cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28714355
title: Results of the determination of the PI curve for <i>Chlorella vulgaris</i> NIES 227 in exponential growth phase

description: This folder contains data sets recording dissolved oxygen concentration evolution accoring to time and light during oxymetric experiments of Chlorella vulgaris NIES 227 in exponential growth phase.The experiment is decribed in the publication "<i>Toward a scalable modelling of photosynthetic carbon assimilation and partition during nitrogen starvation for the microalga </i><i>Chlorella vulgaris</i><i> NIES 227</i>".The R code associated to the analysis of this dataset as described in the publication aforementioned is available at the URL <i>https://github.com/pchambonn/Toward-a-scalable-modelling-of-photosynthetic-carbon-assimilation-and-partition/tree/main</i>.
DOI: 10.6084/m9.figshare.28714259
title: Results from the experiments associated to the publication '<i>Scalable modelling of photosynthetic carbon assimilation and partition during </i><i>nitrogen starvation for the microalga Chlorella vulgaris NIES 227</i>'

description: This document present the raw data set associated to the publication "<i>Scalable modelling of photosynthetic carbon assimilation and partition during</i><i> nitrogen starvation for the microalga Chlorella vulgaris NIES 227</i>".This data set has been analyzed in the R script <i>PhoCAssPaNS_generalCode.Rmd</i> found at the following URL <i>https://github.com/pchambonn/Toward-a-scalable-modelling-of-photosynthetic-carbon-assimilation-and-partition/tree/main</i>.
DOI: 10.18112/openneuro.ds006032.v1.0.0
title: Monkey ECoG Dataset
DOI: 10.5281/zenodo.10793153
title: AUTHIST - Historical gridded monthly climate data for Austria

description: The long-term historical gridded dataset HistAlp provides homogenized instrumental data from 1780-2014 with a 5’ x 5’ spatial resolution and monthly timesteps, for the domain of the European Alps. (Auer et al., 2007)

Data available here: https://data.hub.geosphere.at/group/histalp 

The gridded observational dataset SPARTACUS is available for Austria and some adjacent regions in neighboring countries with a spatial resolution of 1×1 km and a daily temporal resolution from 1961-2020. (Hiebl and Frei, 2016).

Data available here: https://data.hub.geosphere.at/dataset/spartacus-v2-1d-1km

Both datasets were combined by regridding the lower-resolution HistAlp data to the SPARTACUS grid, and adjusting the mean bias of HistAlp to SPARTACUS based on monthly climatologies of the period 1961-2014 on each grid cell. The original variability and trends of HistAlp are retained with this method. The dataset provides bias-adjusted HistAlp data for the period 1780-1960 (temperature) and 1801-1960 (precipitation), and from 1961 to 2024 unmodified SPARTACUS data is used. 



Auer, I., Böhm, R., Jurković, A., Lipa, W., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Matulla, C., Briffa, K., Jones, P., Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre, O., Moisselin, J.-M., Begert, M., … Nieplova, E. (2007). HISTALP—historical instrumental climatological surface time series of the Greater Alpine Region. International Journal of Climatology, 27(1), 17–46. https://doi.org/10.1002/joc.1377

Hiebl, J., & Frei, C. (2016). Daily temperature grids for Austria since 1961—Concept, creation and applicability. Theoretical and Applied Climatology, 124(1–2), 161–178. https://doi.org/10.1007/s00704-015-1411-4

 

Hiebl, J., & Frei, C. (2018). Daily precipitation grids for Austria since 1961—Development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling. Theoretical and Applied Climatology, 132(1–2), 327–345. https://doi.org/10.1007/s00704-017-2093-x
DOI: 10.5281/zenodo.10793154
title: AUTHIST - Historical gridded monthly climate data for Austria

description: The long-term historical gridded dataset HistAlp provides homogenized instrumental data from 1780-2014 with a 5’ x 5’ spatial resolution and monthly timesteps, for the domain of the European Alps. (Auer et al., 2007)

Data available here: https://data.hub.geosphere.at/group/histalp 

The gridded observational dataset SPARTACUS is available for Austria and some adjacent regions in neighboring countries with a spatial resolution of 1×1 km and a daily temporal resolution from 1961-2020. (Hiebl and Frei, 2016).

