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7,378 results for “case”
Implications of Socio-Economic Conditions on Common Mental Disorders: A Case-study of Salt Pan Workers in Marakkanam Block of Tamil Nadu
<p>Socio-economic indicators of Saltpan workers in Marakaanam, Tamil Nadu, India and their SRQ-20 scoring.</p><p>Data regarding; Consumption, Wages, Debt, Social Group, Gender, Age, Ration Card, Education, Distance from work(saltpan), ownership of house and the SRQ-20 Questionanaire used for Screening of CMDs and Distress levels </p>
Towards an open pipeline for the detection of Critical Infrastructure from satellite imagery – A case study on electrical substations in The Netherlands
<p><strong>Abstract.</strong> Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI prevents their incorporation in large-scale risk assessment studies. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMaps, processes the imagery and assets' masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25%, 50% and 100%) and hyperparameters settings (batch size and learning rate). The best metrics achieved are an Average Precision at an Intersection over Union of 50% of 30.93 and a tile F-score of 89.88%. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable AI.</p>
SPLEEN - High Speed Turbine Cascade – Test Case Database - PIV Measurements
<p>This is an open-access database of Particle Image Velocimetry (PIV) measurements of the flow in the <strong>high-speed low-pressure turbine cascade SPLEEN C1</strong>. </p><p>This database complements aerodynamics measurements reported in the database "SPLEEN - High Speed Turbine Cascade – Test Case Database" which can be found at <a href="https://zenodo.org/doi/10.5281/zenodo.7264761">10.5281/zenodo.7264761</a>. </p><p>The data have been collected at the <strong>von Karman Institute for Fluid Dynamics</strong> during 2022 within the H2020 Clean Sky 2 project <strong>SPLEEN </strong>– Secondary and Leakage Flow Effects in High-Speed Low-Pressure Turbines.</p><p>This database contains documents that describe the experimental setup, instrumentation, measurement uncertainties, geometries, and the dataset structure related to the SPLEEN C1 PIV measurements.</p><p>The database contains experimental data of the test campaign conducted on the linear cascade codenamed SPLEEN C1 in the VKI high-speed wind tunnel S1/C.</p><p>PIV measurements were performed on the upstream blade-to-blade plane at cascade midspan, passage blade-to-blade plane at cascade midspan, and on a cascade outlet axial plane located 50% of the airfoil axial chord downstream the cascade trailing edge, near the cascade endwall (0-18% of the cascade span).</p><p>The turbine cascade geometry is representative of designs of high-speed low-pressure turbines for next-generation geared turbofan engines.</p><p>The measurement datasets describe the flow through the turbine cascade tested at on- and off-design conditions (cascade exit Reynolds and Mach numbers), with and without a turbulence grid located upstream of the cascade. The database includes measurements of flow velocity, flow angles, Mach numbers, and turbulence quantities.</p><p>The PIV measurements were performed on the <strong>SPLEEN C1 turbine cascade</strong> equipped <strong>WITH a REFERENCE CAVITY ENDWALL</strong> (Cavity Aref), <strong>WITHOUT WAKE GENERATOR</strong> (steady inlet flow), <strong>WITH and WITHOUT TURBULENCE GRID</strong> (different inlet turbulence level).</p><p>The project has received Funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation program under the grant agreement 820883.</p>
Data for "Demographic inequalities in digital spaces in China: The case of Weibo"
<p>These data underlie the results and figures used in the article "Demographic inequalities in digital spaces in China: The case of Weibo" (https://doi.org/10.36190/2023.01). This research was presented at the ICWSM workshop "Data for the wellbeing of the most vulnerable" on June 5, 2023.</p><p>The corresponding workflow can be found in the linked repository.</p><p>`README.md` provides more details.</p>
FAIR Evaluations of University and College Repositories at DataCite Using MetaDIG Mappings for Four Use Cases.
