Find research datasets worth reusing

Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 1 FOV2

Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 1 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - CONROL CASE 2 FOV2

Image-based data set of a post-mortem lung sample from a COVID-19 donor (CONTROL CASE 2 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 3 FOV2

Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 3 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

opencc-by-sa-4.0Jul 2020View details →

Coronavirus disease (COVID-19) case data - South Africa

COVID 19 Data for South Africa created, maintained and hosted by <a href="https://dsfsi.github.io/">DSFSI research group</a> at the University of Pretoria Disclaimer: We have worked to keep the data as accurate as possible. We collate the COVID 19 reporting data from NICD and South Africa DoH. We only update that data once there is an official report or statement. For the other data, we work to keep the data as accurate as possible. If you find errors let us know.  See original GitHub repo for detailed information <a href="https://github.com/dsfsi/covid19za">https://github.com/dsfsi/covid19za</a>

opencc-by-4.0Mar 2023View details →

Data for Accident Severity Prediction Modelling for Indian Highways Case Study

Accident Data: Road accidents data is of Indian Highways sections Pune-Solapur and Bengal (BAEL) Section.  For the Pune-Solapur Section of NH-9, which is located between Km.144/400 and Km. 249/000 in the state of Maharashtra, accident dates from 2013 to 2018. For the Six-Laning of Barwa-Adda-Panagarh Section of NH-2, which includes Panagarh Bypass and is located in the States of Jharkhand and West Bengal Stretch, accident dates from 2015 to 2019 for the stretch between km 398.240 and km 521.120.  The data is sorted and analyzed using Random Forest Machine Learning for Accident Severity Prediction Modelling. Acknowledgement: We highly acknowledge the two organizations 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company for making the raw data available. Source: 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company.

opencc-by-4.0Feb 2023View details →

OpenFOAM cases for the Validation of the CHT Model

This dataset contains the underlying data for the paper " Conjugate Heat Transfer Modeling of a Cold Plate Design for Hybrid-Cooled Data Centers” in Energies Journal. https://www.mdpi.com/1996-1073/16/7/3088  Numerical simulations are performed using the open-source CFD code OpenFoam. Features of the numerical model described in the paper can be summarized as: <ul> <li>This data set contains OpenFoam cases for the validation of the thermal model with the experimental data in the literature (Saitoh et al. 1993) using both buoyantPimpleFoam and chtMultiRegionFoam solvers.</li> </ul> Saitoh, T.; Sajiki, T.; Maruhara, K. Benchmark solutions to natural convection heat transfer problem around a horizontal circular cylinder. Int. J. Heat Mass Trans. 1993, 36, 1251–1259. <ul> <li>A multi-region unstructured mesh was created using Salome software and exported as unv files. The generated unv files can be found in the corresponding directories.</li> <li>Allmesh script imports regions from the unv files to the OpenFoam and runs createPatch file for the application of boundary conditions .</li> <li>Allrun script runs transient simulation using parallel computing.</li> <li>postProcess script compares case results with experimental results and generates a plot in the directory results.</li> <li>Flow inside air and water regions are considered as laminar due to the low Reynolds numbers.</li> <li>Numerical schemes used in the solutions of constitutive equations are carefully selected to obtain results consistent with the experimental data.</li> </ul> A new function object is developed for the calculation of the Nusselt number on the cylinder. This library can be downloaded via the following link and sould be  compiled before cases are run:   <a href="https://github.com/DSTECHNO/NusseltNumber">https://github.com/DSTECHNO/NusseltNumber</a> FreeConvectionBPF.tar.gz: OpenFoam files and scripts for the transient simulation of free convection case using buoyantPimpleFoam solver. FreeConvectionCHT.tar.gz: OpenFoam files and scripts for the transient simulation of free convection case using chtMultiRegionFoam solver. ForcedConvectionBPF.tar.gz: OpenFoam files and scripts for the transient simulation of forced convection case using buoyantPimpleFoam solver. ForcedConvectionCHT.tar.gz:  OpenFoam files and scripts for the transient simulation of forced convection case using chtMultiRegionFoam solver.