Data available here: https://data.hub.geosphere.at/dataset/spartacus-v2-1d-1km

Both datasets were combined by regridding the lower-resolution HistAlp data to the SPARTACUS grid, and calculating monthly adjustment factors for the common period 1961-1990 on each grid cell. Those factors are applied to the HistAlp data for the period 1780-1960, and from 1961 to 2020 the unmodified SPARTACUS data is used. The method is described in detail in Scharnhorst et al. (2023).

 



Auer, I., Böhm, R., Jurković, A., Lipa, W., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Matulla, C., Briffa, K., Jones, P., Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre, O., Moisselin, J.-M., Begert, M., … Nieplova, E. (2007). HISTALP—historical instrumental climatological surface time series of the Greater Alpine Region. International Journal of Climatology, 27(1), 17–46. https://doi.org/10.1002/joc.1377

Hiebl, J., & Frei, C. (2016). Daily temperature grids for Austria since 1961—Concept, creation and applicability. Theoretical and Applied Climatology, 124(1–2), 161–178. https://doi.org/10.1007/s00704-015-1411-4

Scharnhorst, V. S., Thierolf, K., Neumayer, J., Becsi, B., Formayer, H., Lanner, J., Ockermüller, E., Mirwald, A., König, B., Kriechbaum, M., Meimberg, H., Meyer, P., Rupprecht, C., & Pachinger, B. (2023). Changes in Community Composition and Functional Traits of Bumblebees in an Alpine Ecosystem Relate to Climate Warming. Biology, 12(2), 316. https://doi.org/10.3390/biology12020316
DOI: 10.5281/zenodo.15129095
title: AUTHIST - Historical gridded monthly climate data for Austria

description: The long-term historical gridded dataset HistAlp provides homogenized instrumental data from 1780-2014 with a 5’ x 5’ spatial resolution and monthly timesteps, for the domain of the European Alps. (Auer et al., 2007)

Data available here: https://data.hub.geosphere.at/group/histalp 

The gridded observational dataset SPARTACUS is available for Austria and some adjacent regions in neighboring countries with a spatial resolution of 1×1 km and a daily temporal resolution from 1961-2020. (Hiebl and Frei, 2016).

Data available here: https://data.hub.geosphere.at/dataset/spartacus-v2-1d-1km

Both datasets were combined by regridding the lower-resolution HistAlp data to the SPARTACUS grid, and adjusting the mean bias of HistAlp to SPARTACUS based on monthly climatologies of the period 1961-2014 on each grid cell. The original variability and trends of HistAlp are retained with this method. The dataset provides bias-adjusted HistAlp data for the period 1780-1960 (temperature) and 1801-1960 (precipitation), and from 1961 to 2024 unmodified SPARTACUS data is used. 



Auer, I., Böhm, R., Jurković, A., Lipa, W., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Matulla, C., Briffa, K., Jones, P., Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre, O., Moisselin, J.-M., Begert, M., … Nieplova, E. (2007). HISTALP—historical instrumental climatological surface time series of the Greater Alpine Region. International Journal of Climatology, 27(1), 17–46. https://doi.org/10.1002/joc.1377

Hiebl, J., & Frei, C. (2016). Daily temperature grids for Austria since 1961—Concept, creation and applicability. Theoretical and Applied Climatology, 124(1–2), 161–178. https://doi.org/10.1007/s00704-015-1411-4

 

Hiebl, J., & Frei, C. (2018). Daily precipitation grids for Austria since 1961—Development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling. Theoretical and Applied Climatology, 132(1–2), 327–345. https://doi.org/10.1007/s00704-017-2093-x
DOI: 10.23668/psycharchives.16200
title: Dataset for CFA of the RPFC-2

description: The Resilience Protective Factors Checklist (RPFC) is a strengths-based oriented clinical questionnaire that assists in the identification of protective factors that have been empirically linked to resilience and positive outcomes (Powell et al., 2021). This study utilized a Confirmatory Factor Analysis to assist in the identification of additional protective factors linked to resilience, which informed the creation of the Resilience Protective Factors Checklist – Second Edition (RPFC-2). Participants were undergraduate college students (n = 652). The results revealed a total of 34 protective factors that represent three interrelated areas of protection—Individual, Family, and Community strengths and resources. The results are further broken down into eleven areas of protection that can be targeted in human services. An important goal of the present study was to identify and strengthen the factors that assist people in leading resilient, well-adjusted lives.
DOI: 10.5281/zenodo.10785529
title: A standardised climate change hazard vocabulary for heritage

description: This dataset (.xlsx) is a vocabulary of climate change hazards for heritage. Hazards are the potential occurrences of natural or physical events that may cause damage or loss. Previously there had been no definitive list of climate hazards for heritage that were directly connected to changing climatic processes. This project addresses this gap by linking the created hazards to the Climatic Impact-Drivers (CIDs) produced by the Intergovernmental Panel on Climate Change (IPCC). The vocabulary consists of 52 primary and key related hazards for heritage. It is international in its remit.

