Find research datasets worth reusing

opencc-by-4.0Oct 2019View details →

A Novel Framework to Harmonise Satellite Data Series for Climate Applications: Matchups, Calibration Parameters and Residuals

The datasets included with this archive supplement the journal article: Giering, R.; Quast, R.; Mittaz, J.P.D.; Hunt, S.E.; Harris, P.M.; Woolliams, E.R.; Merchant, C.J. A Novel Framework to Harmonise Satellite Data Series for Climate Applications. Remote Sens. 2019, 11, 1002. doi:<a href="https://doi.org/10.3390/rs11091002">10.3390/rs11091002</a>. The archive includes a README file with further explanations.

opencc-by-4.0Aug 2022View details →

Harmonised LUCAS database classified by crop sequence type

Assessing the benefits of crop diversification – a pillar of the agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of the European Union (EU). This work is based on the Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union (doi: <a href="http://doi.org/10.2905/f85907ae-d123-471f-a44a-8cca993485a2">10.2905/f85907ae-d123-471f-a44a-8cca993485a2)</a> to fill this gap, We completed this dataset with a crop sequence type information for each point under non-perennial agricultural land cover in 2012, 2015 and 2018. The dataset lucas_classified.csv includes 31 159 points. Variables "point_id", "nuts0", "nuts2", "th_lat", "th_long", "LC1_2012", "LC1_2015", "LC1_2018" are inherited from the Harmonised LUCAS databse. Variables "cereals", "corn", "rapeseed", "sunflower", "pulses", "rootCrops", "forageLeg", "grassland" correspond to the temporal frequencies of respectively cereals, corn, rapeseed, sunflower, pulses, root crops, forage legumes and grassland within the 2012, 2015 and 2018 crop sequence for each point. Variable "crop_sequence_type" is the crop sequence type assigned to each point, among eight options: cereals, corn and cereals, forage legumes and cereals, pulses and cereals, rapeseed and cereals, root crops and cereals, sunflower and cereals, temporary grasslands. This dataset could be used to map current dominant crop sequences in the European Union, as illustrated in the map attached, and to assess the benefits of future crop diversification.

opencc-by-4.0Jun 2023View details →

SeaFlux v2023: harmonised sea-air CO2 fluxes from surface pCO2 data products using a standardised approach

BE SURE TO DOWNLOAD 2023.02 See the additional notes for updates on the products.  Fluxes calculated using the standardized approach:  \(F\text{CO}_2=K_0 \cdot K_w \cdot (p\text{CO}_2^\text{sea} - p\text{CO}_2^\text{atm})\ \cdot (1 - [ice])\). We provide each of the components to this equation to reduce the potential for errors in fluxes due to methodological differences. The netCDF files contain the following data (note that only bold names have been updated in v2023):  <ul> <li>fgco2_all_winds_products: the sea-air CO2 flux for all spCO2 products (6) and kw from all wind products (5). </li> <li>fgco2_global: the globally integrated sea-air CO2 fluxes for all spCO2 products (6) and kw from all wind products (6)</li> <li>sol:  \(K_0\)  is calculated using the Weiss (1974) parameterization with EN4 salinity and OISST temperatures </li> <li>kw: \(k_w\)  is calculated for winds with each being scaled independently to a 14-C bomb flux estimate of 16.5 cm/hr using the quadratic formulation by Wanninkhof (1992). <ul> <li>CCMPv2</li> <li>ERA5</li> <li>JRA55</li> <li>NCEP1</li> <li>NCEP2</li> </ul> </li> <li>spco2_SOCOM_unfilled: \(p\text{CO}_2^\text{sea}\) downloaded from various sources contains the following products: <ul> <li>CMEMS_FFNN</li> <li>CSIR_ML6</li> <li>JENA_MLS</li> <li>JMA_MLR</li> <li>MPI_SOMFFN</li> <li>NIES_FNN</li> </ul> </li> <li>spco2_filler: scaled version of the Landschützer et al. (2020) climatology used to fill missing regions of spco2_SOCOM_unfilled</li> <li>fco2atm: \(p\text{CO}_2^\text{atm}\) is calculated from NOAA's marine boundary layer product with ERA5 mean sea level pressure corrected for pH2O. The virial coefficient is then applied to pCO2atm</li> <li>ice: \([ice]\)  is the ice fraction from the OISST product</li> <li>area_ocean: the surface area of the ocean including the fractional area of the coastal regions</li> <li>seafrac: the fraction of a pixel that is ocean</li> </ul> Units are listed in the metadata of each of the netCDF variables.

