Find research datasets worth reusing

opencc-by-4.0Jun 2024View details →

Data and analysis script for "The (non)effect of personalization in climate texts on credibility of climate scientists: A case study on sustainable travel"

Dataset and analysis script for the article "The (non)effect of personalization in climate texts on credibility of climate scientists: A case study on sustainable travel", under review at Geoscience Communication (https://doi.org/10.5194/egusphere-2024-543)

opencc-by-4.0Jul 2024View details →

Data Set from GREAT Case Study 1

This open data set contains the raw CSV files that were generated in the first GREAT case study, carried out in collaboration with UNDP and using the infrastructure developed by PlanetPlay. A merged file is also provided that may be more convenient for some users who wish to carry out their own analysis.

opencc-by-4.0Jul 2024View details →

Dataset for Sandboxing use case SUC2 related to cyber attacks affecting Wide Area Protection

This dataset is related to the operation of the second KIOS CoE sandboxing use case (SUC2) which inclused 3 scenarios (S1-S3) which examins the behavious a WAP scheme of power grids in case of a short circuit fault and in case of two types of cyber attacks. The description of the architecture of the University of Cyprus/ KIOS CoE sandboxing environmnet used for extracting these datasets along with the full list of scenarios and their detailed implementation are described in the supporting documents. Brief description of each of the 3 scenarios of this SUC2 are provided below. The datasets for the first scenario (S1) of SUC2 examines the operation of a wide area protection scheme in a transmission line which receives data sent from PMUs at the two ends of the lines, when a short-circuit fault occurred in the range of the transmission line between buses 7 and 8 of the system. More details about the scenario SUC2/S1 related to this scenario's dataset can be found in Section 1.3.1 of the SUC2 supporting document. The dataset includes electrical measurements of the current flow in line 7-8 (of the IEEE 9-bus system), in both magnitude and sinusoidal form. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files, which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller as they were sent by the two PMUs, while the sine wave measurements were recorder through the OPAL-RT The datasets for second scenario (S2) of SUC2 investigates the operation of a wide area protection scheme which receives data sent from PMUs when a MITM FDI cyber-attack is conducted on the measurements of bus 7, virtually implemented within the sandboxing, and introduces a multiplicative change to the current measurements before they are received by the Typhoon controller via IEEE C37.118 protocol. Section 1.3.2 of the SUC2 supporting document provides more details about the scenario related to this dataset. This dataset includes electrical measurements of the current flow, in magnitude and sinusoidal format, of the transmission line between buses 7 and 8 of the digital twin of the IEEE 9-bus system. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of magnitude values were recorded by the Typhoon controller, while the data from the sinusoidal waveform were recorder by OPAL-RT.  Thie dataset of the SUC2/S3 examines the operation of a wide area protection scheme which receives data sent from PMUs when a combined MITM with DoS cyber-attack is conducted, as actual attack, in the isolated communication network of the sandboxing environment, disrupting the C37.118 UDP communication exchanged between OPAL-RT 5707, where the digital twin of IEEE 9-bus system was implemented, and Typhoon controller. More details about this scenario associated to this dataset can be found in Section 1.3.3 of the supporting document of SUC2. This dataset includes electrical measurements of current’s flow magnitude of the transmission line between buses 7 and 8 of the digital twin of the IEEE 9-bus system. The dataset was recorded by the Typhoon controller, and it is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. In addition, the dataset includes network traffic packets captured as .pcapng  and .csv files.

opencc-by-4.0Sep 2018View details →

Raw Data of Pilot Plant Runs for CONSENS Project (Case Study 1)

