cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7341520
title: Indicators used to calculate the Vulnerability of European wine PDOs to climate change

description: Dataset of the indicators used to define the Vulnerability of European wine PDO to climate change. The database includes the values of Exposure, Sensitivity and Adaptive Capacity for each PDO region. In the case of Exposure we also included the values for the indicators of Cool Night Iindex, Dryness Index and Huglin index. In the case of Adaptive Capacity we also included all the fifteen indicators used for the assessment of financial, natural, physical, social and human capacity. The classification in one of the Vulnerability classes is included in the related field.

Please refer to the following article when citing the dataset:

Tscholl, S., Candiago, S., Marsoner, T., Fraga, H., Giupponi, C., & Egarter Vigl, L.Climate resilience of European wine regions. Nat Commun 15, 6254 (2024). https://doi.org/10.1038/s41467-024-50549-w
DOI: 10.5281/zenodo.7341521
title: Calculation of the Vulnerability of European wine PDOs to climate change

description: Dataset and code used to calculate the vulnerability of European wine Protected Designation of Origin to climate change. It includes the indicators for exposure, sensitivity and adaptive capacity. In the case of adaptive capacity we also included all the fifteen indicators used for the assessment of financial, natural, physical, social and human capacity.
DOI: 10.5281/zenodo.8091027
title: Calculation of the Vulnerability of European wine PDOs to climate change

description: Dataset and code used to calculate the vulnerability of European wine Protected Designation of Origin to climate change. It includes the indicators for exposure, sensitivity and adaptive capacity. In the case of adaptive capacity we also included all the fifteen indicators used for the assessment of financial, natural, physical, social and human capacity.
DOI: 10.5281/zenodo.13143296
title: Monthly TM5-4DVar CO2 fluxes based on GOSAT and in situ measurements for the South American Temperate region from 2009 to 2018

description: The data set contains monthly CO2 land-atmosphere exchange fluxes (Net Biome Productivity, NBP) for the South American Temperate (SAT) region, as defined by TRANSCOM, from 2009 to 2018. The fluxes are calculated using the atmospheric inversion TM5-4DVar (Basu et al., 2013), as described in Metz et al. (2023), assimilating in situ and/or Greenhouse Gases Observing Satellite (GOSAT) measurements.

If the data is used for publications, please contact sanam.vardag@uni-heidelberg.de to discuss potential co-authorship and technical details.

The following data sets are included:

TM5-4DVar_ACOS_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/ACOSv9 XCO2 data and in situ CO2 concentration measurements together.

TM5-4DVar_RT_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/RemoTeCv2.4.0 XCO2 data and in situ CO2 concentration measurements together.

TM5-4DVar_GOSAT_MeanAcosRt_SAT: Mean of the monthly NBP fluxes of TM5-4DVar_ACOS_SAT and TM5-4DVar_RT_SAT.

TM5-4DVar_IS_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating only in situ CO2 concentration measurements.

TM5-4DVar_prior_SAT: Monthly NBP fluxes for the whole South American Temperate region used as prior in the atmospheric inversion TM5-4DVar.

TM5-4DVar_GOSAT_MeanAcosRt_arideast: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the eastern SAT region.

TM5-4DVar_GOSAT_MeanAcosRt_aridwest: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the western SAT region.

TM5-4DVar_GOSAT_MeanAcosRt_humid: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the humid regions in the SAT region.

All data sets have the following variables:

MonthDate: date (YYYY-MM-DD) of the middle of the individual month

Month: MM

Year: YYYY

NBP_flux_monthly_TgC_per_subregion: NBP flux as total monthly flux over the whole individual region (SAT, SAT humid, SAT arid east, west) in TgC/month.

NBP fluxes are calculated as Net Ecosystem Exchange fluxes + fire emissions. For more details about the atmospheric inversion and the used measurement data, please see Metz et al., 2023.

 

Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., et al. (2013). Global CO 2 fluxes estimated from GOSAT retrievals of total column CO 2. Atmospheric Chemistry and Physics, 13(17), 8695–8717, 2013. 