openother-openSep 2023View details →

Harmonised branded foods dataset for FNS Cloud case studies (2017-2020)

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).

opencc-by-4.0Jul 2020View details →

Preprocessed and Harmonised Transcriptomics Datasets for Psoriasis and Atopic Dermatitis

The datasets uploaded within this record contain transcriptomics data of psoriasis and atopic dermatitis patients retrieved from the NCBI Gene Expression Omnibus and EBI ArrayExpress repositories. Overall, we retrieved 39 transcriptomics datasets, produced through both DNA microarrays and RNA-Sequencing technologies, along with relative meta-data tables. After data collection, each dataset was quality checked and preprocessed in order to obtain a harmonised source of data, ready-to-use for the research community. Beside, a thorough quality check was carried out on the retrieved meta-data, and data dictionaries were created (both for DNA microarry and RNA-Seq datasets) in order to homogenise the phenotypic data, enabling the comparability across the datasets.

opencc-by-4.0Feb 2024View details →

GHOST: A globally harmonised dataset of surface atmospheric composition measurements

<div> <div>GHOST: Globally Harmonised Observations in Space and Time, represents one of the biggest collection of harmonised measurements of atmospheric composition at the surface. In total, ~10 billion measurements from 1970-2025, of ~600 different components, from ~40 reporting networks, are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties.</div> <div>The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of provided information from the major public reporting networks, saved in 21 data variables, and 163 metadata variables. Extensive effort in particular is put towards the standardisation of measurement process information, and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use), and temporal classifications per measurement (e.g. day / night). A range of standardised network quality assurance flags are associated with each individual measurement. GHOST own quality assurance is also performed and associated with measurements. Measurements prefiltered by some default GHOST quality assurance are also provided.  </div> <h3>Data Access </h3> <div>The data processed in version 1.5.1 was a result of research undertaken in two separate projects. The processing and creation of new aerosol optical property products was done within the FOCI project, and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative. The processed data is designed to be complementary to the data provided in version 1.5 of GHOST.  </div> <div> </div> <div>The data is separated out per network, per temporal resolution, per component, and is saved as netCDF4 files, per year and month. There is additionally one synthetic network entitled "GHOST", which aggregates data across all networks. The dataset is compressed as .zip files per network. Beneath each network, collections of files per temporal resolution, per component, are compressed as tar.xz files.</div> <div> </div> <div>Each network .zip file can be decompressed via the following syntax: unzip [network].zip</div> <div> </div> <div>Component tar.xz files can be decompressed via the following syntax: tar -xf [component].tar.xz</div> <h3>How to Use</h3> Inside the GHOST dataset are a plethora of variables, thus it can difficult to fully exploit the extent of the available information. For this reason a companion publication has been written, detailing every aspect of the GHOST dataset: https://doi.org/10.5194/essd-2023-397 <div>If you have any other doubts of queries regarding the dataset, please email: dene.bowdalo@bsc.es</div> <h3>How to Cite</h3> If you plan to use this work please kindly cite both this dataset and the describing publication: </div> <div> <div>Bowdalo, D.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Zenodo [data set], https://doi.org/10.5281/zenodo.10637449, 2024.</div> <div>Bowdalo, D., Basart, S., Guevara, M., Jorba, O., Pérez García-Pando, C., Jaimes Palomera, M., Rivera Hernandez, O., Puchalski, M., Gay, D., Klausen, J., Moreno, S., Netcheva, S., and Tarasova, O.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Earth Syst. Sci. Data, 16, 4417–4495, https://doi.org/10.5194/essd-16-4417-2024, 2024.</div> <h3>Acknowledgements</h3> <div>We gratefully acknowledge all data providers for the substantial work done in establishing and maintaining the measuring stations that provide the data contained in this dataset. We would also like to warmly thank all data providers who met with GHOST authors through this work, and for all support given, from helping resolve data rights issues, to giving suggestions for improvements.</div> <div> </div> <div>We acknowledge the computing resources of MareNostrum, and the technical support provided by the Barcelona Supercomputing Center (AECT-2020-1-0007, AECT-2021-1-0027, AECT-2022-1-0008, and AECT-2022-3-0013). We also acknowledge the Red Temática ACTRIS España (CGL2017-90884-REDT), and the H2020 project ACTRIS IMP (\#871115).</div> <div> </div> <div>The processing and creation of new aerosol optical property products was funded by EU HORIZON EUROPE under grant agreement no. 101056783 (FOCI project), and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative.  </div> <div> </div> <div>The research leading to the creation of this dataset has also received funding from the grant RTI2018-099894-BI00 funded by MCIN/AEI/ 10.13039/501100011033 (BROWNING), the EU H2020 Framework Programme under grant agreement No. GA 821205 (FORCES), the European Research Council under the Horizon 2020 research and innovation programme through the ERC Consolidator Grant grant agreement No. 773051 (FRAGMENT), the AXA Research Fund (AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center), and the Department of Research and Universities of the Government of Catalonia through the Atmospheric Composition Research Group (code 2021 SGR 01550).</div> </div>

