Find research datasets worth reusing

opencc-by-4.0May 2020View details →

iNRACM: Incorporating 15N into the Regional Atmospheric Chemistry Mechanism (RACM) for assessing the role photochemistry plays in controlling the isotopic composition of NOx, NOy, and atmospheric nitrate

15N compounds and reactions were incorporated into Regional Atmospheric Chemistry Mechanism (RACM), based on recent experimental or calculated values of isotope fractionation factors (α), to  simulate δ15N values in NOy compounds.

opencc-by-4.0Jun 2022View details →

Regional assessment of the current status of Short Food Supply Chains

The assessment of the current status of the Short Food Supply Chains (SFSCs) in each of the 12 Beacon Regions (focal regions) of the agroBRIDGES project is one of the different key aspects that jointly conform to the overall project concept. It is important to study the existing knowledge and SFSCs in each region, to identify and later stimulate the wide adoption of sustainable SFSC-based business models already existing successfully in one or some of the regions. The traditional/current business models, that will be spotted here, like the new SFSCs business and marketing models to be developed later, be categorised, and analysed from an economic (with special attention on producer’s income), environmental and social point of view. The 12 Beacon Regions portrayed in this report are listed in the table below: <table> <thead> <tr> <th> Country </th> <th> Region </th> <th> Beacon Region Leader </th> </tr> </thead> <tbody> <tr> <td> Greece </td> <td> Central Macedonia </td> <td> Q-PLAN </td> </tr> <tr> <td> Finland </td> <td> All </td> <td> VTT </td> </tr> <tr> <td> Netherlands </td> <td> All </td> <td> WU </td> </tr> <tr> <td> Italy </td> <td> Lazio and Piemonte </td> <td> CREA </td> </tr> <tr> <td> Ireland </td> <td> All </td> <td> MTU </td> </tr> <tr> <td> Denmark </td> <td> All </td> <td> FBCD </td> </tr> <tr> <td> Spain </td> <td> Andalucia </td> <td> CTA </td> </tr> <tr> <td> Poland </td> <td> All </td> <td> UNMF </td> </tr> <tr> <td> Latvia </td> <td> All </td> <td> UNMF </td> </tr> <tr> <td> Lithuania </td> <td> All </td> <td> UNMF </td> </tr> <tr> <td> France </td> <td> Bretagne, Pays de la Loire, Auvergne – Rhône Alpes </td> <td> VGP </td> </tr> <tr> <td> Turkey </td> <td> All </td> <td> SUF </td> </tr> </tbody> </table> All Beacon Regions have contributed data and insights about their specific region collected through desk research. They have focused on 2 appointed sectors that is of importance for the region. The focal sectors have been settled in the application for this project. The knowledge generated in the desk research is qualified through interviews with members of the regional Multi Actor Platform (MAP). Some interviews are summarized in the document, while others are integrated into the description. For each Beacon Region the current status of SFSCs is described in these chapters: <ul> <li>Overall status</li> <li>Main regional drivers</li> <li>Environmental aspects</li> <li>Economic aspects</li> <li>Social aspects</li> </ul> Along this information you will find a description of 5-10 important business or marketing models for each region together with statistics on market sectors and size. The description of the models is based on Business Model Canvas. The dataset contains the 12 regional reports, outlining the core topics of the regional assessment described above. In some regional reports, the notes from interviews with local actors are available and provided in the respective Annex.

opencc-by-4.0Jul 2024View details →

Risk assessment (susceptibility) of thaw slumps and thermokarst lakes in the Yangtze River source region

Due to the influence of climate warming, the degradation of permafrost on the Qinghai-Tibet Plateau (QTP) has become evident. The formation of thermokarst hazards induced by the degradation of ice-rich permafrost has a significant impact on infrastructure construction and local ecology; therefore, it is necessary to assess its risk. In this study, a novel multiple thermokarst hazards risk assessment framework was proposed by combining stacking machine learning and potential environmental factors (vegetation factors, terrain factors, climate factors, and soil factors) to assess the risk of thermokarst hazards in the Yangtze River source region (YRSR). The results show the risk assessment (susceptibility) of thermokarst hazards in the YRSR from 2000 to 2016 at 500 m spatial resolution. This study divided the risk into 5 levels: very low (0.0-0.2), low (0.2-0.4), moderate (0.4-0.6), high (0.6-0.8), and very high (0.8-1.0)

opencc-zeroApr 2023View details →

Assessment of current and future invasive plants in protected dune habitats of the Atlantic coastal region for the LIFE DUNIAS project (LIFE20 NAT/BE/001442)