The list will be published as a vocabulary on the Forum on Information Standards in Heritage (FISH) where it can be accessed in multiple formats including linked data. This .xlsx format places the hazards in relationship to each other and in their CID context. Candidate terms can be submitted to the research group Heritage Environmental Risk and Data Analytics herada@ucl.ac.uk (terms submitted to Terminologies@HistoricEngland.org.uk will be directed to the research group for approval). An accompanying Historic England Research Report provides more information, including the methodology of the project (available in both English and Welsh).

The authors are interested in hearing from users of the vocabulary, specifically those that link the hazards to observed impacts of climate change on parts of the historic environment. This dataset was produced as part of a funded 6-month project between Historic England and the UCL Institute for Sustainable Heritage on developing a standardised vocabulary of climate change hazards for the historic environment. The Welsh version of the dataset was translated in 2025 by Lingo Soar, in collaboration with Fforest Fawr UNESCO Geopark, and as part of the UK National Commission for UNESCO's Climate Change and UNESCO Heritage project.
DOI: 10.5281/zenodo.15118646
title: A standardised climate change hazard vocabulary for heritage

description: This dataset (.xlsx) is a vocabulary of climate change hazards for heritage. Hazards are the potential occurrences of natural or physical events that may cause damage or loss. Previously there had been no definitive list of climate hazards for heritage that were directly connected to changing climatic processes. This project addresses this gap by linking the created hazards to the Climatic Impact-Drivers (CIDs) produced by the Intergovernmental Panel on Climate Change (IPCC). The vocabulary consists of 52 primary and key related hazards for heritage. It is international in its remit.

The list will be published as a vocabulary on the Forum on Information Standards in Heritage (FISH) where it can be accessed in multiple formats including linked data. This .xlsx format places the hazards in relationship to each other and in their CID context. Candidate terms can be submitted to the research group Heritage Environmental Risk and Data Analytics herada@ucl.ac.uk (terms submitted to Terminologies@HistoricEngland.org.uk will be directed to the research group for approval). An accompanying Historic England Research Report provides more information, including the methodology of the project (available in both English and Welsh).

The authors are interested in hearing from users of the vocabulary, specifically those that link the hazards to observed impacts of climate change on parts of the historic environment. This dataset was produced as part of a funded 6-month project between Historic England and the UCL Institute for Sustainable Heritage on developing a standardised vocabulary of climate change hazards for the historic environment. The Welsh version of the dataset was translated in 2025 by Lingo Soar, in collaboration with Fforest Fawr UNESCO Geopark, and as part of the UK National Commission for UNESCO's Climate Change and UNESCO Heritage project.
DOI: 10.6084/m9.figshare.28714391
title: Results associated to the specific oxymetric experiments performed for the publication '<i>Scalable modelling of photosynthetic carbon assimilation and partition during</i><i> </i><i>nitrogen starvation for the microalga Chlorella vulgaris NIES 227</i>'

description: This folder contains all the data associated to oxymetric experiments performed in the context of the publication '<i>Scalable modelling of photosynthetic carbon assimilation and partition during</i><i> </i><i>nitrogen starvation for the microalga Chlorella vulgaris NIES 227</i>' in order to determine the oxygen productivity of the microalgae Chlorella vulgaris NIES 227 at different stage of nitrogen starvation as described in the manuscript.The R code associated to the data to interpret it, and then analyze it are provided at the URL <i>https://github.com/pchambonn/Toward-a-scalable-modelling-of-photosynthetic-carbon-assimilation-and-partition/tree/main</i>.
DOI: 10.6084/m9.figshare.28714391.v1
title: Results associated to the specific oxymetric experiments performed for the publication '<i>Scalable modelling of photosynthetic carbon assimilation and partition during</i><i> </i><i>nitrogen starvation for the microalga Chlorella vulgaris NIES 227</i>'