opencc-by-4.0Mar 2023View details →

Dataset for "Reproducibility and FAIR Principles: The Case of a Segment Polarity Network Model"

Results of random sampling the segment polarity network with the simulator COPASI. These results correspond to Fig. 2 and Table 2 of von Dassow et. al (2000) (doi:10.1038/35018085). The random sampling was carried out with file vonDassow2000_1x4_alt.cps  with COPASI version 4.39 selecting the appropriate parameter set named (1-7) and setting the number of repeats in the parameter scan task to the desired number. Full results of sampling are in files prefixed with the row number of Table 2 of von Dassow et. al (2000) and extension .tsv. Results with scores below 0.2 are in corresponding files with the word "-hits" in the filename. Includes also results from a time course simulation of this model using four different simulators (COPASI, Tellurium, Amici, and VCell). Finally also contains a study on multistability carried out by random sampling of parameters and initial conditions (run with COPASI). Markdown file README.md contains more detailed explanation. See also https://github.com/pmendes/models/tree/main/vonDassow2000

opencc-by-4.0Jul 2022View details →

Dissemination of information in event-based surveillance, a case study of Avian Influenza - dataset

This dataset contains a set of tables corresponding to the manual analysis of outbreak-related reports detected by two event-based surveillance tools, PADI-web and HealthMap, supporting the submitted article "Dissemination of information in event-based surveillance, a case study of Avian Influenza". The reports were published between 1 July 2018 and 30st June 2019 and described one or several avian influenza outbreaks.  We collected 337 reports from PADI-web and 115 from HealthMap. Two epidemiologists identified all the reported events in the news, and classified them as official (notified to the World Organization for Animal Health) or non-official. In order to trace back the source of the event’s information, the epidemiologist manually traced the information pathway of all events mentioned in the PADI-web and HealthMap news. The pathway was deducted from the sources cited in the news. When a source was cited with a hyperlink, we followed the hyperlink to retrace the information pathway as far as possible to the primary source. For each cited source, we created a pair of emitter SE and receptor sources SR. We labelled each new source with its type (e.g. online news source, national veterinary authority, etc.). We also recorded their geographical focus (local, national or international) and their specialization in the animal health news coverage (general or specialized). The script for data analyses is available at https://github.com/SarahVal/EBS-network.

opencc-by-4.0Oct 2022View details →

Electoral competition and strategic intra-coalition oversight in parliament: the case of the bipolar Belgian polity (replication data)

Replication dataset for B. de Vet (2023). Electoral competition and strategic intra-coalition oversight in parliament: the case of the bipolar Belgian polity (In: Political Studies Review)