This .csv file contains the raw data from the risk screening supplementing the LIFE DUNIAS horizon scan for (invasive) alien species in protected habitats of Atlantic coastal dune ecosystems (<a href="https://doi.org/10.21436/inbor.86703335">Adriaens et al. 2022</a>). We gladly refer to the annexes and methods section in this report for more explanation about the fields and their contained values. The file contains the following fields: TaxonName: original taxonomic name of the considered alien species WorkName: taxonomic name of the considered alien species after lumping of subspecies, closely related species of a complex, functionally similar species of the same genus (see chapter 3.1) hab_xxxx (1110, 1130, 1140, 1210, 1230, 1310, 1320, 1330, 2110, 2120, 2130, 2140, 21A0, 2150, 2190, 2160, 2170, 2180): susceptibility of habitat for the alien species (4-digit code refering to the Annex I habitat under the Habitats Directive)  occ_XX (BE, FR, IE, NL, ES, UK, DK, DE, PT, ALL): occupancy of the alien species in different countries of the Atlantic European region (as the number of 10km2 squares per country). Country codes: BE = Belgium, FR = France, IE = Ireland, NL = Netherlands, ES = Spain, UK = United Kingdom, DK = Denmark, DE = Germany, PT = Portugal, ALL = total for all countries. scor_XXX_xxxx: score of the assessment per criterium (INT = introduction, EST = establishment, SPR = spread, IMP = ecological impact, ALL = overall score) and per habitat group (salt = salties, sand = sandies, shru = shrubbies) conf_XXX_xxxx: confidence on the scores of the assessment per criterium (INT = introduction, EST = establishment, SPR = spread, IMP = ecological impact, ALL = overall score) and per habitat group (salt = salties, sand = sandies, shru = shrubbies) scor_ALL_MAX: maximum ecological impact score of the alien taxon across all habitats

opencc-by-nc-sa-4.0Jun 2023View details →

Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of regions in Europe (1981-2100)

This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat. A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010. To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS). The database is organized in three datasets: Regional_era5land_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>tmax: daily maximum temperature threshold.</li> <li>tmin: daily minimum temperature threshold.</li> </ul> Historical_era5land_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>start: first date of the heatwave.</li> <li>tmax: maximum temperature reached during the heatwave.</li> <li>intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li>duration: duration of the heatwave.</li> </ul> Future_and_baseline_era5land_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are: <ul> <li>hazard_level: it can be a warning, an alert or an alarm.</li> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li>period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li>decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li>decade_frequency_best: only applicable to the future. It determines the best projection among the models.</li> <li>decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.</li> <li>year_days: average annual days.</li> <li>year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li>year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>

opencc-by-nc-sa-4.0Jun 2023View details →

Heatwaves characterization derived from observations and climate projections to assess thermal behavior of regions in Europe (1981-2100)

This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat. A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010. To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS). The database is organized in three datasets: Regional_eobs_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>tmax: daily maximum temperature threshold.</li> <li>tmin: daily minimum temperature threshold.</li> </ul> Historical_eobs_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>start: first date of the heatwave.</li> <li>tmax: maximum temperature reached during the heatwave.</li> <li>intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li>duration: duration of the heatwave.</li> </ul> Future_and_baseline_eobs_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are: <ul> <li>hazard_level: it can be a warning, an alert or an alarm.</li> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li>period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li>decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li>decade_frequency_best: only applicable to the future. It determines the best projection among the models.</li> <li>decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.</li> <li>year_days: average annual days.</li> <li>year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li>year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>

opencc-by-4.0Oct 2023View details →

Dataset associated with the manuscript "A comprehensive assessment of anthropogenic and natural sources and sinks of Australasia's carbon budget" by Villalobos et al. (2023), part of the the second phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2).