Metz, E.-M., Vardag, S.N.,  Basu, S., Jung, M., Ahrens, B., El-Madany, T., Sitch, S., Arora, V.  K., Briggs, P. R. , Friedlingstein, P., Goll, D.S., Jain, A.K.,  Kato, E., Lombardozzi, D., Nabel,J .E. M. S., Poulter, B., Séférian, R., Tian, H., Wiltshire, A., Yuan, W., Yue, X., Zaehle, S.,  Deutscher, N.M.,  Griffith, D.W.T., Butz, A. Soil respiration–driven CO2 pulses dominate Australia’s flux variability. Science, 379, 1332-1335, https://doi.org/10.1126/science.add7833, 2023.
DOI: 10.5281/zenodo.13143297
title: Monthly TM5-4DVar CO2 fluxes based on GOSAT and in situ measurements for the South American Temperate region from 2009 to 2018

description: The data set contains monthly CO2 land-atmosphere exchange fluxes (Net Biome Productivity, NBP) for the South American Temperate (SAT) region, as defined by TRANSCOM, from 2009 to 2018. The fluxes are calculated using the atmospheric inversion TM5-4DVar (Basu et al., 2013), as described in Metz et al. (2023), assimilating in situ and/or Greenhouse Gases Observing Satellite (GOSAT) measurements.

If the data is used for publications, please contact sanam.vardag@uni-heidelberg.de to discuss potential co-authorship and technical details.

The following data sets are included:

TM5-4DVar_ACOS_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/ACOSv9 XCO2 data and in situ CO2 concentration measurements together.

TM5-4DVar_RT_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/RemoTeCv2.4.0 XCO2 data and in situ CO2 concentration measurements together.

TM5-4DVar_GOSAT_MeanAcosRt_SAT: Mean of the monthly NBP fluxes of TM5-4DVar_ACOS_SAT and TM5-4DVar_RT_SAT.

TM5-4DVar_IS_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating only in situ CO2 concentration measurements.

TM5-4DVar_prior_SAT: Monthly NBP fluxes for the whole South American Temperate region used as prior in the atmospheric inversion TM5-4DVar.

TM5-4DVar_GOSAT_MeanAcosRt_arideast: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the eastern SAT region.

TM5-4DVar_GOSAT_MeanAcosRt_aridwest: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the western SAT region.

TM5-4DVar_GOSAT_MeanAcosRt_humid: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the humid regions in the SAT region.

All data sets have the following variables:

MonthDate: date (YYYY-MM-DD) of the middle of the individual month

Month: MM

Year: YYYY

NBP_flux_monthly_TgC_per_subregion: NBP flux as total monthly flux over the whole individual region (SAT, SAT humid, SAT arid east, west) in TgC/month.

NBP fluxes are calculated as Net Ecosystem Exchange fluxes + fire emissions. For more details about the atmospheric inversion and the used measurement data, please see Metz et al., 2023.

 

Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., et al. (2013). Global CO 2 fluxes estimated from GOSAT retrievals of total column CO 2. Atmospheric Chemistry and Physics, 13(17), 8695–8717, 2013. 

Metz, E.-M., Vardag, S.N.,  Basu, S., Jung, M., Ahrens, B., El-Madany, T., Sitch, S., Arora, V.  K., Briggs, P. R. , Friedlingstein, P., Goll, D.S., Jain, A.K.,  Kato, E., Lombardozzi, D., Nabel,J .E. M. S., Poulter, B., Séférian, R., Tian, H., Wiltshire, A., Yuan, W., Yue, X., Zaehle, S.,  Deutscher, N.M.,  Griffith, D.W.T., Butz, A. Soil respiration–driven CO2 pulses dominate Australia’s flux variability. Science, 379, 1332-1335, https://doi.org/10.1126/science.add7833, 2023.
DOI: 10.15468/dl.wfc7fp
title: Occurrence Download

description: A dataset containing 2841 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Cacomantis merulinus (Scopoli, 1786), Cacomantis merulinus threnodes Cabanis & Heine, 1863)",
 "Year 2019-2023"
 ]
}

 The dataset includes 2841 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043383-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p9gkoc1h
title: USGS National and Global Oil and Gas Assessment Project—Southwestern Wyoming Province, Lewis Shale Conventional and Continuous Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables

description: This data release contains the boundaries of assessment units and input data for the assessment of undiscovered conventional and continuous oil and gas resources in in the Lewis Shale of the Southwestern Wyoming Province, Wyoming and Colorado. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border a Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary. In addition to the shapefile, for U.S. assessments, allocation tables are provided that enumerate percentages assigned to various land categories. Machine-readable tables are also provided that contain the input and results for each assessment unit summarized in the USGS Fact Sheet. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.
DOI: 10.34740/kaggle/dsv/9074166
title: GOOGLE_CASE_A
DOI: 10.15468/dl.s47hg9
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Chamaegastrodia shikokiana Makino & F.Maek."
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043384-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ffvtu9
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Centropus bengalensis javanensis (Dumont, 1818), Centropus bengalensis sarasinorum Stresemann, 1912, Centropus bengalensis medius Bonaparte, 1850)"
 ]
}