ipaast-czo case study: Dehesa Boyal de Botija

VV01 (“Cerca del cementerio” sector). Data set description. One single geophysical method was used: <ul> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Although topography obliged to split the survey area in 3 independent data sets, after correction and merging of all the data in one single data set it was observed that, considering the small time lapse between each one and homogeneous weather conditions,  no significant discontinuity  in measured data was observed.</li> </ul> The interest on testing EMI survey in this dehesa environment within the framework of the IPAAST project was motivated with a triple objective: <ul> <li>The aim of exploring off-site activities around the hillfort of Villasviejas: traces of intensive agriculture, industrial activities, dumping areas, mining etc.</li> <li>The aim of assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and make a regression analysis on the potential distribution of arable lands during the Iron Age in the area.</li> <li>The aim of combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.</li> </ul> VV01 sector corresponds to an area of dehesa named “Cerca del cementerio” (“cemetery enclosure”): it is a land plot 70m SW of the hillfort with  a surface of 5240 sq. m. Previous knowledge of the area revealed the presence of a high density of archaeological materials, but there was no clear evidence of buried structures. A particular feature of this sector was the great depth of soil deposits within the walled enclosure, in great contrast with the surrounding fields. Data sets: <ul> <li> EMI04. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> </ul> VV02 (Mercadillo sector). Data set description. Two main methods were used: <ul> <li>Magnetic survey: a dual-sensor gradiometer system (Grad602 Bartington)</li> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Dense vegetation and topography obliged to split the survey area in 3 independent data sets.</li> </ul> Additionally several areas were surveyed with GPR (Nogging system of Sensor&Software). The interest on testing different survey methods in the framework of the IPAAST project was motivated with a triple objective: <ul> <li>The aim of exploring off-site activities around the hillfort of Villasviejas: traces of intensive agriculture, industrial activities, dumping areas, mining etc.</li> <li>The aim of assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and make a regression analysis on the potential distribution of arable lands during the Iron Age in the area.</li> <li>The aim of combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.</li> </ul> VV02 sector corresponds to an area of dehesa named “El Mercadillo (“little market”). It is located 150 m south of the hillfort of Villasviejas and covers a surface of approx. 3500 sq m. In this sector was excavated in the 1980s a funerary area corresponding to the earliest period of the hillfort (IV-II centuries B.C). Analysis of LiDAR data revealed that in the same area there were several topographic features that could be linked with off-site activity during the protohistoric and early roman period. Data sets: <ul> <li> EMI01. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li> EMI02. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>EMI03. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>MAG01. <ul> <li>01 Vector limits of survey area.</li> <li>02 Vector point file of vertices of survey area.</li> <li>03 Grid composite.</li> <li>04 Raster interpolation of magnetic data.</li> </ul> </li> </ul>

ipaast-czo case study: The Rinconada Estate.

The interest in testing different survey methods within the framework of the IPAAST project was motivated by three objectives: <ul> <li>identifying evidence of rural life in the hinterland of the Roman colony of Augusta Emerita, with special attention to forms of resilience and diversification of agrarian activities beyond the areas of highest productivity of the alluvial plain of the Guadiana River.</li> <li>assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and perform a regression analysis to assess the potential distribution of arable lands in the area from Roman times to the present.</li> </ul> combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.   Attention was focused on a sector of the estate where two elements were coincident 1) preliminary evidence suggested the estate’s highest concentration of archaeological finds. 2) land plots within the estate used for grazing where LIFE Adapt experiments were undertaken. This area encompassed approximately. 6.5 ha.   2 geophysical methods for the exploration were used: <ul> <li>Magnetic survey: a 2 sensors gradiometer system was used (Grad602 Bartington). Data sets: <ul> <li>RC_MAG <ul> <li>01 Vector limits of survey area.</li> <li>02 Vector point file of vertices of survey area.</li> <li>03 Grid composite.</li> <li>04 Raster interpolation of magnetic data.</li> </ul> </li> </ul> </li> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Data sets: <ul> <li>RC_EMI01. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data (raw data)</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>RC_EMI02. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> </ul> </li> </ul>

ipaast case study: Wiltshire yield and EM survey data

These data were collected by Soyl Ltd as part of a commercial agricultural soil survey. They were reprocessed by the University of Ghent ipaast team.

ipaast-czo case study OptRX data: Montalcino (SI, Italy)

These data were collected as part of a case study for the ipaast project. The aim of the survey was to produce datasets interoperable for applications in archaeology and precision agriculture. The OptRx® Crop Sensors (AgLeader Technology, Ames, IO, USA) measure the reflectance in the 630–685 nm (red), 695–750 nm (RE red edge) and 760–850 nm (NIR—Near InfraRed) wavebands. Using those wavebands, NDVI and NDRE indexes are calculated. NDVI and NDRE are vegetative indexes obtained from the red, red-edge and NIR wavebands with formulas 1 and 2:     NDVI = NIR−REDNIR+RED ; NDRE= NIR−RENIR+RE   The two index values range from -1 (bare ground or water) to 1 (highly vigorous vegetation).   To collect data, the sensor was mounted on a ground vehicle, a Kubota B2420 tractor. The sensor was paired with a GNNS receiver, GPS 6500 from AgLeader Technology (Ames, IO, USA). The instrumentation was coupled with the hardware and the rough book (Panasonic ToughPad FG-Z1, Panasonic Core. It was possible to install the sensor facing the ground using a metal bracket positioned on the front of the tractor. The sensor was positioned 1.15 m from the ground, emitting a rectangular footprint of 1.14 m in length and 20cm in width. The data were collected every 30 cm in alternate rows. 12 rows in total were analysed, covering a surface of 1.07 ha.  Data were processed on QGIS. First, the data was interpolated with the Inverse Distance Weighting (IDW) function. The function was set up with a distance coefficient P of 4, with 40 rows and 98 columns. A Gaussian filter with a standard deviation value of 2 and a range of research of 3 was subsequently applied to create a representative raster.