<p>This spreadsheet has the results of an evaluation of FAIRness of 387 University and College DataCite repositories using techniques developed in the MetaDIG project. It is possible to compare scores from different repositories and to create rose diagrams showing the results for any of the repositories.</p>
Received power for Reconfigurable Intelligent Surface use case
<p>Received power in the sampled analyzed area (shown in file area.png) in a scenario with single room with a short wall, and signal transmitter behind this wall. Output was generated with usage of the MATLAB raytracing tool. </p> <p>Analyzed area was sampled in both dimensions, creating 99 points in x-axis (values between 3 meters to 7.9 meters with 5 cm step), and 89 points in y-axis (values between 2.2 meters to 6.6 meters with 5 cm step). Reconfigurable Intelligent Surface was placed with 9 configurations of angle (angle between RIS surface and wall) from -20 to 20 degrees with 5 degree step. This setup created 9 files (for each angle) and inside file there is received power for each analyzed area point (99x89).</p> <p> </p> <p>The work has been funded by the National Science Centre in Poland within the project (no. 2021/43/B/ST7/01365) of the OPUS programme.</p>
HEATDesalination - Case-study lowest-cost optimisation results
<p>Optimisation results for the lowest lifetime cost system consisting of solar photovoltaic (PV), hybrid photovoltaic-thermal (PV-T) and solar-thermal collectors alongside battery and hot-water storage systems for meeting the electrical and thermal (hot-water) needs of three multi-effect distillation (MED) plants.</p> <p>The updated results are from optimisations runs carried out in response to peer-review comments.</p>
A Case-Control Study to Measure Behavioral Risks of Malware Encounters in Organizations
<p>The behavior of enterprise users (e.g. browsing at night or visiting gambling sites) is a potential factor that might increase the chances of malware encounters (e.g. coinminers vs ransomware) on the field. This dataset report the aggregated results of a case-control study on telemetry data collected by Trend Micro, a global cybersecurity vendor, to identify users’ behavioral characteristics that can be used to differentiate cybersecurity risks profiles.</p>
Online Knowledge Production in Polarized Political Memes: The Case of Critical Race Theory (Dataset)
<p>This is the supplementary dataset to the article entitled "Online Knowledge Production in Polarized Political Memes: The Case of Critical Race Theory." This study, completed by Alyvia Walters, Tawfiq Ammari, Kiran Garimella, and Shagun Jhaver, was accepted for publication in <em>New Media & Society </em>in 2024.</p> <p>Description of files:</p> <p>Memes Codebook.dox - Codebook used for qualitative coding of memes.</p> <p>Memes Project.qdpx - NVivo coding project, exported.</p> <p>all_posts.jsonl, clusters.zip, images.zip, & image_data.csv - the complete collection of data and images used for this project. Please see publication in <em>New Media & Society </em>for more information on the use and collection of these items.</p>
Dataset for "Gender and Gender Research in a Research Community: CSTT as a Case Study"
<p>This is the dataset used for and generated during our research for the following publication:<br>Francis Borchardt, Hanna Tervanotko and Saana Svärd "Gender and Gender Research in a Research Community: CSTT as a Case Study." In: Changes in Sacred Texts and Traditions: Methodological Encounters and Debates, eds. Martti Nissinen and Jutta M. Jokiranta. Resources for Biblical Study 106. SBL Press; Atlanta, USA. Pp. 517-544. 2024.<br><br></p>
Flood Hazard Maps and Associated Data for Case Study: Funding rules that promote equity in climate adaptation outcomes
<p>Inundation grids for multiple return periods and multiple scenarios. Please see the underlying study for more details about the methods. The data here can be reproduced following the code and instructions at this repository: https://github.com/CoRE-Lab-UCF/Pollack_et_al_2024/tree/main. Also available here: https://doi.org/10.5281/zenodo.14515896. </p>
A stakeholder-centered determination of High-Value Data sets: the use-case of Latvia
