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3,225 results for “Case of study”
Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 3)
<p>Hourly photos from camera 3 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.</p>
Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 2)
<p>Hourly photos from camera 2 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.</p>
Data for manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)'
<p>The uploaded zip-file entails the data obtained through four field campaigns performed in the province of Azuay (Ecuador) in the period July 2023 - May 2024, which is used as a basis for the manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)' that was submitted to a Special Issue in the journal Water in 2024. The study aimed at illustrating the impact of urbanisation and drought on the abiotic water conditions of the rivers passing through the studied urban areas.</p> <p>The data includes a subfolder with data obtained from an external website (https://generacioncsr.celec.gob.ec/graficasproduccion/) and aligns with the folder structure of the GitHub-repository that contains the analysis scripts (to be added when the manuscript is accepted). The data file only contains the baseline data, while results can be obtained through running the R-scripts in the GitHub-repository. Additional comments on the analyses are also provided in the analysis scripts.</p> <p><strong>DATA COLLECTION</strong></p> <p>Information on the locations was collected prior to the first field campaign (July 2023) and confirmed in the field (and corrected when necessary). The following variables were registered: Date & Time, Coordinates (latitude and longitude, in WGS84 format), Altitude (in meters above mean sea level), Distance (to a fixed location downstream; being the province border), and Category (River or Stream).</p> <p>Information on the physicochemical conditions was collected directly in the field with a <strong>Horiba U-52</strong> multiprobe. The following variables were registered: Temperature, pH, Electrical conductivity (reference at 25 °C), Oxygen level (as concentration), and Turbidity (in NTU).</p> <p>At each site, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. The multiprobe was rinsed with this sample water and then submerged in the bucket, followed by continuous stirring (to avoid a decrease of the oxygen levels) until the readings stabilised. After stabilisation, readings were recorded on a separate data sheet prior to being digitalised.</p> <p>Information on the nutrient levels was obtained through the collection of water samples in the field and the subsequent analysis in the laboratory. The following nutrients were selected: ammonium, nitrate, nitrite, and orthophosphate. For the analyses, <strong>Merck test kits</strong> (equivalent to USEPA analyses) were used in combination with a Genesys UV-VIS spectrophotometer (Thermofisher).</p> <p><strong>In the field</strong>, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. A polyethylene syringe was rinsed thrice with sample water and subsequently filled prior to being fitted with a 0.45 µm PES filter. About 100 mL of sampled water was filtered and collected in a 250 mL polyethylene bottle that was rinsed with the first 5 mL of filtered water. The bottle was stored in a cooling box and transported to the laboratory.</p> <p><strong>In the laboratory</strong>, the 250 mL bottle was stored at 4 °C until analysis. Within 36 hours, concentrations of ammonium, nitrate, nitrite, and orthophosphate were determined <strong>in triplicate</strong>. More specifically, the following test kits were used to determine said nitrogen and phosphorus concentrations (with quantification range between brackets):</p> <ul> <li>Ammonium: 1.14752.0001 (0.05-3.00 mgN/L)</li> <li>Nitrate: 1.14773.0001 (2-20 mgN/L)</li> <li>Nitrite: 1.14776.0001 (0.02-1.00 mgN/L)</li> <li>Orthophosphate: 1.14848.0001 (0.05-5.00 mgP/L)</li> </ul> <p>Regarding the <strong>spectrophotometric determination</strong>, all analyses were complemented with a blank and a standard with a known concentration of each individual nutrient component. For each nutrient, a specific wavelength was used and the resulting absorbance was converted to the associated nutrient concentration through known factors (similar to the use of calibration curves), after setting the absorbance of the blank as reference absorbance (i.e. a concentration of 0 mg/L). All of the analyses were performed with plastic 1-cm cuvettes during the first campaign, while 5-cm cuvettes were used in the remaining three campaigns due to low nutrient levels (except for nitrate, for which an analysis through 5-cm cuvettes is not supported by the used test kits).</p>
