cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3402786
title: Inundation maps of Camargue for 47 dates within the period 2016/02/09 to 2018/06/19 and their accompanying INSPIRE metadata XML files

description: Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services. Inundation maps within the period 2016/02/09 to 2018/06/19 were generated for Camargue based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.". Each inundation map is named as " 'Date'_inundation_map_Camargue_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. 'Date' is in the form YYYY_MM_DD.
description: {"references": ["Kordelas, G.A.; Manakos, I.; Aragon\u00e9s, D.; D\u00edaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. Remote Sens. 2018, 10, 910."]}
DOI: 10.5066/f7jm28xz
title: Indian Wells Valley, California, sub-watersheds for the Basin Characterization Model

description: This data Set includes: 1) A shapefile of the Indian Wells Valley sub watersheds containing 9 sub-watersheds, 2) Geology shapefile of all the watersheds 3) Vegetation shapefile of all the watersheds 4) Basin Characterization Model (BCM) model output of recharge values for all the watersheds from 3 model runs (CanESM2, CCSM4, and HadGEM2-CC)
DOI: 10.5281/zenodo.3403004
title: Hydroperiod map of Camargue for 2016/2017 and its accompanying INSPIRE metadata XML file

description: The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands. The hydroperiod map of Camargue is named: "Camargue_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". The pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. The map is generated by interpolating satellite-derived inundation maps falling within the period indicated in its filename. The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.
DOI: 10.5281/zenodo.3403005
title: Hydroperiod map of Camargue for 2016/2017 and its accompanying INSPIRE metadata XML file

description: The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands. The hydroperiod map of Camargue is named: "Camargue_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". The pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. The map is generated by interpolating satellite-derived inundation maps falling within the period indicated in its filename. The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.
DOI: 10.3886/e101025
title: State Health Practices Database for Research (SHPDR)

description: The State Health Practice Database for Research (SHPDR) captures cross-sectional and longitudinal variation in states’ statutes and laws to enable researchers to more effectively perform clinically oriented health economics research, and investigate the diffusion of medical technology and other health services research outcomes of interest.
description: The State Health Practices Database for Research (SHPDR) captures cross-sectional and longitudinal information on states’ statutes and regulations.
DOI: 10.3886/e101025v1-13401
title: State Health Practices Database for Research (SHPDR)

description: The State Health Practice Database for Research (SHPDR) captures cross-sectional and longitudinal variation in states’ statutes and laws to enable researchers to more effectively perform clinically oriented health economics research, and investigate the diffusion of medical technology and other health services research outcomes of interest.
description: The State Health Practices Database for Research (SHPDR) captures cross-sectional and longitudinal information on states’ statutes and regulations.
DOI: 10.3886/e101025v1-13403
title: State Health Practices Database for Research (SHPDR)

description: The State Health Practice Database for Research (SHPDR) captures cross-sectional and longitudinal variation in states’ statutes and laws to enable researchers to more effectively perform clinically oriented health economics research, and investigate the diffusion of medical technology and other health services research outcomes of interest.
description: The State Health Practices Database for Research (SHPDR) captures cross-sectional and longitudinal information on states’ statutes and regulations.
DOI: 10.5066/p9r8gjgr
title: Influenza A virus detected from bivalves in waterfowl habitat of the Delmarva Peninsula

description: This data set includes information pertaining to 300 bivalve mollusk tissue samples collected from mollusk specimens from waters in eastern Maryland on the Delmarva Peninsula. Data includes source location, species, morphometric data, and IAV matrix gene detection status for the bivalve samples collected and analyzed in the associated study.
DOI: 10.5061/dryad.bt3938p/1
title: Dryad Item 10.5061/DRYAD.BT3938P/1

description: (:tba)
DOI: 10.5061/dryad.bt3938p/2
title: Dryad Item 10.5061/DRYAD.BT3938P/2

description: (:tba)
DOI: 10.5061/dryad.bt3938p/10
title: Dryad Item 10.5061/DRYAD.BT3938P/10

description: (:tba)
DOI: 10.5061/dryad.bt3938p/11
title: Dryad Item 10.5061/DRYAD.BT3938P/11

description: (:tba)
DOI: 10.5061/dryad.bt3938p/3
title: Dryad Item 10.5061/DRYAD.BT3938P/3