<p>The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.<br> The survey is created for both individuals and businesses.<br> It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.</p> <p>The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)</p> <p>***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***<br> 1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}<br> 2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high<br> 3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)<br> 4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}<br> 5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available<br> 6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}<br> 7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}<br> 8. How would you assess the value of the following data categories?<br> 8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question<br> 10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question<br> 11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question<br> 12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}<br> 13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable<br> 14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}<br> 15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company<br> 16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}<br> 17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}<br> 18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}</p> <p>***Format of the file***<br> .xls, .csv (for the first spreadsheet only), .odt</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p> </p> <p> </p>
H2020 Platone German Demonstrator Use Case 1 Measurement Data
<p>This dataset contains measurement datas collected from mesurements devices in the field (substation, battery storage, etc) during the application of Use Case 1 (Islanding/Maximization of local self-consumption).</p> <p> </p>
MIROC4-ACTM CO2 Inversion flux (2001-2020; case s050_ux4_gvjf)
<p>Details in :</p> <p>Chandra, N., Patra, P. K., Niwa, Y., Ito, A., Iida, Y., Goto, D., Morimoto, S., Kondo, M., Takigawa, M., Hajima, T., and Watanabe, M.: Estimated regional CO<sub>2</sub> flux and uncertainty based on an ensemble of atmospheric CO<sub>2</sub> inversions, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-1039, in review, 2021.</p>
Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455
<p>This is a basic reproduction package for the paper "Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455" by <a href="https://doi.org/10.1093/mnras/stab2202">N. Degenaar et al. (2021)</a>. It provides reduced data products, simulated data and scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data.</p>
Covid-19 Worldwide Data 2021 Cases and Vaccination
<p>This dataset includes information about active cases, accumulative cases, accumulative deaths, daily information, vaccination classified by country of year 2021. </p>
Bias-corrected EURO-CORDEX RCM simulations for the OPTAIN case studies
<p>Bias-corrected EURO-CORDEX RCM simulations are available on a daily timescale for:</p> <p>-period 1981-2099/2100,</p> <p>-6 RCM,</p> <p>-3 scenarios (RCPs 2.6, 4.5 and 8.5),</p> <p>-7 variables (mean, minimum and maximum temperature, precipitation, solar radiation, wind speed at 2 m and relative humidity) and</p> <p>-18 domains and 23 locations within these domains.</p> <p>Bias correction and further downscaling to 0.1° was done using <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> reanalysis data with non-parametric empirical quantile mapping. Moreover, the interpolation of gridded bias-corrected climate model simulations to the locations was made using universal kriging.</p> <p><strong>Organization of the data</strong></p> <p>The name of the files are <em>domain</em>-<em>type</em>.zip, where <em>type</em> is gridded (NetCDF) or point (csv). Each zip file contains multiple files, organized in subfolders: <em>experiment</em>/<em>modelNumber</em>/<em>variable</em>.nc for gridded and <em>experiment</em>/<em>modelNumber</em>/<em>variable-pilotFieldNumber</em>.txt for point data, where <em>experiment </em>is rcp26, rcp45 or rcp85.