In case study one of the CONSENS project, two aromatic substances were coupled by a lithiation reaction, which is a prominent example in pharmaceutical industry. The two aromatic reactants (Aniline and o-FNB) were mixed with Lithium-base (LiHMDS) in a continuous modular plant to produce the desired product (Li-NDPA) and a salt (LiF). The salt precipitates which leads to the formation of particles. The feed streams were subject to variation to drive the plant to its optimum.  The uploaded data comprises the results from four days during continuous plant operation time. Each day is denoted from day 1-4 and represents the dates 2017-09-26, 2017-09-28, 2017-10-10, 2017-10-17. In the following the contents of the files are explained. NIR_data_AQ15_raw.zip: Contains Bruker binary files (0-Files) of NIR spectrometer at AQ15 (Location is located subsequently to NMR spectrometer) NMR_spectra_raw.zip: Contains Spinsolve files (Binarys of FID and Spectrum, DX-Files) of NMR spectrometer. The use of DX-files files is not recommended. PCS_data_csv.zip: Contains csv-files of the process control system (PCS) including data of mass flow controlers (*_Bilanz.csv), filling level (*_FillLe.csv), pressures (*_pres), temperatures (*_Temp), position of valves (*_Valves). Relevant labels are: BP13 = LiHMDS storage tank, BP12 = aniline storage tank, BP12 = o-FNB storage tank, CM003 and CM004 = tubular reactors, T0041 and T005 = Temperature at reactor exits, P009 and P003 = Pressure at reactor inlets, P006 = Pressure at reactor exits. housing_data_NMR.csv: Contrains data of NMR enclosure of all four days. Each columns from left to right represent timestamps, bypass pressure (bar), bypass temperature (°C), Gasalarm (logical), bypass actual flowrate (g min-1),  bypass flowrate setpoint (g min-1), bypass density (kg m3) matlab_variables_explanation.xlsx: Explanation of variables used in matlab structure "data_validation_run". data_validation_run.mat: Matlab structure containing most relevant process data including NMR results, NIR results, housing data of NMR, and process control system data.

opencc-by-4.0Aug 2019View details →

Investigating instability architectural smells evolution: an exploratory case study

This is the dataset used in our case study on architectural smells evolution. We tracked smells from 524 versions among 14 open source Java systems.  More information can be found in our ICSME'19 paper titled: "Investigating instability architectural smells evolution: an exploratory case study". Additionally, you can find the tool on GitHub: <a href="https://github.com/darius-sas/astracker">https://github.com/darius-sas/astracker</a>

opencc-zeroAug 2024View details →

The data that support the findings of a review paper "From urban data to city-scale models: A review of traffic simulation case studies"

This dataset contains the data that were used in a review paper "From urban data to city-scale models: A review of traffic simulation case studies". It contains the following files: <ul> <li>keywords with counts.txt -  list of keywords and their counts in the considered corpus of traffic simulation case studies. The data were used to produce Figure 2 and Figure 3 in the paper.</li> <li>Papers analysis.xlsx - Excel file containing the data on the reviewed studies. The document has the following sheets: <ul> <li> Appendix A - contains a table short reference, location, simulation period, spatial scale, simulated units and marked categories for a paper;</li> <li>Geography - contains data on geographical distribution of simulated areas between world regions and countries, these data were used to produce Figure 4 in the paper;</li> <li>Software tools - contains data on simulation tools used in the studies. </li> <li>Journals and conferences - contains data on where the reviewed papers were published.</li> </ul> </li> </ul>