Dataset associated with the manuscript  "A comprehensive assessment of anthropogenic and natural sources and sinks of Australasia’s carbon budget" by Villalobos et al. (2023), part of the the second phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2).

opencc-by-4.0Apr 2020View details →

Hazardous geological processes occurrence assessment for Transcarpathian region,_Ukraine

Maps of hazardous geological processes specific occurrence by administrative districts for Transcarpathian region were produced by the Institute of Geological Sciences of the National Academy of Sciences of Ukraine based on the processing of materials from such institutions: State Service of Geology and Mineral Resources of Ukraine, Transcarpathian geological and hydrogeological center of the State Enterprise "Zakhidukrgeologiia" of the National Joint Stock Company "Nadra Ukrainy", Berehovo, State Geological Information Archive of Ukraine. In particular, maps of the distribution of hazardous geological processes with a scale of 1:100000 (by V. Barnychka, 1980) and a scale of 1: 200000 (by M. Gabor) for the period 1980-2010 were used, as well as data provided by V. Petryk ("Zakhidukrgeologiia", 1983-2001), and data from information yearbooks on the of hazardous exogenous geological processes activization for Ukraine territory according to monitoring of engineering and geological processes 2015-2018. The ranking principles for Transcarpathian region administrative districts due to the hazardous geological processes occurrence depended on type of process.

opencc-by-4.0Sep 2015View details →

Figure 2 in Improved local inventory and regional contextualization for anuran (Amphibia) diversity assessment at an endangered habitat in southeastern Brazil

Figure 2. Rarefaction curves based on Jackknife I species-richness estimator for records of adults, tadpoles and all life stages pooled for four canga lakes at the Quadrilátero Ferrífero region, southeastern Brazil.

opencc-by-4.0Mar 2024View details →

Regional Assessment of buildings' Material Intensities (RASMI): Version 20230905: first public release B - data only

Version 20230905: first public release of RASMI (Regional Assessment of buildings' Material Intensities). This Zenodo version contains two files: <ul> <li><code>MI_ranges_20230905.xlsx</code> is the dataset of the estimated MI ranges. This is probably the file you're looking for.</li> <li><code>MI_data_20230905.xlsx</code> is the raw pools of MI used to create the MI ranges. This is mostly for reproducability.</li> </ul> Please refer to the GitHub readme.md in <a href="https://github.com/TomerFishman/MaterialIntensityEstimator">https://github.com/TomerFishman/MaterialIntensityEstimator</a> for details and how to use. Please cite both the Data Descriptor and the specific data version used: Data Descriptor: Tomer Fishman, Alessio Mastrucci, Yoav Peled, Shoshanna Saxe, Bas van Ruijven. RASMI: Global Ranges of Building Material Intensities Differentiated by Region, Structure, and Function. Scientific Data 2024, 11 (1), 418. <a href="https://doi.org/10.1038/s41597-024-03190-7" rel="nofollow">https://doi.org/10.1038/s41597-024-03190-7</a>. Data version: preferably use the DOI of the Zeonodo release. Refer to the release number (on the right) This work was conducted with support by the IIASA-Israel program, and by the Israel Science Foundation project RUSTY (grant no. 2706/19). Funding was also provided by the Horizon Europe research and innovation programme under grant agreement no. 101056868 (CIRCOMOD) for TF and grant agreement No 101056810 (CircEUlar) for AM. Opinions are those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for this. BvR and AM have been supported by the Energy Demand changes Induced by Technological and Social innovations (EDITS) project, which is an initiative coordinated by the Research Institute of Innovative Technology for the Earth (RITE) and the International Institute for Applied Systems Analysis (IIASA), and funded by the Ministry of Economy, Trade, and Industry (METI), Japan. SS was supported by the Canada Research Chair in Sustainable Infrastructure, Grant Number: 232970.

Fig. 2–11 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection

Fig. 2–11. Sampling and observation locations in the Kaliningrad Region: 2 –Ceruchus chrysomelinus (red points), Sinodendron cylindricum (blue points), and Lucanus cervus (green point); 3 – Dorcus parallelepipedus (green points), Platycerus caprea (red points), and P. caraboides (yellow points); 4 – Trox sabulosus (green points), and Trox scaber (red points); 5 – Geotrupes spiniger (green points), Geotrupes stercorarius (blue points), and Trypocopris vernalis (red points); 6 – Aphodius brevis (yellow point), A. borealis (green point), A. coenosus (red point), and A. fasciatus (blue points); 7 – Aphodius conspurcatus (red points), A. melanostictus (green points), Aegialia sabuleti (blue point), and Copris lunaris (yellow point); 8 – Aphodius varians (blue points), A. porcus (yellow points); A. distinctus (red points), and A. subterraneus (green points); 9 – Rhyssemus puncticollis (blue points), Psammodius asper (green points), and Oxyomus sylvestris (red points); 10 – Onthophagus coenobita (blue point), O. taurus (red points), O. gibbulus (yellow point), and O. nuchicornis (green points); 11 – Maladera holosericea (green points), and Omaloplia nigromarginata (red points).