Graphic Illustration of Talks in our Digital Collections Data and Tracking Disease Workshop Section: Case Studies
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Case Studies section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse
<p>Datasets and R source code of the article Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Paulhac H, Gaillard J-M, Beltran-Bech S (2023) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <strong><em>Peer Community Journal</em></strong> 3:e7 http://dx.doi.org/<a href="https://doi.org/10.24072/pcjournal.228">10.24072/pcjournal.228</a></p> <p>This article previously appeared as preprint Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Pauhlac H, Gaillard J-M, Beltran-Bech S (2022) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <em><strong>bioRxiv</strong>, 2022.09.26.509512 </em> https://doi.org/10.1101/2022.09.26.509512</p> <p><em>Peer-reviewed and recommended by <strong>Peer Community in Ecology</strong>: </em> Belsare A (2022) An experimental approach for understanding how terrestrial isopods respond to environmental stressors. <em>Peer Community in Ecology, 100506. </em><a href="https://doi.org/10.24072/pci.ecology.100506"><strong>https://doi.org/10.24072/pci.ecology.100506</strong></a></p>
Documentary sources of case studies on the issues a data protection officer faces on a daily basis
<p>The dataset contains the text of the documents that are sources of evidence used in [1] and [2] to distill our reference scenarios according to the methodology suggested by Yin in [3].</p> <p>The dataset is composed of 95 unique document texts spanning the period 2005-2022. This dataset makes available a corpus of documentary sources useful for outlining case studies related to scenarios in which the DPO finds himself operating in the performance of his daily activities.</p> <p>The language used in the corpus is mainly Italian, but some documents are in English and French. For the reader's benefit, we provide an English translation of the title of each document.</p> <p>The documentary sources are of many types (for example, court decisions, supervisory authorities' decisions, job advertisements, and newspaper articles), provided by different bodies (such as supervisor authorities, data controllers, European Union institutions, private companies, courts, public authorities, research organizations, newspapers, and public administrations), and redacted from distinct professional roles (for example, data protection officers, general managers, university rectors, collegiate bodies, judges, and journalists).</p> <p>The documentary sources were collected from 31 different bodies. Most of the documents in the corpus (a total of 83 documents) have been transformed into Rich Text Format (RTF), while the other documents (a total of 12) are in PDF format. All the documents have been manually read and verified.<br> The dataset is helpful as a starting point for a case studies analysis on the daily issues a data protection officer face. Details on the methodology can be found in the accompanying papers.</p> <p>The available files are as follows:</p> <ul> <li><strong>documents-texts.zip</strong> --> contain a directory of .rtf files (in some cases .pdf files) with the text of documents used as sources for the case studies. Each file has been renamed with its SHA1 hash so that it can be easily recognized.</li> <li><strong>documents-metadata.csv</strong> --> Contains a CSV file with the metadata for each document used as a source for the case studies.</li> </ul> <p>This dataset is the original one used in the publication [1] and the preprint containing the additional material [2].</p> <p>[1] F. Ciclosi and F. Massacci, "The Data Protection Officer: A Ubiquitous Role That No One Really Knows" in IEEE Security & Privacy, vol. 21, no. 01, pp. 66-77, 2023, doi: 10.1109/MSEC.2022.3222115, url: https://doi.ieeecomputersociety.org/10.1109/MSEC.2022.3222115.</p> <p>[2] F. Ciclosi and F. Massacci, "The Data Protection Officer, an ubiquitous role nobody really knows." arXiv preprint arXiv:2212.07712, 2022.</p> <p>[3] R. K. Yin, Case study research and applications. Sage, 2018.</p>
Data for "Saturation of destratifying and restratifying instabilities during down front wind events: a case study in the Irminger Sea"