</p> <p><em>domain and pilotFieldNumber</em></p> <table> <tbody> <tr> <td> <p><strong>domain</strong></p> </td> <td> <p><strong>domain </strong><strong>location (min and max. Longitude, min and max latitude</strong><strong>)</strong></p> </td> <td> <p><strong>pilotFieldNumber</strong></p> </td> <td> <p><strong>pilot field </strong><strong>location (longitude, latitude)</strong></p> </td> <td> <p><strong>case study</strong><strong> number</strong></p> </td> <td> <p><strong>country</strong></p> </td> <td> <p><strong>Name (OPTAIN case study)</strong></p> </td> </tr> <tr> <td> <p>01</p> </td> <td> <p>50.95 51.45 14.55 15.05</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>1</p> </td> <td> <p>DEU</p> </td> <td> <p>Schoeps</p> </td> </tr> <tr> <td> <p>02</p> </td> <td> <p>46.35 47.05 6.55 7.15</p> </td> <td> <p>2</p> </td> <td> <p>46.816667 6.95</p> </td> <td> <p>2</p> </td> <td> <p>CHE</p> </td> <td> <p>Petite Glane</p> </td> </tr> <tr> <td> <p>02_1</p> </td> <td> <p>46.75 47.25 7.25 7.75</p> </td> <td> <p>1</p> </td> <td> <p>46.983333 7.466667</p> <p> </p> </td> </tr> <tr> <td> <p>02_34</p> </td> <td> <p>47.35 47.85 8.35</p> </td> <td> <p>3</p> <p>4</p> </td> <td> <p>47.433333 8.516667</p> <p>47.683333 8.616667</p> </td> </tr> <tr> <td> <p>02_5</p> </td> <td> <p>46.15 46.65 5.95 6.45</p> </td> <td> <p>5</p> </td> <td> <p>46.4 6.233333</p> </td> </tr> <tr> <td> <p>03a</p> </td> <td> <p>46.65 47.15 17.45 17.95</p> </td> <td> <p>1</p> <p>2</p> <p>3</p> <p>4</p> </td> <td> <p>46.92649 17.68246</p> <p>46.9166 17.68976</p> <p>46.91283 17.69754</p> <p>46.91283 17.69723</p> </td> <td> <p>3a</p> </td> <td> <p>HUN</p> </td> <td> <p>Csorsza</p> </td> </tr> <tr> <td> <p>03b</p> </td> <td> <p>46.45 46.95 16.65 17.15</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>3b</p> </td> <td> <p>HUN</p> </td> <td> <p>Felso Valicka</p> </td> </tr> <tr> <td> <p>04</p> </td> <td> <p>52.35 52.85 18.45 18.95</p> </td> <td> <p>1</p> </td> <td> <p>52.597469 18.728617</p> </td> <td> <p>4</p> </td> <td> <p>POL</p> </td> <td> <p>Upper Zglowiaczka</p> </td> </tr> <tr> <td> <p>05</p> </td> <td> <p>46.35 46.85 15.35 15.85</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>5</p> </td> <td> <p>SVN</p> </td> <td> <p>Pesnica</p> </td> </tr> <tr> <td> <p>06</p> </td> <td> <p>46.45 46.95 16.15 16.65</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>6</p> </td> <td> <p>HUN/SVN</p> </td> <td> <p>Kebele/Kobiljski</p> </td> </tr> <tr> <td> <p>07</p> </td> <td> <p>49.85 50.35 4.75 5.25</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>7</p> </td> <td> <p>BEL</p> </td> <td> <p>La Wimbe</p> </td> </tr> <tr> <td> <p>08</p> </td> <td> <p>55.15 55.75 23.55 24.05</p> </td> <td> <p>1</p> <p>2</p> </td> <td> <p>55.522057 23.799235</p> <p>55.42233194 23.82580339</p> </td> <td> <p>8</p> </td> <td> <p>LTU</p> </td> <td> <p>Dotnuvele</p> </td> </tr> <tr> <td> <p>09</p> </td> <td> <p>45.45 45.95 9.65 10.15</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>9</p> </td> <td> <p>ITA</p> </td> <td> <p>Cherio</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>59.45 59.95 10.75 11.25</p> </td> <td> <p>1</p> <p>2</p> <p>3</p> <p>4</p> <p>5</p> <p>6</p> <p>7</p> <p>8</p> </td> <td> <p>59.71949 10.83576</p> <p>59.6833306 10.8833298</p> <p>59.6833306 10.8833298</p> <p>59.665 10.9475</p> <p>59.665 10.9475</p> <p>59.841012 10.903597</p> <p>59.757631 11.072031</p> <p>59.539623 10.856447</p> </td> <td> <p>10</p> </td> <td> <p>NOR</p> </td> <td> <p>Krogstad</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>46.45 46.95 17.55 18.05</p> </td> <td> <p>1</p> <p>2</p> </td> <td> <p>46.658333 17.75583</p> <p>46.656944 17.75833</p> </td> <td> <p>11</p> </td> <td> <p>HUN</p> </td> <td> <p>Tetves</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>49.35 49.85 14.75 15.25</p> </td> <td> <p>1</p> </td> <td> <p>49.616837 15.078266</p> </td> <td> <p>12</p> </td> <td> <p>CZE</p> </td> <td> <p>Cechticky</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>55.85 56.35 25.85 26.45</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>13</p> </td> <td> <p>LVA</p> </td> <td> <p>Dviete</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>59.75 60.25 17.55 18.05</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>14</p> </td> <td> <p>SWE</p> </td> <td> <p>Ingvastaan Lehstaan</p> </td> </tr> </tbody> </table> <p> </p> <p><em>modelNumber</em></p> <table> <tbody> <tr> <td> <p><strong>modelNumber</strong></p> </td> <td> <p><strong>Driving Model (GCM)</strong></p> </td> <td> <p><strong>Ensemble</strong></p> </td> <td> <p><strong>RCM </strong></p> </td> <td> <p><strong>End