opencc-by-4.0Oct 2022View details →

openENTRANCE - Case Study 1 - Residential Demand Response - Data and Scripts

Data files and Python and R scripts are provided for Case Study 1 of the openENTRANCE project. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are full battery electric vehicles (EV), storage heater (SH), water heater with storage capabilitites (WH), air conditiong (AC), heat circulation pump (CP), air-to-air heat pump (HP), refrigeration (includes refrigerators (RF) and freezers (FR)), dish washer (DW), washing machine (WM), and tumble drier (TD). The data for the study uses represenative hours to describe load expectations and constraints for each residential device - hourly granularity from 2020 to 2050 for a representative day for each month (i.e. 24 hours for an average day in each month). The aggregated final results are in Full_potential.V9.csv and acheivable_NUTS2_summary.csv. The file metaData.Full_Potential.csv is provided to guide users on the nomenclature in Full_potential.V9.csv and the disaggregated data sets.The disaggregated loads can be found in d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv while the disaggregated maximum capacities p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv.  Full_potential.V9.csv shows the NUTS2 level unadjusted loads for the residential devices using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050). These summaries have allready adjusted the disaggregated loads with direct load participation rates from participation_rates_country.csv. A detailed overview of the data files are provided below. Where possible, a brief description, input data, and script use to generate the data is provided. If questions arise, first refer to the publication. If something still needs clarification, send an email to ryano18@vt.edu. Description of data provided <ol> <li>Achievable_NUTS2_summary.csv <ol> <li>Description <ol> <li>Average hourly achievable direct load potentials for each NUTS2 region and device for 2020, 2022, 2030,2040, 2050</li> </ol> </li> <li>Data input <ol> <li>Full_potential.V9.csv</li> <li>participation_rates_country.csv</li> <li>P_inc_SH.csv</li> <li>P_inc_WH.csv</li> <li>P_inc_HP.csv</li> <li>P_inc_DW.csv</li> <li>P_inc_WM.csv</li> <li>P_inc_TD.csv</li> </ol> </li> <li>Script <ol> <li>NUTS2_acheivable.R</li> </ol> </li> </ol> </li> <li>COP_.1deg_11-21_V1.csv <ol> <li>Description <ol> <li>NUTS2 average coefficient of performance estimates from 2011-2021 daily temperature</li> </ol> </li> <li>Data <ol> <li>tg_ens_mean_0.1deg_reg_2011-2021_v24.0e.nc</li> <li>NUTS_RG_01M_2021_3857.shp</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>COP_from_E-OBS.R</li> </ol> </li> </ol> </li> <li>Country dd projections.csv <ol> <li>Description <ol> <li>Assumptions for annual change in CDD and HDD</li> <li>Spinoni, J., Vogt, J. V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A., & Füssel, H. M. (2018). Changes of heating and cooling degree‐days in Europe from 1981 to 2100. International Journal of Climatology, 38, e191-e208.</li> <li>Expectations for future HDD and CDD used the long-run averages and country level expected changes in the rcp45 scenario</li> </ol> </li> </ol> </li> <li>EV NUTS projectionsV5.csv <ol> <li>Description <ol> <li>NUTS2 level EV projections 2018-2050</li> </ol> </li> <li>Data input <ol> <li>EV projectionsV5_ave.csv <ol> <li>Country level EV projections</li> </ol> </li> <li>NUTS 2 regional share of national vehicle fleet <ol> <li>Eurostat - Vehicle Nuts.xlsx</li> </ol> </li> </ol> </li> <li>Script <ol> <li>EVprojections_NUTS_V5.py</li> </ol> </li> </ol> </li> <li>EV_NVF_EV_path.xlsx <ol> <li>Description <ol> <li>Country level – EV share of new passenger vehicle fleet</li> <li>From: Mathieu, L., & Poliscanova, J. (2020). Mission (almost) accomplished. Carmakers’ Race to Meet the, 21.