description: (:tba)
DOI: 10.5061/dryad.bt3938p/4
title: Dryad Item 10.5061/DRYAD.BT3938P/4

description: (:tba)
DOI: 10.5061/dryad.bt3938p/5
title: Dryad Item 10.5061/DRYAD.BT3938P/5

description: (:tba)
DOI: 10.5061/dryad.bt3938p/6
title: Dryad Item 10.5061/DRYAD.BT3938P/6

description: (:tba)
DOI: 10.5061/dryad.bt3938p/7
title: Dryad Item 10.5061/DRYAD.BT3938P/7

description: (:tba)
DOI: 10.5061/dryad.bt3938p/8
title: Dryad Item 10.5061/DRYAD.BT3938P/8

description: (:tba)
DOI: 10.5061/dryad.bt3938p/9
title: Dryad Item 10.5061/DRYAD.BT3938P/9

description: (:tba)
DOI: 10.5281/zenodo.3402895
title: Hydroperiod maps of Donana for 2015/2016, 2016/2017 and their accompanying INSPIRE metadata XML files

description: The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands. The hydroperiod maps for Donana are named: "Donana_Hydroperiod_from_1st_Sept_2015_to_31st_Aug_2016_using_Sentinel_2_and_Landsat_inundation_maps.tif" and "Donana_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". Their pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. They were generated by interpolating satellite-derived inundation maps falling within the period indicated in their filenames. The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.
DOI: 10.5281/zenodo.3402896
title: Hydroperiod maps of Donana for 2015/2016, 2016/2017 and their accompanying INSPIRE metadata XML files

description: The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands. The hydroperiod maps for Donana are named: "Donana_Hydroperiod_from_1st_Sept_2015_to_31st_Aug_2016_using_Sentinel_2_and_Landsat_inundation_maps.tif" and "Donana_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". Their pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. They were generated by interpolating satellite-derived inundation maps falling within the period indicated in their filenames. The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.
DOI: 10.5061/dryad.bt3938p/12
title: Dryad Item 10.5061/DRYAD.BT3938P/12

description: (:tba)
DOI: 10.5061/dryad.bt3938p/13
title: Dryad Item 10.5061/DRYAD.BT3938P/13

description: (:tba)
DOI: 10.5281/zenodo.3403009
title: Hydroperiod map of Danube Delta for 2016/2017 and its accompanying INSPIRE metadata XML file

description: The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical<br> ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat<br> for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands. The hydroperiod map of Danube Delta is named: "Danube_Delta_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". The pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. The map is generated by interpolating satellite-derived inundation maps falling within the period indicated in its filename. The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.
DOI: 10.5281/zenodo.3403010
title: Hydroperiod map of Danube Delta for 2016/2017 and its accompanying INSPIRE metadata XML file

description: The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical<br> ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat<br> for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands. The hydroperiod map of Danube Delta is named: "Danube_Delta_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". The pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. The map is generated by interpolating satellite-derived inundation maps falling within the period indicated in its filename. The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.
DOI: 10.5281/zenodo.3258940
title: Supporting data and code for "Nagaoka ferromagnetism observed in a quantum dot plaquette"

description: This supplement contains the files needed to reproduce the all the figures in the main article. It contains the raw data from measurements and Python scripts to reproduce the figures made from the experimental data, as well as scripts that implement the extended Hubbard models used to make the simulated spectra and charge stability diagrams shown in the article figures. Refer to the readme.txt file for detailed information on each of the files.
DOI: 10.5281/zenodo.3258941
title: Supporting data and code for "Nagaoka ferromagnetism observed in a quantum dot plaquette"

description: This supplement contains the files needed to reproduce the all the figures in the main article. It contains the raw data from measurements and Python scripts to reproduce the figures made from the experimental data, as well as scripts that implement the extended Hubbard models used to make the simulated spectra and charge stability diagrams shown in the article figures. Refer to the readme.txt file for detailed information on each of the files.
DOI: 10.5061/dryad.7034412/1
title: Dryad Item 10.5061/DRYAD.7034412/1

description: (:tba)
DOI: 10.5061/dryad.7034412/2
title: Dryad Item 10.5061/DRYAD.7034412/2