date</strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>EC-EARTH</p> </td> <td> <p>r12i1p1</p> </td> <td> <p>CCLM4-8-17</p> </td> <td> <p>31.12.2100</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>EC-EARTH</p> </td> <td> <p>r3i1p1</p> </td> <td> <p>HIRHAM5</p> </td> <td> <p>31.12.2100</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>HadGEM2-ES</p> </td> <td> <p>r1i1p1</p> </td> <td> <p>HIRHAM5</p> </td> <td> <p>30.12.2099</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>HadGEM2-ES</p> </td> <td> <p>r1i1p1</p> </td> <td> <p>RACMO22E</p> </td> <td> <p>30.12.2099</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>HadGEM2-ES</p> </td> <td> <p>r1i1p1</p> </td> <td> <p>RCA4</p> </td> <td> <p>30.12.2099</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>MPI-ESM-LR</p> </td> <td> <p>r2i1p1</p> </td> <td> <p>REMO2009</p> </td> <td> <p>31.12.2100</p> </td> </tr> </tbody> </table> <p> </p> <p><em>variable</em></p> <table> <tbody> <tr> <td> <p><strong>variable</strong></p> </td> <td> <p><strong>description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>Tmean</p> </td> <td> <p>Mean temperature</p> </td> <td> <p>°C</p> </td> </tr> <tr> <td> <p>Tmin</p> </td> <td> <p>Min temperature</p> </td> <td> <p>°C</p> </td> </tr> <tr> <td> <p>Tmax</p> </td> <td> <p>Max temperature</p> </td> <td> <p>°C</p> </td> </tr> <tr> <td> <p>prec</p> </td> <td> <p>Precipitation</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>solarRad</p> </td> <td> <p>Solar radiation</p> </td> <td> <p>MJ/m2</p> </td> </tr> <tr> <td> <p>windSpeed</p> </td> <td> <p>Wind speed at 2m</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>relHum</p> </td> <td> <p>Relative humidity</p> </td> <td> <p>%</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Methodolody</strong></p> <p>Bias correction was done using non-parametric empirical quantile mapping with modified method from R package <a href="https://cran.r-project.org/web/packages/qmap/index.html">qmap</a>. Parameters selected were: corrections for each day of the year using a moving windows for a 31 days; 100 quantiles; wet days corrections for precipitation. The reference period is 1981-2010.</p> <p>The interpolation of gridded bias-corrected climate model simulations to the location was made using universal kriging with R packages <a href="https://cran.r-project.org/web/packages/automap/index.html">automap</a> and <a href="https://cran.r-project.org/web/packages/gstat/index.html">gstat</a> with (external) variables x, y, x2, y2, x*y, z, where x is latitude, y is longitude, and z is elevation. For Digital Elevation Model <a href="https://webmap.ornl.gov/wcsdown/dataset.jsp?dg_id=10008_1">Shuttle Radar Topography Mission</a> was used. If there was an error using above mentioned variables, the number of variables was reduced to x, y, x*y, z and if there was still an error to x, y, z.</p> <p> </p> <p><strong>Funding</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862756.</p>
Where2Test Saxony-Czechia COVID-19 new cases dataset
<p>Data in the repository were used in the study "Fine-scale variation in the effect of national border on COVID-19 spread: A case study of the Saxon-Czech border region", published in <a href="https://www.sciencedirect.com/journal/spatial-and-spatio-temporal-epidemiology">Spatial and Spatio-temporal Epidemiology</a>.</p> <p>This repository consists of two files:</p> <p><strong>saxony-westczechia_cases7</strong></p> <p>Weekly numbers of new COVID-19 cases in all municipalities in Saxony and Northwestern Czechia (Liberec, Ústí nad Labem, and Karlovy Vary regions) in the first half of 2021. Data are extracted from the websites <a href="https://www.coronavirus.sachsen.de">coronavirus.sachsen</a> and <a href="https://onemocneni-aktualne.mzcr.cz/covid-19">onemocneni-aktualne.mzcr.cz/covid-19</a>. The missing values were interpolated, and daily values were recalculated to weekly values.</p> <p><strong>municipalities</strong></p> <p>The second file consists of a list of all municipalities with their names, geometries, and population values. For Germany, we used the dataset <a href="https://hub.arcgis.com/datasets/esri-de-content::gemeindegrenzen-2018-mit-einwohnerzahl/about">"Gemeindegrenzen 2018 mit Einwohnerzahl"</a> (© GeoBasis-DE / BKG, Statistisches Bundesamt (Destatis) (2020), <a href="http://www.govdata.de/dl-de/by-2-0">dl-de/by-2-0</a>) as a source of geometries and population sizes of the municipalities (“<em>Gemeinde”</em>) in Saxony. Czech population numbers on the municipality level ("obec") were taken from the <a href="https://www.czso.cz/csu/czso/population-of-municipalities-1-january-2021">Czech Statistical Office</a>, while the geometries were obtained from <a href="https://www.cuzk.cz/ruian/RUIAN.aspx">RÚIAN</a> (@<a href="http://geoportal.cuzk.cz">Czech Office for Surveying, Mapping and Cadastre</a>, 2021). To keep the same geometry detail on both sides of the borders, we applied the Douglas-Peucker simplification algorithm implemented in the Python library <a href="https://github.com/mattijn/topojson">TopoJSON</a>.</p>