opencc-by-nc-4.0Sep 2024View details →

Case Data Lahore High Court 2013 v1.0

Case-level judicial data from Lahore High Courts extracted from District Judiciary Punjab’s case management system website for the year 2013, to establish a baseline to empirically understand Pakistan’s legal system.  <div> <div>The data repository contains the following files:</div> <ul> <li>raw_data_lahore_highcourt_2013_v1.xlsx: This file contains the raw data extracted from the Punjab High Court website.</li> <li>to_clean_data_lahore_highcourt_2013_v1.do: This file contains the Stata do file used to clean the raw data.</li> <li>stata_cleaned_data_lahore_highcourt_2013_v1.dta: This file contains the cleaned data in Stata format.</li> <li>cleaned_data_lahore_highcourt_2013_v1.csv: This file contains the cleaned data in CSV format. The variable names for cleaned data are human-readable and mostly make clear what information the variable contains. The variables available are as follows: <ul> <li>case_no: Case Number</li> <li>case_status: Case Status</li> <li>inst_no: A unique number representing the case in the system</li> <li>inst_date: Date when the case was officially filed or registered in the court.</li> <li>type_of_case: The type or category of the case (e.g., First Class Cases, Bail Applications).</li> <li>offense: The description of crime or violation for which the case was filed (e.g., Cheating, Rape, etc).</li> <li>procedure_or_documentation: Indicates the type of procedure involved, such as pre-arrest bail or other legal processes.</li> <li>fir_no: The First Information Report (FIR) number associated with the case.</li> <li>fir_year: The year in which the FIR was registered.</li> <li>police_station: The name of the police station where the FIR was lodged.</li> <li>primary_offence: The main crime or charge the case deals with</li> <li>presiding_judge: The name of the judge presiding over the case.</li> <li>bench: The judicial authority or composition of the bench (e.g., Civil Judge or Additional District).</li> <li>decision_date: The date when the court issued a decision or verdict.</li> <li>decision_type: The type of decision made by the court (e.g., Acquittal, Conviction).</li> <li>contested: Indicates whether the case was contested or uncontested.</li> <li>acquitted_convicted: Indicates whether the accused was acquitted or convicted.</li> </ul> </li> </ul>   </div>

opencc-by-4.0Sep 2024View details →

Data for Tradeoff Analysis of Recovery Pathways post Superstorm Sandy: a New Jersey Case Study - v0.1

Data repository for the manuscript titled: Tradeoff Analysis of Recovery Pathways post Superstorm Sandy: a New Jersey Case Study. This repository includes all of the input data, processed data, and output data used for the project. This is a working version of our Tradeoff Analysis ready for interested users, peer reviewers, and others to run.  README file on GitHub: https://github.com/Laura-Geronimo/Geronimo-etal_2024_NaturalHazardsReview/blob/main/README.md

Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 1)

Hourly photos from camera 1 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.

Roadmapping information for each Wider Uptake case study

Database summarizing information for each roadmapping step and for each of the WIDER UPTAKE H2020 project's case studies

Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 3)

Hourly photos from camera 3 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.

Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 2)

Hourly photos from camera 2 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.

opencc-by-4.0Nov 2024View details →

Data for manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)'

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. 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. DATA COLLECTION 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). Information on the physicochemical conditions was collected directly in the field with a Horiba U-52 multiprobe. The following variables were registered: Temperature, pH, Electrical conductivity (reference at 25 °C), Oxygen level (as concentration), and Turbidity (in NTU). 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. 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, Merck test kits (equivalent to USEPA analyses) were used in combination with a Genesys UV-VIS spectrophotometer (Thermofisher). In the field, 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. In the laboratory, the 250 mL bottle was stored at 4 °C until analysis. Within 36 hours, concentrations of ammonium, nitrate, nitrite, and orthophosphate were determined in triplicate. More specifically, the following test kits were used to determine said nitrogen and phosphorus concentrations (with quantification range between brackets): <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> Regarding the spectrophotometric determination, 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).

opencc-by-4.0Jun 2024View details →

Graphic Illustration of Talks in our Digital Collections Data and Tracking Disease Workshop Section: Case Studies

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.

opencc-by-4.0Jun 2021View details →

Stocktaking GO FAIR Discovery IN - Use cases, infrastructure

In order to build a better ecosystem for data discovery tools the Data Discovery Implementation Group of GO Fair (https://www.go-fair.org/implementation-networks/overview/discovery) collected use cases between 2019 and 2020 from a variety of sources. We also detail the ‘Actors’ for these use cases and the ‘Source’ providing links, whenever possible. Since we found over a hundred individual use cases, we decided to cluster them to provide a better overview. The clustering, as well as the results of a small survey among data infrastructure specialists to find how they rate the importance of the clusters are detailed in the documentation to this dataset, a draft of which can currently be found <a href="https://docs.google.com/document/d/1sq78eCFYgmWcMFYcbNonA2KrkUO1qdGr7d49tHuRcRM/edit?usp=sharing">here</a>. The code and data to produce the figures in the documentation are available as R code in the GO_FAIR_Discovery_Use_case-master.zip file. The use cases themselves are available as Excel sheet and csv.