</li> </ol> </li> </ol> </li> <li>EV_parameters.xlsx <ol> <li>Description <ol> <li>Parameters used to calculate future loads from EVs</li> <li>Wunit_EV – represents annual kWh per EV</li> <li>evLIFE_150kkm <ol> <li>number of years</li> <li>represents usable life if EV only lasted 150 thousand km. Hence, 150,000/average km traveled per year with respect to country (this variable is dropped and not used for estimation).</li> </ol> </li> <li>Average age/#years assuming 150k life – represents <ol> <li>Number of years</li> <li>Average between evLIFE_150kkm and average age of vehicle with respect to the country</li> </ol> </li> </ol> </li> </ol> </li> <li>full_potentialV9.csv <ol> <li>Description <ol> <li>Final data that shows hourly demand (Maximum Reduction) and (Maximum Dispatch for each device, region, and year. <ol> <li>This data has not been adjusted with participation_rates_country.csv</li> <li>Maximum dispatch is equal to max capacity – hourly demand with respect to the device, region, year, and hour.</li> </ol> </li> </ol> </li> <li>Script <ol> <li>Full_potentialV9.py</li> </ol> </li> </ol> </li> <li>gils projection assumptions.xlsx <ol> <li>Description <ol> <li>Data from: Gils, H. C. (2015). Balancing of intermittent renewable power generation by demand response and thermal energy storage.</li> <li>A linear extrapolation was used to determine values for every year and country 2020-2050. AC – Air Conditioning, SH – Storage Heater, WH – Water heater with storage capability, CP – heat circulation pump, TD – Tumble Drier, WM – Washing Machine, DW -Dish Washer, FR – Freezer, RF – Refrigerator. The results are in the files shown below. <ol> <li>nflh – full load hours <ol> <li>nflh_ac.csv</li> <li>nflh_cp.csv</li> </ol> </li> <li>wunit – annual energy consumption <ol> <li>Wunit_rf_fr.csv</li> </ol> </li> <li>Pcycle – power demand per cycle <ol> <li>Pcycle_wm.csv</li> <li>Pcycle_dw.csv</li> <li>Pcycle_td.csv</li> </ol> </li> <li>Punit – power damand for device <ol> <li>Punit_ac.csv</li> <li>Punit_cp.csv</li> </ol> </li> <li>r – country level household ownership rates of residential device <ol> <li>rfr.csv</li> <li>rrf.csv</li> <li>rwm.csv</li> <li>rtd.csv</li> <li>rdw.csv</li> <li>rac.csv</li> <li>rwh.csv</li> <li>rcp.csv</li> <li>rsh.csv</li> </ol> </li> <li>Script <ol> <li>openENTRANCE projections.py</li> </ol> </li> </ol> </li> </ol> </li> </ol> </li> <li>heat_pump_hourly_share.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From ENTROS TYNDP – Charts and Figures <ol> <li><a href="https://2020.entsos-tyndp-scenarios.eu/download-data/#download">https://2020.entsos-tyndp-scenarios.eu/download-data/#download</a></li> </ol> </li> </ol> </li> </ol> </li> <li>hourlyEVshares.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From My Electric Avenue Study <ol> <li><a href="https://eatechnology.com/consultancy-insights/my-electric-avenue/">https://eatechnology.com/consultancy-insights/my-electric-avenue/</a></li> </ol> </li> </ol> </li> </ol> </li> <li>HP_transitionV2.csv <ol> <li>Description <ol> <li>Used to create Qhp_thermal_MWh_projectedV2.csv</li> <li>Final_energy_15-19 <ol> <li>Average final energy demand for the residential heating sector between 2015-2019</li> </ol> </li> <li>Final_energy_15-19_nonEE <ol> <li>Average final energy demand for the residential heating sector for energy sources that are not energy efficient between 2015-2019 (see paper for sources)</li> </ol> </li> <li>Final_energy_15-19_nonEE_share <ol> <li>share of inefficient heating sources</li> </ol> </li> <li>HP_thermal_2018 <ol> <li>Thermal energy provided by residential heat pumps in 2018</li> </ol> </li> <li>HP_thermal_2019 <ol> <li>Thermal energy provided by residential heat pumps in 2019</li> </ol> </li> <li>See publication for