 The dataset includes 39 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043385-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13143526
title: Comparative Engagement: Political vs. Socio-humanitarian

description: The war in Ukraine and the resulting flight of millions of people have once again put the reception of refugees at the centre of the public debate in Europe. Similar to 2015, volunteers are pitching in and organising support or politically campaigning for the rights of refugees. It is remarkable that many people are opening the doors of their houses and flats to those seeking protection. Private accommodation is currently making a significant contribution to ensuring the accommodation of arrivals from Ukraine. At the same time, there is a need for other forms of engagement for refugees, be it in accompanying them to the authorities, in language acquisition or in sponsorships.

At the moment, however, we know little about what exactly engagement looks like in times of war in Ukraine. We don't know who is getting involved, for what reasons and with what attitude patterns. Are they the same people who were already active in 2015/2016, or is the Ukraine war activating other population groups? Similarly, we know little about the concrete practice of housing, such as the spatial conditions, the support and counselling needs of people hosting refugees, and the challenges. Has their engagement in general and private hosting in particular changed how they perceive refugees? By answering these questions, we can capture the current extent, shape, opportunities and challenges of refugee engagement. The findings will also enable us to better support engaged people and the government agencies, initial reception centres and charities that work with them.

In order to close the research gap and address the acute lack of data, the project pursues an integrated research design that combines findings from different surveys with different sampling strategies, content-related foci and cooperation partners. This includes firstly a quantitative survey of private accommodation providers (including the extent, form and duration of accommodation, access to support services, practices and challenges of private accommodation (in short "private accommodation survey") and secondly a population survey of engaged people (in short "engagement survey"). Building on and inspired by previous surveys on refugee engagement (Volunteer Survey 2019, Allensbach Study on Refugee Engagement 2018), the survey allows for more differentiated insights into fields of activity, motivations, and experiences in engagement (including reasons for engagement termination). Finally, we supplement the quantitative surveys with qualitative background interviews to gain relevant contextual knowledge, an analysis of social media debates, and possibly, from 2023 onwards, discussion rounds on selected key questions with volunteers ("focus groups").

The results of the project will enable us to formulate recommendations for action for politics and society on how to sustainably strengthen the engagement for refugees and improve the accommodation situation.
DOI: 10.5281/zenodo.13143527
title: Comparative Engagement: Political vs. Socio-humanitarian

description: The war in Ukraine and the resulting flight of millions of people have once again put the reception of refugees at the centre of the public debate in Europe. Similar to 2015, volunteers are pitching in and organising support or politically campaigning for the rights of refugees. It is remarkable that many people are opening the doors of their houses and flats to those seeking protection. Private accommodation is currently making a significant contribution to ensuring the accommodation of arrivals from Ukraine. At the same time, there is a need for other forms of engagement for refugees, be it in accompanying them to the authorities, in language acquisition or in sponsorships.

At the moment, however, we know little about what exactly engagement looks like in times of war in Ukraine. We don't know who is getting involved, for what reasons and with what attitude patterns. Are they the same people who were already active in 2015/2016, or is the Ukraine war activating other population groups? Similarly, we know little about the concrete practice of housing, such as the spatial conditions, the support and counselling needs of people hosting refugees, and the challenges. Has their engagement in general and private hosting in particular changed how they perceive refugees? By answering these questions, we can capture the current extent, shape, opportunities and challenges of refugee engagement. The findings will also enable us to better support engaged people and the government agencies, initial reception centres and charities that work with them.

In order to close the research gap and address the acute lack of data, the project pursues an integrated research design that combines findings from different surveys with different sampling strategies, content-related foci and cooperation partners. This includes firstly a quantitative survey of private accommodation providers (including the extent, form and duration of accommodation, access to support services, practices and challenges of private accommodation (in short "private accommodation survey") and secondly a population survey of engaged people (in short "engagement survey"). Building on and inspired by previous surveys on refugee engagement (Volunteer Survey 2019, Allensbach Study on Refugee Engagement 2018), the survey allows for more differentiated insights into fields of activity, motivations, and experiences in engagement (including reasons for engagement termination). Finally, we supplement the quantitative surveys with qualitative background interviews to gain relevant contextual knowledge, an analysis of social media debates, and possibly, from 2023 onwards, discussion rounds on selected key questions with volunteers ("focus groups").