opencc-by-sa-4.0Oct 2023View details →

C2D2: An Open-Source, Pan-European, Harmonised Crop Development Database for Use in Regulatory Pesticide Exposure Modelling and Risk Assessment.

There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonised, pan-European, CropLife Europe Crop Development Database, C2D2, that is fully aligned with this regulatory requirement utilising efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU) 1107/2009. Evaluation of C2D2 against an independent dataset showed good agreement for equivalent time periods, crop growth stages and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large dataset compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being under-represented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modelling framework (using the AppDate tool) are often indicated over some/many growth stages suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons licence to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection.

opencc-by-4.0May 2022View details →

European Aerosol Phenomenology - 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets

Organic aerosol (OA) is a key component of total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013–2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30–240 min) and long-term data coverage (9–36 months), providing essential information to improve/validate air quality, health impact, and climate models.

opencc-by-4.0Jul 2022View details →

A Harmonised Dataset for Modelling Select Underutilised Crops Across EU

Version 2: the data was checked and refined against issues that were found in the columns bulk density and treatments.  Datasets are a compilation of information that are collected from various sources including books, research articles, databases, website articles, experts and local communities growing underutilised crops, etc. The focus was on extracting data from literature sources that were mainly peer reviewed, credible, and are primarily published in English language.  Taxonomy data: crop, variety or landrace Publication information: author, journal, year Geographic data: continent, country, site, latitude, longitude Soil data: Lower Depth, Clay (%), Sand (%), Silt (%), Texture, S.O., Bulk Density, Total carbon (%) Experimental data: Experiment duration (years), Seeding rate, Sowing depth, dates of each treatment /intervention. Production: yield, yield date, Above Ground Biomass (Agb), Agb date Phenology data: Growing Degree Days (sowing-to-harvest), emergence date, flowering date, maturity date Metadata: Study number, DOI, link  Credible sources containing experimental data, either from agronomy trials or meta-analysis containing experimental data were selected through literature search. As the focus of the work was to collate as much information as possible about agronomy trials of select underutilised crops, all data were collected and inserted into a shareable template on Google Docs.  List of crops:   Ceratonia siliqua, Carob, Underutilised legume tree Cichorium endivia, Endive, Underutilised vegetable Eragrostis tef, Teff, Underutilised cereal Ficus carica, Fig, Underutilised fruit tree Helianthus tuberosus, Jerusalem artichoke, Underutilised starchy roots/tubers Lentil Culinaris, Lentil, Pulse Lupinus albus, White lupin, Underutilised legume Malus domestica, Apple, Fruit Malus pumila, Apple, Fruit Medicago sativa, Alfaalfa, Underutilised legume Panicum miliaceum , Proso millet, Underutilised minor millet Pisum sativum, Pea, Legume Prunus avium, Cherry, Fruit Prunus domestica, Plum, Fruit Pyrus communis, Pear, Fruit Rheum rhaponticum, Rhubarb, Underutilised vegetable Setaria italic, Foxtail millet, Underutilised minor millet Trifolium repens , Clover, Underutilised legume Vicia faba, Faba bean, Underutilised legume Vigna unguiculata, Cowpea, Underutilised legume

zenodo40/100

Notified bodies, horizontal specifications and harmonised standards for construction materials third party assessment according to the CPR - EU 305/2011

This dataset was extracted from NANDO, the website of the European Commission for notified bodies. It contains the list of notified bodies, horizontal specifications and harmonised standards for construction materials third party assessment according to the CPR - EU 305/2011

opencc-by-4.0Apr 2020View details →

zenodo40/100

Appendices to the Roadmap for action for the project More Welfare: towards new risk assessment methodologies and harmonised animal welfare data in the EU

Open the record for dataset details and reuse information.

opencc-by-4.0Dec 2023View details →

zenodo40/100

Training data for the "Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints"