data sources</li> </ol> </li> </ol> </li> <li>Nflh_ac.csv, nflh_cp.csv <ol> <li>See gils projection assumptions.xlsx</li> </ol> </li> <li>nhhV2 <ol> <li>Description <ol> <li>Expected number of households for NUTS2 regions for 2020-2050</li> <li>See publication for data sources</li> </ol> </li> <li>Script <ol> <li>EUROSTAT_POP2NUTSV2.R</li> </ol> </li> </ol> </li> <li>NUTS0_thermal_heat_annum.csv <ol> <li>Description <ol> <li>Country level residential annual thermal heat requirements in kWh</li> <li>Used to determine maximum dispatch in openENTRANCE final V14.py</li> <li>Mantzos, L., Wiesenthal, T., Matei, N. A., Tchung-Ming, S., Rozsai, M., Russ, P., & Ramirez, A. S. (2017). JRC-IDEES: Integrated Database of the European Energy Sector: Methodological Note (No. JRC108244). Joint Research Centre (Seville site).</li> </ol> </li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Description <ol> <li>Maximum capacity – load for a device can never exceed maximum capacity</li> </ol> </li> <li>Data <ol> <li>gils projection assumptions.xlsx</li> </ol> </li> <li>Script <ol> <li>openENTRANCE final V14.py</li> </ol> </li> </ol> </li> <li>P_inc_DW.csv, P_inc_HP.csv, P_inc_SH.csv, P_inc_TD.csv, P_inc_WH.csv, P_inc_WM.csv, SAMPLE_PINC.csv <ol> <li>Description <ol> <li>Unadjusted average hourly potential for increase by NUTS2 region for 2018-2050</li> </ol> </li> <li>Data <ol> <li>d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv <ol> <li>Theoretical maximum reduction / load of the respective device</li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Maximum capacity</li> </ol> </li> </ol> </li> <li>Script <ol> <li>P_increaseV2.py</li> </ol> </li> </ol> </li> <li>Pcycle_dw.csv, Pcycle_td.csv, Pcycle_wm.csv <ol> <li>Description <ol> <li>power demand per cycle kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Punit_ac.csv, Punit_cp.csv <ol> <li>Description <ol> <li>Unit capacities kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Qhp_thermal_MWh_projectedV2.csv <ol> <li>Description <ol> <li>NUTS2 expectations for thermal energy demand met by heat pumps for 2022-2050</li> <li>Assumes a linear decomposition of non-renewable and non-energy efficient heating sources until 2050</li> </ol> </li> <li>Data <ol> <li>HP_transitionV2.csv</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>HP_projection_nuts.py</li> </ol> </li> </ol> </li> <li>rac.csv, rcp.csv, rdw.csv, rfr.csv, rrf.csv, rsh.csv, rtd.csv, rwh.csv, rwm.csv <ol> <li>Description <ol> <li>Household ownership rates</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>s_hdd nutsV3.csv, s_cdd nutsV3.csv, yr_hdd nutsV3.csv, yr_cdd nutsV3.csv <ol> <li>Description <ol> <li>s_hdd nutsV3.csv and s_cdd nutsV3.csv – months share of total heating and cooling degree days (yr_hdd and yr_cdd respectively)</li> <li>yr_hdd nutsV3.csv and yr_cdd nutsV3.csv – annual heating and cooling degree days respectively</li> <li>long run (2011-2021) average NUTS 2 level hdd and cdd</li> </ol> </li> </ol> </li> <li>s_wash nuts_V2.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for washing machine, tumble drier, and dishwasher</li> </ol> </li> <li>Data <ol> <li>stamminger_V2.xlsx</li> </ol> </li> <li>Script <ol> <li>S_wash_nuts_V2.py</li> </ol> </li> </ol> </li> <li>Stamminger_2009.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for water heater – WH, storage heater – SH, air conditioner AC, heat circulation pump – CP</li> <li>From Stamminger, R. (2009). Synergy potential of smart domestic appliances in renewable energy systems.</li> </ol> </li> </ol> </li> <li>Time_index.csv <ol> <li>Used to create the appropriate timestamp for representative hours</li> </ol> </li> <li>Wunit_rf_fr.csv <ol> <li>Annual energy consumption for refrigeration and freezers</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol>