description: This folder contains all the data associated to oxymetric experiments performed in the context of the publication '<i>Scalable modelling of photosynthetic carbon assimilation and partition during</i><i> </i><i>nitrogen starvation for the microalga Chlorella vulgaris NIES 227</i>' in order to determine the oxygen productivity of the microalgae Chlorella vulgaris NIES 227 at different stage of nitrogen starvation as described in the manuscript.The R code associated to the data to interpret it, and then analyze it are provided at the URL <i>https://github.com/pchambonn/Toward-a-scalable-modelling-of-photosynthetic-carbon-assimilation-and-partition/tree/main</i>.
DOI: 10.5281/zenodo.6862012
title: Dataset CA2

description: Dataset CA2

List of the archaeological sites in different file formats (geoJSON, kml, csv, shp).
DOI: 10.5281/zenodo.6862013
title: Dataset CA2

description: Dataset CA2

List of the archaeological sites in different file formats (geoJSON, kml, csv, shp).
DOI: 10.5522/04/28712444
title: Subtropical southern Africa top-down biomass burning NOx and NH3 emissions for the 2019 fire season derived with satellite observations and the GEOS-Chem model

description: Data generated for the paper entitled "Subtropical southern Africa fire emissions of nitrogen oxides and ammonia obtained with satellite observations and GEOS-Chem" submitted for peer-review to Royal Society's Environmental Science: Atmospheres journal.All the data are gridded to 0.25 degree latitude by 0.3125 degree longitude for the year 2019.The data include:(1) IASI NH3 monthly mean column densities for June-October from IASI instruments onboard MetOp-A and MetOp-B sensors reprocessed using GEOS-Chem a priori profiles. The IASI retrieval product used is version 4.0.0. Data are in files named with the format "iasi-metopa-metopb-gc-prior-[inventory]-nh3-cols.nc", where [inventory] is either "finnv25" for FINN version 2.5, "gfedv4s" for GFED version 4 with small fires, or "gfasv12" for GFAS version 1.2 for the three separate biomass burning inventories that were used to drive the model. (2) Top-down NOx and NH3 biomass burning emissions for 2019 obtained by applying a mass-balance approach to satellite observations and GEOS-Chem. NOx emissions are for June-October. NH3 emissions are for July-October. Only non-negative NH3 emissions are summed and use to report monthly and biomass burning season totals in the accompanying manuscript. Emissions data are in files named with the format "top-down-bb-[compound]-emis.nc", where [compound] is either NOx or NH3.(3) GEOS-Chem monthly mean tropospheric column densities of NO2 and total column densities of NH3 for June-October 2019. These are obtained by co-sampling with TROPOMI and applying the TROPOMI averaging kernel for NO2 and co-sampling with IASI for NH3. Model data are averaged over 13h00-14h00 local solar time (LST) for coincidence with IASI and 09h00-10h00 LST for coincidence with IASI. Model data are in files named with the format "geos-chem-no2-nh3-cols-[inventory].nc", where [inventory] is either "finnv25" for FINN version 2.5, "gfedv4s" for GFED version 4 with small fires, or "gfasv12" for GFAS version 1.2.
DOI: 10.5522/04/28712444.v1
title: Subtropical southern Africa top-down biomass burning NOx and NH3 emissions for the 2019 fire season derived with satellite observations and the GEOS-Chem model

description: Data generated for the paper entitled "Subtropical southern Africa fire emissions of nitrogen oxides and ammonia obtained with satellite observations and GEOS-Chem" submitted for peer-review to Royal Society's Environmental Science: Atmospheres journal.All the data are gridded to 0.25 degree latitude by 0.3125 degree longitude for the year 2019.The data include:(1) IASI NH3 monthly mean column densities for June-October from IASI instruments onboard MetOp-A and MetOp-B sensors reprocessed using GEOS-Chem a priori profiles. The IASI retrieval product used is version 4.0.0. Data are in files named with the format "iasi-metopa-metopb-gc-prior-[inventory]-nh3-cols.nc", where [inventory] is either "finnv25" for FINN version 2.5, "gfedv4s" for GFED version 4 with small fires, or "gfasv12" for GFAS version 1.2 for the three separate biomass burning inventories that were used to drive the model. (2) Top-down NOx and NH3 biomass burning emissions for 2019 obtained by applying a mass-balance approach to satellite observations and GEOS-Chem. NOx emissions are for June-October. NH3 emissions are for July-October. Only non-negative NH3 emissions are summed and use to report monthly and biomass burning season totals in the accompanying manuscript. Emissions data are in files named with the format "top-down-bb-[compound]-emis.nc", where [compound] is either NOx or NH3.(3) GEOS-Chem monthly mean tropospheric column densities of NO2 and total column densities of NH3 for June-October 2019. These are obtained by co-sampling with TROPOMI and applying the TROPOMI averaging kernel for NO2 and co-sampling with IASI for NH3. Model data are averaged over 13h00-14h00 local solar time (LST) for coincidence with IASI and 09h00-10h00 LST for coincidence with IASI. Model data are in files named with the format "geos-chem-no2-nh3-cols-[inventory].nc", where [inventory] is either "finnv25" for FINN version 2.5, "gfedv4s" for GFED version 4 with small fires, or "gfasv12" for GFAS version 1.2.
DOI: 10.5522/04/28582799
title: Assessing the future heating and cooling needs of the UK housing stock (dataset)