There are two ZIP-files consisting of small histological image tiles that have been used to detect and quantify distinct tissue textures and lymphocyte proportions from H&E-stained clear cell renal cell carcinoma (KIRC) digital tissue sections of the Cancer Genome Atlas (TCGA) image archive and the Helsinki dataset. The tissue_classification file contains 300x300px tissue texture image tiles (n=52,713) representing renal cancer (“cancer”; n=13,057, 24.8%); normal renal (“normal”; n=8,652, 16.4%); stromal (“stroma”; n= 5,460, 10.4%) including smooth muscle, fibrous stroma and blood vessels; red blood cells (“blood”; n=996, 1.9%); empty background (“empty”; n=16,026, 30.4%); and other textures including necrotic, torn and adipose tissue (“other”; n=8,522, 16.2%). Image tiles have been randomly selected from the TCGA-KIRC WSI and the Helsinki datasets. The binary_lymphocytes file contains mostly 256x256px-sized but also smaller image tiles of Low (n=20,092, 80.1%) or High (n=5,003, 19.9%) lymphocyte density (n=25,095). Image tiles have been randomly selected from the TCGA-KIRC WSI dataset. All accuracy of all annotations have been double-checked. However, the classification between multiple tissue textures or lymphocyte density can be sometimes ambiguous. The deep learning model parameters trained with the ResNet-18 infrastructure for (1) lymphocyte and (2) texture classification are named as (1) resnet18_binary_lymphocytes.pth and (2) resnet18_tissue_classification.pth. Codes and instructions to use these are found in <a href="https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis">https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis</a>.   If you use either work, please cite the publication by Brummer O et al (1) AND the TCGA Research Network (2): (1) Brummer, O., Pölönen, P., Mustjoki, S. et al. Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints. Br J Cancer 129, 683–695 (2023). <a href="https://doi.org/10.1038/s41416-023-02329-4">https://doi.org/10.1038/s41416-023-02329-4</a> (2) The results shown here are in whole or part based upon data generated by the TCGA Research Network: <a href="https://www.cancer.gov/tcga">https://www.cancer.gov/tcga</a>.

opencc-by-4.0Aug 2022View details →

opencc-by-4.0Aug 2023View details →

Harmonising Contributions: Exploring Diversity in Software Engineering through CQA Mining on Stack Overflow

Community question-and-answering platforms dedicated to software engineering, such as Stack Overflow, have assumed indispensable roles in fostering a thriving global knowledge ecosystem. As these platforms suffer from diversity-related issues, investigating the underlying reasons behind such challenges becomes imperative to devise potential intervention strategies. The proposed study highlights Stack Overflow users’ contribution profiles, both in isolation and relative to various diversity metrics, including GDP and access to electricity. Finally, the study explores whether these contribution profiles extend to the city and state levels. This replication package complements our study, prompting future scholars to further examine our research process or conduct follow up analyses.

opencc-by-4.0Jun 2024View details →

Harmonising Contributions: Exploring Diversity in Software Engineering through CQA Mining on Stack Overflow – Replication Package

opencc-by-4.0Aug 2018View details →

Feasibility of PET/CT system performance harmonisation for quantitative multicenter ⁸⁹Zr studies

Data supporting the findings reported in EJNMMI Physics article Feasibility of PET/CT system performance harmonisation for quantitative multicenter ⁸⁹Zr studies

opencc-by-4.0Feb 2023View details →

The geological map of Italy 1:100,000 scale INSPIRE harmonised

The INSPIRE Directive institute a European infrastructure for spatial information to support the environmental policies of the European Union. In the mainframe of the Directive, 34 different themes that represents different environmental information has been identified. One of this is the Geology theme; it is split into three subthemes and represent a "reference data theme” because it provides basic knowledge on the physical properties and composition of rocks and sediments, their structure and their age as represented in geological maps, as well as geomorphological features. In the feature catalogue of the INSPIRE application schema Geology has been defined the term lists for the information types. Some of these are fully compliant with the features defined in the 1:100.000 scale geological map database and are used in the semantic harmonization procedure. The Geological Map of Italy at 1:100,000 scale is, at present, the most detailed complete geological map of Italy. It consists of a collage of 277 sheets, and it was produced over a period of more than 100 years, with some sheets in two editions. In the late 1990s, it was transformed into a vector database by digitising from raster format. The dataset was revised, integrated, and corrected between 2005-2009 by the Geological Survey of Italy. In 2021-22 the dataset was harmonised according to the INSPIRE and GeoSciML data models. The whole area has been subdivided into three different .gml format dataset on geographic base: Northern, Central and Southern Italy.