opencc-by-4.0May 2023View details →

From genomics to integrative species delimitation? The case study of the Indo-Pacific Pocillopora corals

With the advent of genomics, sequencing thousands of loci from hundreds of individuals now appears feasible at reasonable costs, allowing complex phylogenies to be resolved. This is particularly relevant for cnidarians, for which insufficient data is available due to the small number of currently available markers and obscures species boundaries. Difficulties in inferring gene trees and morphological incongruences further blur the study and conservation of these organisms. Yet, can genomics alone be used to delimit species? Here, focusing on the coral genus Pocillopora, whose colonies play key roles in Indo-Pacific reef ecosystems but have challenged taxonomists for decades, we explored and discussed the usefulness of multiple criteria (genetics, morphology, biogeography and symbiosis ecology) to delimit species of this genus. Phylogenetic inferences, clustering approaches and species delimitation methods based on genome-wide single-nucleotide polymorphisms (SNP) were first used to resolve Pocillopora phylogeny and propose genomic species hypotheses from 356 colonies sampled across the Indo-Pacific (western Indian Ocean, tropical southwestern Pacific and south-east Polynesia). These species hypotheses were then compared to other lines of evidence based on genetic, morphology, biogeography and symbiont associations. Out of 21 species hypotheses delimited by genomics, 13 were strongly supported by all approaches, while six could represent either undescribed species or nominal species that have been synonymised incorrectly. Altogether, our results support (1) the obsolescence of macromorphology (i.e., overall colony and branches shape) but the relevance of micromorphology (i.e., corallite structures) to refine Pocillopora species boundaries, (2) the relevance of the mtORF (coupled with other markers in some cases) as a diagnostic marker of most species, (3) the requirement of molecular identification when species identity of colonies is absolutely necessary to interpret results, as morphology can blur species identification in the field, and (4) the need for a taxonomic revision of the genus Pocillopora. These results give new insights into the usefulness of multiple criteria for resolving Pocillopora, and more widely, scleractinian species boundaries, and will ultimately contribute to the taxonomic revision of this genus and the conservation of its species.   This deposit contains the data related to Oury N, Noël C, Mona S, Aurelle D, Magalon H (2023) From genomics to integrative species delimitation? The case study of the Indo-Pacific Pocillopora corals. Mol Phylogenet Evol 107803. doi:10.1016/j.ympev.2023.107803 See 0_README.txt for more content details.

Multiplexed histology of COVID-19 post-mortem lung samples - CHRONIC CASE 3 FOV1

Image-based data set of a post-mortem lung sample from a COVID-19 donor (CHRONIC CASE 3 FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - CHRONIC CASE 3 FOV2

Image-based data set of a post-mortem lung sample from a COVID-19 donor (CHRONIC CASE 3 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

opencc-by-4.0Jun 2023View details →

Project's repository for: Co-immersion in Audio Augmented Virtuality: the Case Study of a Static and Approximated Late Reverberation Algorithm