description: This dataset provides modelled estimates of indoor overheating, heating and cooling needs across the UK housing stock, aggregated by dwelling archetype and 2021 Lower-layer Super Output Areas (LSOA). Eight dwelling archetypes broadly representative of the UK housing stock were assessed: Detached; semi-detached; bungalow; end-terrace; mid-terrace; highrise, lowrise and converted flats. Future weather files using 2018 UK Climate Projections (UKCP18) were used to model the projected outdoor conditions in the years 2030, 2050 and 2085 under Relative Concentration Pathway (RCP) 2.6. Additionally, outdoor conditions in England in 2085 were modelled using a RCP 8.5 weather file to assess a scenario in which global emissions continue to rise with little abatement. The results show the median percent of hours the bedroom temperature exceeded 26oC during annual nighttime hours (defined as 10pm - 7am); median cooling and heating demand (expressed as a Cooling Energy Use Intensity and a Heating Energy Use Intensity, in kWh/yr/m2), with different levels of building-level adaptation at three time intervals: 2030, 2050 and 2085. The five adaptation scenarios modelled and the assumptions they include are detailed on the 'Metadata' worksheet.
DOI: 10.5522/04/28582799.v1
title: Assessing the future heating and cooling needs of the UK housing stock (dataset)

description: This dataset provides modelled estimates of indoor overheating, heating and cooling needs across the UK housing stock, aggregated by dwelling archetype and 2021 Lower-layer Super Output Areas (LSOA). Eight dwelling archetypes broadly representative of the UK housing stock were assessed: Detached; semi-detached; bungalow; end-terrace; mid-terrace; highrise, lowrise and converted flats. Future weather files using 2018 UK Climate Projections (UKCP18) were used to model the projected outdoor conditions in the years 2030, 2050 and 2085 under Relative Concentration Pathway (RCP) 2.6. Additionally, outdoor conditions in England in 2085 were modelled using a RCP 8.5 weather file to assess a scenario in which global emissions continue to rise with little abatement. The results show the median percent of hours the bedroom temperature exceeded 26oC during annual nighttime hours (defined as 10pm - 7am); median cooling and heating demand (expressed as a Cooling Energy Use Intensity and a Heating Energy Use Intensity, in kWh/yr/m2), with different levels of building-level adaptation at three time intervals: 2030, 2050 and 2085. The five adaptation scenarios modelled and the assumptions they include are detailed on the 'Metadata' worksheet.
DOI: 10.5281/zenodo.15129275
title: Exploring the interplay between artificial intelligence and open innovation: insights from innovation management - dataset
title: Scopus and VOS

description: The objective of this research was to explore the relationship between AI and OI through a systematic literature review. The data collection and analysis were based on the Scopus database, which provides access to the most extensive and reliable scientific resources. The structured and transparent filtering of relevant scientific publications was facilitated by the use of keyword combinations, including "open innovation," "artificial intelligence," "business model innovation," and "digital transformation." Publications were selected based on quality, language and time frame.
DOI: 10.5281/zenodo.15129276
title: Exploring the interplay between artificial intelligence and open innovation: insights from innovation management - dataset
title: Scopus and VOS

description: The objective of this research was to explore the relationship between AI and OI through a systematic literature review. The data collection and analysis were based on the Scopus database, which provides access to the most extensive and reliable scientific resources. The structured and transparent filtering of relevant scientific publications was facilitated by the use of keyword combinations, including "open innovation," "artificial intelligence," "business model innovation," and "digital transformation." Publications were selected based on quality, language and time frame.