description: (:tba)
DOI: 10.5281/zenodo.3403704
title: Maps related to the detection of abrupt changes in NDVI approximated phenological cycles of Donana marshes for 2007-2016

description: Monitoring of abrupt changes among annual vegetation cycles of consequent years in Protected Areas is valuable for the recognition of patterns, which represent the reaction of the biomes to external factors, such as changes in the meteorological conditions (e.g. the precipitation regime), human intervention or extreme events (e.g. fire). It is an indicator of the primary production of the area and other relevant functions of the ecosystem. The BFAST, Breaks For Additive Seasonal and Trend, approach can be used for monitoring changes, since it is globally applicable and able to analyze each pixel individually without the need to set thresholds for detecting changes within time series. Thus, BFAST is applied for the detection of abrupt trend changes in NDVI time series in the case of Doñana marshes, as a proxy to phenological metrics per pixel. BFAST outputs are used to generate: (i) a raster with the time of all detected abrupt changes per pixel (filename: “All_break_times_2007_to_2016.tif”), (ii) a raster with the total number of detected abrupt changes per pixel (filename: “Marshes_maximum_number_of_breaks_2007_to_2016.tif”), (iv) a raster with the time for which the biggest change is detected per pixel has the (filename: “Marshes_maximum_break_time_2007_to_2016.tif”). The above files are accompanied by INSPIRE metadata XML files. Detailed information can be found in the “Readme.docx” included in the zip containing the dataset.
description: {"references": ["Verbesselt, J.; Zeileis A.; Hyndman, R.: Breaks For Additive Season and Trend (BFAST), R Package Version 1.5.7. Available online: https://cran.r-project.org/web/packages/bfast (accessed on 10 September 2019)."]}
DOI: 10.5281/zenodo.3403705
title: Maps related to the detection of abrupt changes in NDVI approximated phenological cycles of Donana marshes for 2007-2016

description: Monitoring of abrupt changes among annual vegetation cycles of consequent years in Protected Areas is valuable for the recognition of patterns, which represent the reaction of the biomes to external factors, such as changes in the meteorological conditions (e.g. the precipitation regime), human intervention or extreme events (e.g. fire). It is an indicator of the primary production of the area and other relevant functions of the ecosystem. The BFAST, Breaks For Additive Seasonal and Trend, approach can be used for monitoring changes, since it is globally applicable and able to analyze each pixel individually without the need to set thresholds for detecting changes within time series. Thus, BFAST is applied for the detection of abrupt trend changes in NDVI time series in the case of Doñana marshes, as a proxy to phenological metrics per pixel. BFAST outputs are used to generate: (i) a raster with the time of all detected abrupt changes per pixel (filename: “All_break_times_2007_to_2016.tif”), (ii) a raster with the total number of detected abrupt changes per pixel (filename: “Marshes_maximum_number_of_breaks_2007_to_2016.tif”), (iv) a raster with the time for which the biggest change is detected per pixel has the (filename: “Marshes_maximum_break_time_2007_to_2016.tif”). The above files are accompanied by INSPIRE metadata XML files. Detailed information can be found in the “Readme.docx” included in the zip containing the dataset.
description: {"references": ["Verbesselt, J.; Zeileis A.; Hyndman, R.: Breaks For Additive Season and Trend (BFAST), R Package Version 1.5.7. Available online: https://cran.r-project.org/web/packages/bfast (accessed on 10 September 2019)."]}
DOI: 10.5281/zenodo.3406863
title: Details of offspring and source data for analysis of metabolic health and dietary preference in a rat model of acute alcohol exposure.

description: This Excel file contains information on the number of offspring used to examine each outcome and the raw data for each data Table and Figure within a manuscript submitted to Journal of Physiology.
DOI: 10.7910/dvn/arybji/kp8oh9
title: 2017quest.pdf

description: Questionnaire for wave 8
DOI: 10.7910/dvn/arybji/memgg5
title: 2013quest.pdf

description: Questionnaire for wave 7
DOI: 10.7910/dvn/arybji/ukzrpa
title: 2001quest.pdf

description: Questionnaire for wave 4
DOI: 10.5281/zenodo.3403797
title: Phenology metric layers and their classification layers for the NDVI approximated phenological cycle of Donana from 01/12/2015 to 31/11/2016.

