cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3588037
title: Seventy-year chronology of Salinas in southern France: coastal surfaces managed for salt production

description: kml files constructed for "Seventy-year chronology of Salinas in southern France: coastal surfaces managed for salt production and conservation issues for abandoned sites" For the years 1975, 2000, 2006, 2012, and 2018, topographical maps and aerial photographs were obtained the IGN web portals (Géoportail; Géoportail, section “remonter le temps”). Literature data and knowledge of the localisation of pumping stations and functioning of exploited Salinas obtained from expert interviews were used to re-itemise the functional pre-concentration ponds not included in CLC-class 4.2.2, as belonging to the Salinas. Polygons were created manually for these items and constituted an added class 4.2.2.A to distinguish these added surfaces from those recognised by Corine land-cover as 4.2.1 or 5.2.1. Both classes were systematically merged into a single shapefile for each Salina and the polygons obtained were checked against topographic maps and aerial photographs to verify if these represented the real coverage of the Salina and corrected if needed. Hence , the ensembles of the exploited Salinas in these 6 years were named as “Salinas_Mediterranean_France_xxxx.kml”, with xxxx as the year).
description: These file were proposed as supplementary on-line material for the following publication: DE WIT, R., VINCENT, A., FOULC, L., KLESCZEWSKI, M., SCHER, O., LOSTE, C., THIBAULT, M., POULIN, B., ERNOUL, L., BOUTRON, O. (2019). Seventy-year chronology of Salinas in southern France: Coastal surfaces managed for salt production and conservation issues for abandoned sites. Journal for Nature Conservation 49: 95-107 https://doi.org/10.1016/j.jnc.2019.03.003 But did not appear on site (error that was never corrected by Elsevier)
DOI: 10.5281/zenodo.3588038
title: Seventy-year chronology of Salinas in southern France: coastal surfaces managed for salt production

description: kml files constructed for "Seventy-year chronology of Salinas in southern France: coastal surfaces managed for salt production and conservation issues for abandoned sites" For the years 1975, 2000, 2006, 2012, and 2018, topographical maps and aerial photographs were obtained the IGN web portals (Géoportail; Géoportail, section “remonter le temps”). Literature data and knowledge of the localisation of pumping stations and functioning of exploited Salinas obtained from expert interviews were used to re-itemise the functional pre-concentration ponds not included in CLC-class 4.2.2, as belonging to the Salinas. Polygons were created manually for these items and constituted an added class 4.2.2.A to distinguish these added surfaces from those recognised by Corine land-cover as 4.2.1 or 5.2.1. Both classes were systematically merged into a single shapefile for each Salina and the polygons obtained were checked against topographic maps and aerial photographs to verify if these represented the real coverage of the Salina and corrected if needed. Hence , the ensembles of the exploited Salinas in these 6 years were named as “Salinas_Mediterranean_France_xxxx.kml”, with xxxx as the year).
description: These file were proposed as supplementary on-line material for the following publication: DE WIT, R., VINCENT, A., FOULC, L., KLESCZEWSKI, M., SCHER, O., LOSTE, C., THIBAULT, M., POULIN, B., ERNOUL, L., BOUTRON, O. (2019). Seventy-year chronology of Salinas in southern France: Coastal surfaces managed for salt production and conservation issues for abandoned sites. Journal for Nature Conservation 49: 95-107 https://doi.org/10.1016/j.jnc.2019.03.003 But did not appear on site (error that was never corrected by Elsevier)
DOI: 10.5519/qd.2zxb940l
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1000 records)
DOI: 10.5281/zenodo.3588091
title: Phenological metrics for Protected Area "HighTatra", MODIS terra tile h19v04

description: Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
DOI: 10.5281/zenodo.3588090
title: Phenological metrics for Protected Area "HighTatra", MODIS terra tile h19v04

description: Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
DOI: 10.5286/isis.e.rb1920268
title: Development of neutrons as entangled quantum probes

description: We recently completed an experiment on LARMOR at ISIS that proved that each individual neutron prepared for a Spin Echo Small Angle Neutron Scattering (SESANS) measurement is in a quantum state with its trajectory and its spin entangled. Entanglement is a property unique to quantum system that is fundamental to attempts to build quantum computers or to create unassailable cryptographic communications. We expect entangled neutrons to allow us to probe the entanglement of electrons in materials, a phenomenon that is hard to measure in any other way that is thought to be important for the properties of materials such as high-temperature superconductors and for so-called topological systems being considered for advanced computer memory applications. Our goal in this experiment is to continue to develop this novel quantum probe for situations we expect to encounter in real materials.
DOI: 10.5286/isis.e.rb1920268-1
title: Development of neutrons as entangled quantum probes

description: We recently completed an experiment on LARMOR at ISIS that proved that each individual neutron prepared for a Spin Echo Small Angle Neutron Scattering (SESANS) measurement is in a quantum state with its trajectory and its spin entangled. Entanglement is a property unique to quantum system that is fundamental to attempts to build quantum computers or to create unassailable cryptographic communications. We expect entangled neutrons to allow us to probe the entanglement of electrons in materials, a phenomenon that is hard to measure in any other way that is thought to be important for the properties of materials such as high-temperature superconductors and for so-called topological systems being considered for advanced computer memory applications. Our goal in this experiment is to continue to develop this novel quantum probe for situations we expect to encounter in real materials.
DOI: 10.5281/zenodo.3588172
title: DESED_public_eval