Repository of the VR scene and the audio data used for the experiment reported in the publication <a href="https://ieeexplore.ieee.org/document/10269056" target="_blank" rel="noopener">available in Open Access</a>: <blockquote> Davide Fantini, Giorgio Presti, Michele Geronazzo, Riccardo Bona, Alessandro Giuseppe Privitera and Federico Avanzini (2023) "Co-immersion in Audio Augmented Virtuality: the Case Study of a Static and Approximated Late Reverberation Algorithm" in IEEE Transactions on Visualization and Computer Graphics (ISMAR special issue) </blockquote> The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/README.md">README.md</a> includes some instructions to use the data in this repository.   AUDIO The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/audio.zip">audio.zip</a> includes the Reaper's projects and audio files used in the experiment to provide the auditory stimuli (simultaneous reverberated speeches) to the participants. Each subfolder corresponds to a different Virtual Acoustics Environment (VAE): <ul> <li><LivingRoom|MARCo|METU> <ul> <li><Living Room|MARCo|METU>.rpp: Reaper's project for the VAE</li> <li>Bin: folder including the speech data convolved with the late reverberation part of the reverb condition \(B\) for each source position in the VAE</li> <li>Freeverb: folder including the speech data convolved with the late reverberation part of the reverb condition \(F_\text{d}\) for each source position in the VAE</li> <li>HOA: <ul> <li>ER: folder including the speech data convolved with the early reflections part (HOA in A-format) of the reference reverb condition \(H\) for each source position in the VAE</li> <li>Ref: folder including the speech data convolved with the entire reference reverb condition \(H\) (HOA in A-format) for each source position in the VAE</li> </ul> </li> </ul> </li> </ul> The reverberated speech data in the <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/audio.zip">audio.zip</a> file are obtained using third-party datasets: <ul> <li>The anechoic speech data are retrieved from four speakers (F2, F5, M3, M6) of the <a href="https://doi.org/10.5281/zenodo.6257551">ACE challenge corpus</a></li> <li>The Room Impulse Responses (RIR) in High-Order Ambisonics (HOA) format used to reverberate the speeches are retrieved from: <ul> <li><a href="https://doi.org/10.5281/zenodo.5747753">Living Room</a></li> <li><a href="https://doi.org/10.5281/zenodo.3477602">Concert hall (MARCo)</a></li> <li><a href="https://doi.org/10.5281/zenodo.2635758">Classroom (METU)</a></li> </ul> </li> </ul>   VR SCENE The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/VRscene.zip">VRscene.zip</a> includes the Virtual Reality (VR) scene provided to the participants during the experiment via an Oculus Quest 2. This file includes two subfolders: <ul> <li>UDPServer: C# code for the UDP server used for sending the OSC messages for head tracking <ul> <li>external/SharpOSC.dll: external library (<a href="https://github.com/ValdemarOrn/SharpOSC">SharpOSC</a>) used to interact with the OSC protocol</li> </ul> </li> <li>VR_Headtracking: folder including the Unity project with the VR scene</li> </ul>

opencc-by-4.0Aug 2022View details →

Paired differential gene expression and splicing analyses results of 199 baseline vs. case comparisons across 100 datasets

This dataset contains results from paired differential expression and differential splicing analyses as well as gene-set over-representation analysis results for 199 baseline vs. case comparisons across 100 randomly curated datasets with accompanying metadata (<a href="https://doi.org/10.1186/s12915-023-01724-w" target="_blank" rel="noopener">article</a>). All results were computed using the R package <a href="https://github.com/shdam/pairedGSEA">pairedGSEA</a>, which utilized DESeq2 (Love et al., 2014), DEXSeq (Anders et al., 2012), and fgsea (Korotkevich et al., 2019). See limma results here: <a href="https://doi.org/10.5281/zenodo.8162214">https://doi.org/10.5281/zenodo.8162214</a> Each .RDS file contains a list with four objects: A 'metadata' object with the metadata of the respective raw data, a 'genes' object with gene-level differential splicing and expression results, a 'gene_set' object with over-representation results, and 'experiment' with the experiment title. The filenames follow this pattern: "[dataset ID]_[GEO accession number]_[Manually assigned comparison title].RDS". All datasets were obtained from a local copy of the ARCHS4 v11 database of  transcript counts (Lachmann et al., 2018).