRT. The dataset contains IFS-like spectroscopy of the stellar continuum for 10 (of the 30) galaxies in the Auriga Project at z=0. One galaxy at z=0.8 (Au 6) and another at z=3 (Au 29).</p>
"Presenting Euroqcharm project outcomes to foster global database synchronization" Zoom Webinar video recording
<p>Video recording of the "Presenting Euroqcharm project outcomes to foster global database synchronization" Zoom Webinar</p>
Surface Survey and Legacy Data in the Upper Plain of the Heraion of Perachora: The Perachora Peninsula Archaeological Project 2020 [Digital Supplement with updated polygons from 2020]
<p><strong>The digital Supplement </strong>contains figures in original size (TIFF) & digital data (SHP, CSV). The data can be also found in the linked GitHub repository <a href="https://github.com/sdam-au/perachora-medit-arch/">https://github.com/sdam-au/perachora-medit-arch/</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p>The Perachora Peninsula Archaeological Project held its first season of intensive surface survey from 12 January to 2 February 2020, during which time we focused on the region called the Upper Plain, situated above the sanctuary of Hera, which was in use from the 8th to the 2nd century BC. The nature of this area has been disputed, with Humfry Payne seeing it as a substantial town, while Richard Tomlinson referred to it as “a sporadic scatter of structures.” In order to clarify the nature of the settlement we employed a two-pronged effort: first to verify and document the legacy data in Tomlinson’s plan of the area, and second to conduct intensive surface survey from the boundary of the sanctuary to Lake Vouliagmeni. Within three weeks, our teams measured, photographed, and took the coordinates of nearly all the structures on Tomlinson’s plan, which included domestic structures, a small temple, and an extensive waterworks system, and several that had not been previously recorded. Building A 1 and the Fountain House were cleared and recorded using photogrammetry. The survey’s finds informed our legacy data verification efforts. Densities across all land types were high: nearly 23,000 artefacts were counted, and all but nine survey units produced artefacts. A preliminary analysis of the ceramics indicates a consistent habitation from the Archaic through the Classical period. Concentrations of miniature votives and fine wares were also found on hillsides, possibly indicating open-air shrines. Our work also highlights the need to conserve the structures in the Upper Plain.</p>
Data from: Evaluating the importance of individual heterogeneity in reproduction to Weddell seal population dynamics using integral projection models
<ol> <li>Identifying and accounting for unobserved individual heterogeneity in vital rates in demographic models is important for estimating population-level vital rates and identifying diverse life-history strategies, but much less is known about how this individual heterogeneity influences population dynamics.</li> <li>We aimed to understand how the distribution of individual heterogeneity in reproductive and survival rates influenced population dynamics using vital rates from a Weddell seal population by altering the distribution of individual heterogeneity in reproduction, which also altered the distribution of individual survival rates through the incorporation of our estimate of the correlation between the two rates and assessing resulting changes in population growth.</li> <li>We constructed an integral projection model (IPM) structured by age and reproductive state using estimates of vital rates for a long-lived mammal that has recently been shown to exhibit large individual heterogeneity in reproduction. Using output from the IPM, we evaluated how population dynamics changed with different underlying distributions of unobserved individual heterogeneity in reproduction.</li> <li>Results indicate that the changes to the underlying distribution of individual heterogeneity in reproduction cause very small changes in the population growth rate and other population metrics. The largest difference in the estimated population growth rate resulting from changes to the underlying distribution of individual heterogeneity was less than 1%.</li> <li>Our work highlights the differing importance of individual heterogeneity at the population level compared to the individual level. Although individual heterogeneity in reproduction may result in large differences in the lifetime fitness of individuals, changing the proportion of above- or below-average breeders in the population results in much smaller differences in annual population growth rate. For a long-lived mammal with stable and high adult-survival that gives birth to a single offspring, individual heterogeneity in reproduction has a limited effect on population dynamics. We posit that the limited effect of individual heterogeneity on population dynamics may be due to canalization of life-history traits.</li> </ol>
FUME projection data