<p>This archive contains processed data used in the study "Saturation of destratifying and restratifying instabilities during down front wind events: a case study in the Irminger Sea".</p> <p>We are grateful for the financial support of the Natural Environment Research Council (grants NE/L002612/1 and NE/T013494/1).</p> <p>This work used the ARCHER2 UK National Supercomputing Service (https://www.archer2.ac.uk).</p> <p>We would also like to thank Andrew Coward for providing computational support.</p> <p>The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p>The results contain modified GEBCO data produced by the GEBCO Compilation Group (2023) GEBCO 2023 Grid (doi:10.5285/f98b053b-0cbc-6c23-e053-6c86abc0af7b)</p>
Harmonised branded foods dataset for FNS Cloud case studies (2017-2020)
<p>The dataset was compiled by merging and harmonisation of different datasets, which were compiled in four countries (Netherlands, Germany, Slovenia, Switzerland) in period 2017-2020 with the use of different methodologies. Dataset was prepared to support conduction of case studies within the HORIZON2020 FNS Cloud project. The dataset includes selected mandatory food labelling information about nutrition composition of foods/drinks within three categories, that are important for reformulation strategies: soft drinks, breakfast cereals and yoghurts. For one of the included datasets, data aslo include category of fruit and vegetable juices (including nectars).</p>
Information Based Behavioural Intervention Study (Bologna case)
<p>iSCAPE Dataset Reference No. = DS_PD_011, DS_MA_003, DS_PD_016</p> <p>Following datasets are acquired and generated during the implementation of behavioural intervention study in Bologna:</p> <ol> <li>Participant's detailed activity-travel diary </li> <li>Pollutant concentrations of a study area</li> <li>Introductory and Followup questionnaire responses </li> </ol>
Information Based Behavioural Intervention Study (Hasselt case)
<p>iSCAPE Dataset Ref. No. = DS_PD_010, DS_MA_002, DS_PD_015</p> <p>Following datasets are acquired and generated during the implementation of behavioural intervention study in Hasselt:</p> <ol> <li>Participant's detailed activity-travel diary </li> <li>Pollutant concentration maps</li> <li>Introductory and Followup questionnaire responses </li> </ol> <p> </p>
Techno-economic sustainability analysis methodology for conversion routes of renewable feedstock resources to bio-based products – case studies
<p>The dataset provides a set of sustainability principles, criteria and indicators for the evaluation of the conversion routes stage of a bio-based product. The selected case studies on the employment of alternative feedstocks and production of the bio-based products are implemented in order to evaluate the proposed methodology. Mass and energy balances for all case studies, estimated techno-economic metrics, cost of externalities and risk assessment results are provided</p>
A case study on the origin of near-Earth plasma
<p>Summary of data files.</p> <p>This is the dataset for a paper "A case study on the origin of near-Earth Plasma"<br> submitted to JGR-space by Glocer et al</p> <p>The paper conducts multifluid MHD simulations of the magnetosphere by the<br> BATSRUS code, with solar wind input, ionospheric outflow by the PWOM code,<br> and ionospheric potential solver, and inner magnetospehre by the CIMI code.<br> Solutions with and without plasmasphere are considered.</p> <p>All plots in the paper are made with spacepy (specific fork:<br> https://github.com/aglocer/spacepy) or with tecplot</p> <p>The data is organized as follows</p> <p>Top directory:<br> imf.dat: Has the solar wind input as a simple time series. Can be read and<br> plotted with the "ImfInput" tool in spacepy</p> <p>tared Directories with "PS" and "noPS" tags have simulation<br> output with and without plasmasphere. Each has three subdirectories described<br> as follows:</p> <p>GM: Has the "Global Magnetosphere" output from BATSRUS for the images shown.<br> y=0*out are cuts in the y=0 GSM plane and the *log file is the log output<br> containing Dst. Both can be plotted with spacepy pybats. The 3d files are<br> used in a few images and are tecplot binary files and read and plotted with<br> tecplot.</p> <p>PW: Has the "Polar Wind" output from the PWOM code for images shown. The<br> *out files are time dependent binary output for each field line. They can be<br> read and plotted with the pybats.pwom tool in spacepy. North and South<br> indicte northern and southern hemisphere respectively</p> <p>IE: Has the "Ionosphere Electrodynamics" output from the potential solver for<br> plots shown. the *log files have the CPCP data as a function of time. They<br> can be read and plotted with pybats in spacepy</p> <p>IM: Has the "Inner Magnetosphere" output from the CIMI code. The CIMIeq.out<br> file has time dependent snapshots of the solution on the min B surface for<br> plots shown. The *log files have the total energy as a function of time for<br> each species (among other variables). Both files are read and plotted with<br> pybats and pybats.cimi code in spacepy.</p> <p> </p>
Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"
<p>The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration.</p> <p>Country codes: ALPHA-3<br> Unit: Megawatt (electric) (interval average values, i.e. MWh/h)</p> <p><strong>Generation technology types</strong></p> <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> <p><strong>Consumption technology types</strong></p> <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>
Data and Code Accompanying the Study on "Benefits of reflex prediction: A case study of Western Kho-Bwa"
<p>Cite the source of the dataset as:</p> <blockquote> <p>Timotheus A. Bodt and Johann-Mattis List (to appear): Benefits of reflex prediction: A case study of Western Kho-Bwa. Diachronica.</p> </blockquote>
Dataset: Effects of high altitude reservoirs on the structure and function of lotic ecosystems: a case study in Italy
<p>Supporting data for "Effects of high altitude reservoirs on the structure and function of lotic ecosystems: a case study in Italy"</p> <p>Macroinvertebrate community composition</p> <p>Water chemical characteristics</p> <p>Daily mean water temperature and daily temperature variations</p> <p>Results of the leaf bags experiments</p>
Volcano-Independent Seismic Recognition (VI.VSR): case studies with 'geoStudio' graphical interface
<p>Video-documentation of the <strong><em><a href="https://zenodo.org/record/3594080#.X9JP-XVudQJ">geoStudio</a></em> Volcano-Independent Seismic Recognition (VI.VSR) software</strong>, supported by the <a href="https://cordis.europa.eu/project/id/749249"><strong><em>VULCAN.ears</em></strong></a> EU-funded project (H2020-MSCA-IF-2016 Grant) and referenced in the <em>"Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears project" - </em>(Cortés et al., Frontiers in Earth Sciences, 2021) article. <em><strong>VI.VSR aim</strong></em> is to automatically detect and classify volcano-seismic events in any volcano 'V' of the world by models built by other volcanoes data. This provides volcano-seismic catalogs of the given volcano 'V', without the fuss of designing a custom recognition system for it, being specially useful in real-time monitoring scenarios.</p> <p>The material includes 2 VDs:</p> <ol> <li><em>"VI.VSR+geoStudio_intro.mp4"</em> -> introducing the main idea and concepts behind the Volcano-Independent Seismic Recognition (VI.VSR) and presenting <em>geoStudio</em> and its role in the whole <em>VULCAN.ears</em> platform.</li> <li><em>"VI.VSR.by.geoStudio_case.studies.mp4"</em> -> running the VI.VSR case studies presented in the <em>(Cortés et al., 2021)</em> manuscript.</li> </ol> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).</p>
Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects
<p>The dataset contains analyzed projects and modules for the paper "Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects". The contents are the following:</p> <ul> <li><a href="/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/manual-studied-modules.csv?versionId=8258b59c-3f88-487b-a4a3-007cb362a44a">manual-studied-modules.csv</a>: Manually analyzed Maven modules mentioned in Section 5.2</li> <li><a href="https://zenodo.org/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/projects.zip">projects.zip</a>: Instrumented and Mutated Github Projects. Projects list applied mutation changes, and their dynamic and static call graph.</li> </ul>
Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model
<p>Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.</p>
plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"
<p>The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems.<br> Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3.<br> The present dataset contains all the public data built for this case study.</p> <p>The related documentation is included in plan4res deliverable D4.5 </p> <pre>https://doi.org/10.5281/zenodo.3785010</pre>
On the airburst of large meteoroids in the Earth's atmosphere. The Lugo bolide: reanalysis of a case study
<p>Seismic data of the Lugo bolide of January 19, 1993.<br /> </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.