description: Domestic environment sound event detection (DESED) dataset is described: In this page: https://project.inria.fr/desed/<br> This repo: https://github.com/turpaultn/DESED<br> These papers: https://hal.inria.fr/hal-02160855 and https://hal.inria.fr/hal-02355573v1. This dataset is the public evaluation (<strong>referred as "youtube" evaluation in papers</strong>) It is composed of real 10 seconds sound clips taken from Youtube with Creative Commons licenses.
DOI: 10.5281/zenodo.3588179
title: DESED_real

description: <strong>If you are in this page, you have to know that the content of this page is also in github associated (metadata + source code to download audio) in the "real_data" section.</strong> Link to the associated github repository: https://github.com/turpaultn/Desed Link to the papers: <em>https://hal.inria.fr/hal-02160855</em>, https://zenodo.org/record/3567925 Domestic Environment Sound Event Detection (DESED). <strong>Description</strong> This dataset is the real part of the DESED dataset. It is a subpart of Audioset. There is the material to: Download the subset of Audioset used in DCASE 2019 task 4. <strong>Files:</strong> <strong>DESED_real_metadata.tar.gz</strong> : Annotations of subset of Audioset. <strong>DESED_real_source.tar.gz</strong>: src files you can find on github: https://github.com/turpaultn/DESED . Source files to download dcase2019 files.
DOI: 10.5281/zenodo.3588184
title: Phenological metrics for Protected Area "GranParadiso", MODIS terra tile h18v04

description: Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
DOI: 10.5281/zenodo.3588185
title: Phenological metrics for Protected Area "GranParadiso", MODIS terra tile h18v04

description: Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
DOI: 10.5281/zenodo.3588230
title: DATASET: De novo assembly and functional annotation of the heart + hemolymph transcriptome in the Caribbean spiny lobster Panulirus argus

description: The spiny lobster <em>Panulirus argus</em> is an ecologically relevant species in shallow water coral reefs and target of the most lucrative fishery in the greater Caribbean region. This study reports, for the first time, the heart + hemolymph transcriptome of the Caribbean spiny lobster<em> Panulirus argus</em> assembled from short Illumina 150 bp PE raw reads. A total 80,152,094 raw reads were assembled using the Oyster River Protocol pipeline that aspires to become the standard protocol for <em>de novo</em> transcriptome assembly. The assembly resulted in a total of 254,773 transcripts. Functional gene annotation was conducted using the software package 'dammit' that also aspires to become the standard protocol for <em>de novo</em> transcriptome annotation. Lastly, gene enrichment analyses were conducted using the Gene Ontology (GO), KEGG pathway analyses (Kaas), and KOG (WebMGA) databases. This resource will be of utmost importance in future research aiming at exploring the effect of local and regional anthropogenic disturbances as well as global climate change on the molecular physiology of this overexploited species.
DOI: 10.5281/zenodo.3588231
title: DATASET: De novo assembly and functional annotation of the heart + hemolymph transcriptome in the Caribbean spiny lobster Panulirus argus

description: The spiny lobster <em>Panulirus argus</em> is an ecologically relevant species in shallow water coral reefs and target of the most lucrative fishery in the greater Caribbean region. This study reports, for the first time, the heart + hemolymph transcriptome of the Caribbean spiny lobster<em> Panulirus argus</em> assembled from short Illumina 150 bp PE raw reads. A total 80,152,094 raw reads were assembled using the Oyster River Protocol pipeline that aspires to become the standard protocol for <em>de novo</em> transcriptome assembly. The assembly resulted in a total of 254,773 transcripts. Functional gene annotation was conducted using the software package 'dammit' that also aspires to become the standard protocol for <em>de novo</em> transcriptome annotation. Lastly, gene enrichment analyses were conducted using the Gene Ontology (GO), KEGG pathway analyses (Kaas), and KOG (WebMGA) databases. This resource will be of utmost importance in future research aiming at exploring the effect of local and regional anthropogenic disturbances as well as global climate change on the molecular physiology of this overexploited species.
DOI: 10.15129/ce80be60-f509-41d3-83df-6375a84f5785
title: Data for: "Establishing a Biocatalytic Platform for Small Molecule C-Alkylation"

description: The data uploaded includes characterisation data for all compounds synthesised and analyses carried out. This data underpins the linked thesis of Iain McKean.
DOI: 10.5281/zenodo.3588228
title: Phenological metrics for Protected Area "MurgiaAltaPark", MODIS aqua tile h19v04

description: Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
DOI: 10.5281/zenodo.3588229
title: Phenological metrics for Protected Area "MurgiaAltaPark", MODIS aqua tile h19v04

description: Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
DOI: 10.3886/e116881v1
title: Supplementary data for: Consumption Insurance Against Wage Risk: Family Labor Supply and Optimal Progressive Income Taxation

description: Life-cycle profiles from the BPS data set.
DOI: 10.3886/e116881
title: Supplementary data for: Consumption Insurance Against Wage Risk: Family Labor Supply and Optimal Progressive Income Taxation

description: Life-cycle profiles from the BPS data set.
DOI: 10.17632/997fnf4zn4.1
title: Data for: Choosing a Good Toolkit: Bayes-rule Based Heuristics

description: Simulation programs and results
DOI: 10.5281/zenodo.3588242
title: Phenological metrics for Protected Area "PenedaGeres", MODIS terra tile h17v04

description: Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
DOI: 10.5281/zenodo.3588243
title: Phenological metrics for Protected Area "PenedaGeres", MODIS terra tile h17v04

description: Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;