<p># FUME data</p> <p>Population data and migration flows from FUME projections</p> <p>## Update 1.0.0</p> <p>Major corrections of the projections</p> <p>## Update 0.3.0</p> <p>- Corrected error in No Migration scenario data</p> <p>- Added IGC (Intensifying Global Competition) scenario</p> <p>- Updated scenario names</p> <p>## Update 0.2.0</p> <p>Updated input rates</p> <p>## Introduction</p> <p>International projection model with dimensions Age, Sex, Education and Country of Birth.</p> <p>Projected from 2015 to 2050, four different scenarios; Benchmark, Ukraine War, Recovery in Europe and Rise of the East. Additional scenario with no migration also included.</p> <p>Benchmark scenario: Identical to SSP2 from Koch & Leimbach (2022), including COVID<br> shock but not Ukraine war.</p> <p>Ukraine War (previously called Short-War) scenario: Same as the benchmark scenario but we use the latest IMF GDP estimates till the year 2027 which include the Ukraine war. Afterwards, a linear transition over 1 years back to SSP2 growth rates is implemented. Exception: Ukraine is estimated to have a 35% decrease in GDP in 2022 and the IMF does not provide estimates for the following year. Using a linear transition to SSP2 growth rates till 2028 would reduce Ukraines GDP very<br> drastically. Instead we assume that in 2023 Ukraine has another 17.5% GDP decrease and in 2024 the growth is zero. Afterwards (2025 and ongoing), we estimate Ukraine to again follow the SSP2 growth rates.</p> <p>Recovery in Europe (Scenario B): The same as the Ukraine War scenario till 2027 but afterwards European countries will transition towards the SSP with highest growth rates and developing countries will transition towards the SSP with lowest growth rates. All other countries will transition towards SSP2.</p> <p>Rise of the East (Scenario C): The same as the Ukraine War scenario till 2027 but afterwards developing countries will transition towards the SSP with highest growth rates and European countries will transition towards the SSP with lowest growth rates. All other countries will transition towards SSP2.</p> <p>IGC (Intensifying Global Competition): The same as the Ukraine War scenario till 2027 but afterwards every country with a projected GDPc less than 15000$ in 2040 will linearly transition to 15000$ in 2040 and afterwards keep on growing with SSP2 growth rates. All countries with a GDPc higher than 15000$ will behave the same as in the short-war scenario. This “catch-up” behaviour of poorer countries is inspired by the “CAP” and “FAIR” GDP scenarios in Bodirsky et al., 2022.</p> <p>No Migration: Same as Benchmark scenario but with no international migration.</p> <p>## Variables</p> <p>### Population data</p> <p>period: Start year of projection step</p> <p>dest: Country of residency / Migration destination country</p> <p>CoB: Country of Birth</p> <p>area: ISO3 numeric country code of destination</p> <p>age: Age, five year groups, 0 - 100+</p> <p>edu: Education, 6 levels, (e1 = No Education, e2 = Some Primary, e3 = Primary, e4 = Lower Secondary, e5 = Upper<br> Secondary, e6 = Post Secondary)</p> <p>sex: Sex, two categories</p> <p>pop: Population</p> <p><br> ### Migration rate data specific variable names</p> <p>POB: Place Of Birth (Country of Birth)</p> <p>Orig: Country of origin</p> <p>Dest: Country of destination</p> <p>flow: Migration rate</p> <p>Skill: Skill categories, (Low (Secondary and Less) and High (Post secondary+))</p> <p>age: Age groups (1 (0-24), 2 (25-64), 3 (65+))</p> <p>flowM: Male specific migration rate</p> <p>flowF: Female specific migration rate</p> <p>## Countries</p> <p>Countries currently included in the model are in total 171 (given in ISO3 country codes):</p> <p>```<br> "AFG" "AUT" "BEL" "BGR" "CYP" "CZE" "DEU" "DNK" "ESP" "EST" "FIN" "FRA" "GBR"<br> "GRC" "HRV" "HUN" "IRL" "ITA" "LTU" "LUX" "LVA" "MLT" "NLD" "POL" "PRT" "ROU"<br> "SVK" "SVN" "SWE" "AGO" "ALB" "ARE" "ARG" "ARM" "AUS" "AZE" "BDI" "BEN" "BFA"<br> "BGD" "BHR" "BHS" "BIH" "BLR" "BLZ" "BOL" "BRA" "BTN" "BWA" "CAF" "CAN" "CHE"<br> "CHL" "CHN" "CIV" "CMR" "COD" "COG" "COL" "COM" "CPV" "CRI" "CUB" "DOM" "DZA"<br> "ECU" "EGY" "ETH" "FJI" "GAB" "GEO" "GHA" "GIN" "GMB" "GNB" "GNQ" "GTM" "GUY"<br> "HKG" "HND" "HTI" "IDN" "IND" "IRN" "IRQ" "ISL" "ISR" "JAM" "JOR" "JPN" "KAZ"<br> "KEN" "KGZ" "KHM" "KOR" "KWT" "LAO" "LBN" "LBR" "LCA" "LKA" "LSO" "MAC" "MAR"<br> "MDA" "MDG" "MDV" "MEX" "MKD" "MLI" "MMR" "MNE" "MNG" "MOZ" "MUS" "MWI" "MYS"<br> "NAM" "NER" "NGA" "NIC" "NOR" "NPL" "NZL" "OMN" "PAK" "PAN" "PER" "PHL" "PRI"<br> "PRY" "PSE" "QAT" "RUS" "RWA" "SAU" "SDN" "SEN" "SGP" "SLB" "SLE" "SLV" "SOM"<br> "SRB" "STP" "SUR" "SWZ" "SYR" "TCD" "TGO" "THA" "TJK" "TKM" "TLS" "TTO" "TUN"<br> "TUR" "TZA" "UGA" "UKR" "URY" "USA" "VCT" "VEN" "VNM" "VUT" "WSM" "YEM" "ZAF"<br> "ZMB" "ZWE"<br> ```</p>
High-resolution throughfall measurement design, Hainich, Germany, project AquaDiva
<p>This dataset contains the sampling design for throughfall data used for the analysis published in Metzger et al. (2017) and Fischer et al. (2023). It gives spatially distributed throughfall measurement points and their forest structural properties. The measurement points are grouped into randomly distributed “kernel” points and “transect” points which are not part of the random design.</p> <p>The field site and sampling design are described in Metzger et al. (2017). The throughfall data is given in an associated published dataset (Metzger and Hildebrandt, 2023).</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.