opencc-by-4.0Aug 2021View details →

COVID19 Flow-Maps Daily Cases Reports

This repository contains COVID-19 data for Spain, including daily cases at the level of autonomous communities as well as provinces, and higher spatial resolution for several autonomous communities (eight out of the nineteen autonomous communities publish reports with local daily COVID-19 cases at the level of municipalities or Basic Health Areas). Each record has an identifier, the associated date, the corresponding identifier of the layer and code of the region and a set of COVID-19 related fields, which include the number of new cases (daily incidence) and total cases. <ul> <li> Autonomous Communities: ES.covid_cca </li> <li> Provinces: ES.covid_cpro </li> <li> Higher spatial resolution: <ul> <li> Principado de Asturias: 03.covid_cumun </li> <li> Cantabria: 06.covid_cumun </li> <li> Castilla y Leon: 07.covid_abs </li> <li> Cataluña/Catalunya: 09.covid_abs </li> <li> Comunitat Valenciana: 10.covid_cumun </li> <li> Comunidad de Madrid: 13.covid_abs </li> <li> Comunidad Foral de Navarra: 15.covid_abs </li> <li> País Vasco/Euskadi: 16.covid_abs </li> </ul> </li> </ul> For information about data sources, visit: <a href="https://flowmaps.life.bsc.es/flowboard/data">https://flowmaps.life.bsc.es/flowboard/data</a>

Virtual Reality Dataset used for Proof of Concept in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)

This dataset contains the dataset used in the Virtual Reality POC for the validation of the Conflict Detection and Resolution (CD&R) use case. This dataset represent a extract of different (using K-means) candidate solution, either good or bad ones.

Solutions and Genetic algorithm dataset of the Scenarios used for the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION )

This dataset contains the solution of the scenarios used for one of the validation of the ARTIMATION project: Conflict Detection and Resolution (CD&R) use case (link). The solution are computed by a Genetic Algorithm developped by Nicolas Durand. Inside, one can find: -One archive, "GA_Scenario_Solution_Dataset.zip", containing 10 couple of files (so 20 files). Each couple of file "sol_X_1.csv" and "sols_X_1.csv" are reciprocally the solutino given by the Genetic Algorithm to scenario X, and all the candidate solution explroed by the GA while solving scenario X. This archive also contain other versions of the solutions made by the GA with other parameters. -One archive, "GA_Toy_Dataset.zip" , containing solution to random scenarios, used to develop the first interfaces. Those solutions are used to developp the heatmatrix and heatmaps of the project (link), and visualisations for the validation (link).

Heatmatrix and Heatmap Layers and Alternatives used in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)

This dataset contains the visualisations of the solutions of Conflict Detection and Resolution (CD&R) use case. The solution are computed by a Genetic Algorithm developped by Nicolas Durand. Inside, one can find: -One archive, "Heatmatrix.zip" , containing the heatmatrix creating using the solutions dataset. -One archive, "Heatmaps_Layer_Alternatives.zip", containing all the layers created and used to created the heatmaps, the heatmaps, and alternative heatmaps (with other candidate solutions). Those layers and heatmaps are used to develop other visualisation used in the validation.

Validation Videos and eXplainable levels used in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)

This dataset contains the explaination levels and the video used in the validation of the Conflict Detection and Resolution (CD&R) use case. The solution are computed by a Genetic Algorithm developped by Nicolas Durand. Inside the dataset, one can find: -One archive, " Validation_Videos_Traffic.zip ", containing the video of traffic of every scenario. -One archive, " Validation_XAI_levels.zip", containing the Blackbox, Heatmap, and Storyboard eXplainable levels for each scenario. The videos and XAI levels are used in the validation exercice.