Data for Korner et al. "Birds and the Post Tower in Bonn: A case study of light pollution"
<p><strong>Abstract</strong></p> <p>During six consecutive autumn seasons we registered birds that were attracted to an illuminated 41-storey building in Bonn, Germany. Casualties on the ground were disoriented by the light and in most cases collided with the building. All-night observations with numbers of casualties, effective light sources, moon, and weather parameters registered hourly allowed for analyses of the role of these factors for the attraction and disorientation of numerous migratory birds. As expected, the conspicuous façade illumination was responsible for many casualties (fatal or non-fatal). Additionally, the illuminated roof logos and even faint light sources like the emergency lights were attractive and led to casualties. Moon and rain were negatively correlated with casualties, but there was no clear correlation with other weather parameters. Turning off lights was key, but effects of other <em>ex post</em> mitigation measures were limited: shutters were not originally intended for the attenuation of light emissions, control technology was insufficient, and there was a lack of willingness of the building owner to reduce light emissions consistently, even during core bird migration periods. Conservation recommendations are derived from this case study.</p> <p> </p> <p><strong>Vögel und der „Postturm“ in Bonn: Eine Fallstudie zur Lichtverschmutzung</strong></p> <p>In sechs aufeinanderfolgenden Herbstsaisons erfassten wir die Vögel, die an ein beleuchtetes, 41stöckiges Hochhaus in Bonn (Deutschland), den sog. „Postturm “ angelockt wurden. Die Opfer am Boden waren aufgrund der Beleuchtung desorientiert und in den meisten Fällen mit dem Gebäude kollidiert. Basierend auf Beobachtungen während des gesamten Nachtverlaufes wurden die registrierten Opfer, die in Betrieb befindlichen Lichtquellen, Mond und Wettervariablen stundenweise dargestellt, um die Bedeutung dieser Faktoren für die Anlockung und Desorientierung zahlreicher Zugvögel zu analysieren. Die auffällige Fassadenbeleuchtung war erwartungsgemäß für die meisten der Todesfälle und Verletzungen verantwortlich. Zusätzlich führten die beleuchteten Firmenlogos auf dem Dach und sogar schwache Lichtquellen wie die Notbeleuchtung zu Opfern durch Anlockung, auch bei ausgeschalteter Fassadenbeleuchtung. Mond und Regen korrelierten negativ mit den Opferzahlen, aber mit anderen Wettervariablen fehlten klare Korrelationen. Das Abschalten der Beleuchtung war ausschlaggebend, während andere nachträgliche Abhilfemaßnahmen wenig wirksam waren: Sonnenschutzlamellen waren ursprünglich beim Einbau nicht dafür konzipiert, Lichtabstrahlung zu reduzieren, die Steuerungstechnik war fehleranfällig und die Bereitschaft der Gebäudeeigentümerin, Lichtemissionen selbst während der Kernzeiten des Vogelzugs konsequent zu reduzieren, war begrenzt. Aus dieser Fallstudie werden Empfehlungen für Schutzmaßnahmen abgeleitet.</p>
Supplementary Data - "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio"
<p>Supplementary data for the manuscript entitled "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio".</p> <p>Includes data for topographic profiles of wrinkle ridges ("wrinkleridge_profiledata.xlsx"), COULOMB model inputs ("COULOMB_modelinputs.xlsx") and outputs ("COULOMB_modeloutputs.xlsx"), and GIS shapefile data for mapped wrinkle ridges (files labelled "allwrinkleridges" and "studiedwrinkleridges"), topographic profile lines (files labelled "topographicprofilelines"), and the regional profile (files labelled "regionalprofile"). </p>
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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.