Fig. 46–51 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection

Fig. 46–51. The images of the living regional Scarabaeoidea specimens in nature: 46 – A. dubia, habitually coloured form (05 July 2011); 47 – Phyllopertha horticola (11 June 2018); 48 – Hoplia graminicola (11 June 2018); 49 – Protaetia marmorata (28 May 2010); 50 – P. metallica (07 July 2011); 51 – Cetonia aurata (20 June 2010).

Fig. 28–33 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection

Fig. 28–33. The images of the living regional Scarabaeoidea specimens in nature: 28 – Aphodius rufipes (11 September 2018); 29 – A. prodromus (01 Oktober 2018); 30 – A. porcus (10 September 2018); 31 – A. sordidus (17 September 2018); 32 – A. foetens (10 September 2018); 33 – A. conspurcatus (15 Oktober 2018).

Fig. 52–57 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection

Fig. 52–57. The images of the living regional Scarabaeoidea specimens in nature: 52 – Oxythyrea funesta (15 June 2010); 53 – Gnorimus nobilis (08 July 2011); 54 – Osmoderma barnabita, male (09 July 2018); 55 and 56 – Trichius fasciatus, colour variations (14 July 2010); 57 – Valgus hemipterus, female (11 May 2018).

Fig. 12–15 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection

Fig. 12–15. Sampling and observation locations in the Kaliningrad Region: 12 – Melolontha hippocastani (green point), and Polyphylla fullo (red points); 13 – Oxythyrea funesta (green points), and Hoplia parvula (red points); 14 – Protaetia marmorata (green points); 15 – Gnorimus nobilis (blue points), and Osmoderma barnabita (green points).

Fig. 40–45 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection

Fig. 40–45. The images of the living regional Scarabaeoidea specimens in nature: 40 – Melolontha melolontha (24 May 2010); 41 – Maladera holosericea (11 May 2016); 42 – Omaloplia nigromarginata (05 July 2010); 43 – Polyphylla fullo, male, light colour form (25 June 2018); 44 – P. fullo, female, dark colour form (07 July 2018); 45 – Anomala dubia, variation with metallic coloured elytra (02 July 2014).

Fig. 1 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection

Fig. 1. Schematic map of the administrative division of the Kaliningrad Region into districts: Bagr. – Bagrationovsky, Chern. – Cherniakhovsky, Gur. – Gur'evsky, Gus. – Gusevsky, Gvard. – Gvardeysky, Krasn. – Krasnoznamensky, Nem. – Nemansky, Nest. – Nesterovsky, Oz. – Ozersky, Pol. – Polessky, Pravd. – Pravdinsky, Slav. – Slavsky, Zel. – Zelenogradsky.

Fig. 34–39. 34 – A in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection

Fig. 34–39. 34 – A. haemorrhoidalis (11 September 2018); 35 – A. sticticus (10 September 2018); 36 – Rhyssemus puncticollis (29 May 2017); 37 – Onthophagus gibbulus, male, forma major (11 September 2018); 38 – O. nuchicornis, male (18 June 2018); 39 – Serica brunnea (04 July 2018).

Fig. 22–27 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection

Fig. 22–27. The images of the living regional Scarabaeoidea specimens in nature: 22 – Anoplotrupes stercorosus (15 May 2013); 23 – Trypocopris vernalis (19 July 2015); 24 – Aegialia arenaria (07 May 2018); 25 – Oxyomus sylvestris (10 April 2018); 26 – Aphodius fimetarius 16 Oktober 2018); 27 – A. distinctus (25 September 2018).

Fig. 16–21 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection

Fig. 16–21. The images of the living regional Scarabaeoidea specimens in nature: 16 – Platycerus caprea (30 April 2018); 17 – P. caraboides (16 May 2010); 18 – Sinodendron cylindricum, male (23 July 2012); 19 – Sinodendron cylindricum, female (31 May 2010); 20 – Dorcus parallelepipedus, male (16 July 2015); 21 – Geotrupes spiniger (11 September 2018).