The results of the project will enable us to formulate recommendations for action for politics and society on how to sustainably strengthen the engagement for refugees and improve the accommodation situation.
DOI: 10.15468/dl.ngggez
title: Occurrence Download

description: A dataset containing 1477 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Argentina, Uruguay, Brazil)",
 "TaxonKey is one of (Caretta caretta (Linnaeus, 1758), Chelonia mydas (Linnaeus, 1758))"
 ]
}

 The dataset includes 1477 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043386-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hzg5c3
title: Occurrence Download

description: A dataset containing 550 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heliocypha fenestrata (Burmeister, 1839)"
 ]
}

 The dataset includes 550 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043387-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ur5zbc
title: Occurrence Download

description: A dataset containing 115 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Japan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ranatra chinensis Mayr, 1865"
 ]
}

 The dataset includes 115 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043388-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12934956
title: Experimental Data for 'Beyond memory-effect matrix-based imaging in scattering media by acousto-optic gating'

description: This repository contains the data used in Figures 3 and 4 of our study on noninvasive imaging beyond the optical memory-effect utilizing acousto-optic gating. The data files are organized into two folders: 'Figure 3' and 'Figure 4'. All data files can be loaded using PyTorch.

Folder Structure and Content:





Figure 3:



This folder contains the measurements that have been digitally propagated to the conjugated plane.

The folder also includes a ground truth file named 'object_direct_imaging.trc'.





Figure 4:



This folder contains two data files:



One file includes measurements with acoustic modulation.

The other file includes measurements without acoustic modulation.



The folder also contains a ground truth file named 'digits_direct_imaging.trc'.
DOI: 10.5281/zenodo.12934957
title: Experimental Data for 'Beyond memory-effect matrix-based imaging in scattering media by acousto-optic gating'

description: This repository contains the data used in Figures 3 and 4 of our study on noninvasive imaging beyond the optical memory-effect utilizing acousto-optic gating. The data files are organized into two folders: 'Figure 3' and 'Figure 4'. All data files can be loaded using PyTorch.

Folder Structure and Content:





Figure 3:



This folder contains the measurements that have been digitally propagated to the conjugated plane.

The folder also includes a ground truth file named 'object_direct_imaging.trc'.





Figure 4:



This folder contains two data files:



One file includes measurements with acoustic modulation.

The other file includes measurements without acoustic modulation.



The folder also contains a ground truth file named 'digits_direct_imaging.trc'.
DOI: 10.34740/kaggle/dsv/9074530
title: World Weather Repository ( Daily Updating )
DOI: 10.17036/researchdata.aston.ac.uk.00000617
title: Automated Polarization-Insensitive C & L Band Fiber Optical Parametric Amplifier for Bidirectional Long-Reach Access Networks
DOI: 10.15468/dl.xhp9s3
title: Occurrence Download

description: A dataset containing 94 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Egypt",
 "Geometry POLYGON((31.93391 25.09622,37.61161 25.09622,37.61161 29.88035,31.93391 29.88035,31.93391 25.09622))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Millepora platyphylla Hemprich & Ehrenberg, 1834",
 "Year 2000-2024"
 ]
}

 The dataset includes 94 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043389-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17036/researchdata.aston.ac.uk.00000644
title: Reading Measures from the Aston Literacy Project and Reading and Vocabulary Project datasets (forming the Literacy in Birmingham and Solihull, LIBS, Dataset)
DOI: 10.5281/zenodo.12912327
title: Dataset for "Optimistic growth of marginal region plantations under climate warming: assessing divergent drought resilience"

description: Dataset for published paper "Optimistic growth of marginal region plantations under climate warming: assessing divergent drought resilience".
DOI: 10.5281/zenodo.12912326
title: Dataset for "Optimistic growth of marginal region plantations under climate warming: assessing divergent drought resilience"

description: Dataset for published paper "Optimistic growth of marginal region plantations under climate warming: assessing divergent drought resilience", including the original tree-ring for four species and site features (topographic information, soil conditions, and LIDAR extracted data).
DOI: 10.5281/zenodo.13143523
title: Dataset for "Optimistic growth of marginal region plantations under climate warming: assessing divergent drought resilience"

description: Dataset for published paper "Optimistic growth of marginal region plantations under climate warming: assessing divergent drought resilience", including the original tree-ring for four species and site features (topographic information, soil conditions, and LIDAR extracted data).