description: Analysis of changes in the phenological cycle of different plant species provide important information that may be used to assess the impact of seasonal and inter-annual climate variations on terrestrial vegetation. Phenex software has been used for estimating phenology related layers for Donana marshes relying on NDVI time series covering one year period from 01/12/2015 to 31/11/2016. “Phenology_metrics_layer_Dec2015_Nov2016.tif” includes the following layers: (i) green up day, (ii) senescence day, (iii) day of max NDVI value, and (iv) total number of NDVI peaks. These layers are also provided separately with the names: “Greenup_day_Dec2015_Nov2016.tif”, “Senescence_day_Dec2015_Nov2016.tif”, “Max_day_Dec2015_Nov2016.tif”, “Number_of_peaks_Dec2015_Nov2016.tif”. Classification layers based on these layers have been also generated. In particular, "ISODATA_classification_all_input_layers_Dec2015_Nov2016.tif" layer contains the classes generated when providing all phenology related layers as input to the ISODATA algorithm, while "ISODATA_classification_three_input_layers_Dec2015_Nov2016.tif" layer contains contains the classes generated when providing three penology related layers (i.e. greenup day, day of max NDVI value, senescence day layers) as input to the ISODATA algorithm. The above files are accompanied by INSPIRE metadata XML files. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
description: {"references": ["Lange, M.; Doktor, D. Phenex: Auxiliary Functions for Phenological Data Analysis, R Package Version 1.4-5. Available online: https://CRAN.R-project.org/package=phenex (accessed on 10 September 2019)."]}
DOI: 10.5281/zenodo.3403798
title: Phenology metric layers and their classification layers for the NDVI approximated phenological cycle of Donana from 01/12/2015 to 31/11/2016.

description: Analysis of changes in the phenological cycle of different plant species provide important information that may be used to assess the impact of seasonal and inter-annual climate variations on terrestrial vegetation. Phenex software has been used for estimating phenology related layers for Donana marshes relying on NDVI time series covering one year period from 01/12/2015 to 31/11/2016. “Phenology_metrics_layer_Dec2015_Nov2016.tif” includes the following layers: (i) green up day, (ii) senescence day, (iii) day of max NDVI value, and (iv) total number of NDVI peaks. These layers are also provided separately with the names: “Greenup_day_Dec2015_Nov2016.tif”, “Senescence_day_Dec2015_Nov2016.tif”, “Max_day_Dec2015_Nov2016.tif”, “Number_of_peaks_Dec2015_Nov2016.tif”. Classification layers based on these layers have been also generated. In particular, "ISODATA_classification_all_input_layers_Dec2015_Nov2016.tif" layer contains the classes generated when providing all phenology related layers as input to the ISODATA algorithm, while "ISODATA_classification_three_input_layers_Dec2015_Nov2016.tif" layer contains contains the classes generated when providing three penology related layers (i.e. greenup day, day of max NDVI value, senescence day layers) as input to the ISODATA algorithm. The above files are accompanied by INSPIRE metadata XML files. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
description: {"references": ["Lange, M.; Doktor, D. Phenex: Auxiliary Functions for Phenological Data Analysis, R Package Version 1.4-5. Available online: https://CRAN.R-project.org/package=phenex (accessed on 10 September 2019)."]}
DOI: 10.7910/dvn/arybji/8s0wo3
title: 1990quest.pdf

description: Questionnaire for wave 1
DOI: 10.7910/dvn/arybji/abt9ui
title: 1997quest.pdf

description: Questionnaire for wave 3
DOI: 10.7910/dvn/arybji/bg2rc6
title: BLS8_Codebook.txt

description: Codebook
DOI: 10.7910/dvn/arybji/iq5dul
title: 1993quest.pdf

description: Questionnaire for wave 2
DOI: 10.7910/dvn/arybji/lmxokz
title: BLS8_Data.tab

description: Consolidated data from waves 1-8
DOI: 10.7910/dvn/arybji/mkwmgy
title: 2009quest.pdf

description: Questionnaire for wave 6
DOI: 10.7910/dvn/arybji/pbfbs0
title: 2005quest.pdf

description: Questionnaire for wave 5
DOI: 10.7910/dvn/arybji/uiuxce
title: BLS8.RData

description: Consolidated dataset from waves 1-8 in original RData format.