cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25574/61090
title: Chandra X-ray Observatory ObsId 61090
DOI: 10.25574/61092
title: Chandra X-ray Observatory ObsId 61092
DOI: 10.25574/61093
title: Chandra X-ray Observatory ObsId 61093
DOI: 10.25574/61094
title: Chandra X-ray Observatory ObsId 61094
DOI: 10.25574/61096
title: Chandra X-ray Observatory ObsId 61096
DOI: 10.25574/61097
title: Chandra X-ray Observatory ObsId 61097
DOI: 10.25574/61102
title: Chandra X-ray Observatory ObsId 61102
DOI: 10.25574/61104
title: Chandra X-ray Observatory ObsId 61104
DOI: 10.25574/61106
title: Chandra X-ray Observatory ObsId 61106
DOI: 10.25574/61107
title: Chandra X-ray Observatory ObsId 61107
DOI: 10.25574/61108
title: Chandra X-ray Observatory ObsId 61108
DOI: 10.25574/61109
title: Chandra X-ray Observatory ObsId 61109
DOI: 10.25574/61110
title: Chandra X-ray Observatory ObsId 61110
DOI: 10.25574/61112
title: Chandra X-ray Observatory ObsId 61112
DOI: 10.25574/61114
title: Chandra X-ray Observatory ObsId 61114
DOI: 10.25574/61116
title: Chandra X-ray Observatory ObsId 61116
DOI: 10.25574/61126
title: Chandra X-ray Observatory ObsId 61126
DOI: 10.25574/61127
title: Chandra X-ray Observatory ObsId 61127
DOI: 10.25574/61128
title: Chandra X-ray Observatory ObsId 61128
DOI: 10.25574/61131
title: Chandra X-ray Observatory ObsId 61131
DOI: 10.25574/61132
title: Chandra X-ray Observatory ObsId 61132
DOI: 10.25574/61133
title: Chandra X-ray Observatory ObsId 61133
DOI: 10.25574/61136
title: Chandra X-ray Observatory ObsId 61136
DOI: 10.25574/61139
title: Chandra X-ray Observatory ObsId 61139
DOI: 10.25574/61141
title: Chandra X-ray Observatory ObsId 61141
DOI: 10.6084/m9.figshare.20765722
title: Table S1.pdf

description: Supplementary table S1. ToxoDB-based ID of the loci analysed in the present study, based on the annotations of frequently used <em>T. gondii </em>reference strains <br> Supplementary Table S2 BioProject PRJEB34235 European Nucleotide Archive (ENA) accession numbers.
DOI: 10.6084/m9.figshare.20765722.v1
title: Table S1.pdf

description: Supplementary table S1. ToxoDB-based ID of the loci analysed in the present study, based on the annotations of frequently used <em>T. gondii </em>reference strains <br> Supplementary Table S2 BioProject PRJEB34235 European Nucleotide Archive (ENA) accession numbers.
DOI: 10.25574/61143
title: Chandra X-ray Observatory ObsId 61143
DOI: 10.25574/61145
title: Chandra X-ray Observatory ObsId 61145
DOI: 10.25574/61148
title: Chandra X-ray Observatory ObsId 61148
DOI: 10.25574/61149
title: Chandra X-ray Observatory ObsId 61149
DOI: 10.25574/61151
title: Chandra X-ray Observatory ObsId 61151
DOI: 10.25574/61152
title: Chandra X-ray Observatory ObsId 61152
DOI: 10.25574/61155
title: Chandra X-ray Observatory ObsId 61155
DOI: 10.25574/61156
title: Chandra X-ray Observatory ObsId 61156
DOI: 10.25574/61157
title: Chandra X-ray Observatory ObsId 61157
DOI: 10.25574/61159
title: Chandra X-ray Observatory ObsId 61159
DOI: 10.25574/61162
title: Chandra X-ray Observatory ObsId 61162
DOI: 10.25574/61165
title: Chandra X-ray Observatory ObsId 61165
DOI: 10.25574/61166
title: Chandra X-ray Observatory ObsId 61166
DOI: 10.25574/61168
title: Chandra X-ray Observatory ObsId 61168
DOI: 10.25574/61169
title: Chandra X-ray Observatory ObsId 61169
DOI: 10.25574/61170
title: Chandra X-ray Observatory ObsId 61170
DOI: 10.25574/61172
title: Chandra X-ray Observatory ObsId 61172
DOI: 10.25574/61173
title: Chandra X-ray Observatory ObsId 61173
DOI: 10.25574/61174
title: Chandra X-ray Observatory ObsId 61174
DOI: 10.25574/61177
title: Chandra X-ray Observatory ObsId 61177
DOI: 10.25574/61178
title: Chandra X-ray Observatory ObsId 61178
DOI: 10.25574/61180
title: Chandra X-ray Observatory ObsId 61180
DOI: 10.25574/61181
title: Chandra X-ray Observatory ObsId 61181
DOI: 10.25574/61183
title: Chandra X-ray Observatory ObsId 61183
DOI: 10.25574/61184
title: Chandra X-ray Observatory ObsId 61184
DOI: 10.25574/61186
title: Chandra X-ray Observatory ObsId 61186
DOI: 10.25574/61187
title: Chandra X-ray Observatory ObsId 61187
DOI: 10.25574/61188
title: Chandra X-ray Observatory ObsId 61188
DOI: 10.25574/61189
title: Chandra X-ray Observatory ObsId 61189
DOI: 10.25574/61205
title: Chandra X-ray Observatory ObsId 61205
DOI: 10.25574/61206
title: Chandra X-ray Observatory ObsId 61206
DOI: 10.25574/61207
title: Chandra X-ray Observatory ObsId 61207
DOI: 10.25574/61208
title: Chandra X-ray Observatory ObsId 61208
DOI: 10.25574/61211
title: Chandra X-ray Observatory ObsId 61211
DOI: 10.25574/61213
title: Chandra X-ray Observatory ObsId 61213
DOI: 10.25574/61214
title: Chandra X-ray Observatory ObsId 61214
DOI: 10.25574/61216
title: Chandra X-ray Observatory ObsId 61216
DOI: 10.25574/61217
title: Chandra X-ray Observatory ObsId 61217
DOI: 10.25574/61220
title: Chandra X-ray Observatory ObsId 61220
DOI: 10.25574/61222
title: Chandra X-ray Observatory ObsId 61222
DOI: 10.25574/61224
title: Chandra X-ray Observatory ObsId 61224
DOI: 10.25574/61227
title: Chandra X-ray Observatory ObsId 61227
DOI: 10.25574/61229
title: Chandra X-ray Observatory ObsId 61229
DOI: 10.25574/61231
title: Chandra X-ray Observatory ObsId 61231
DOI: 10.25574/61232
title: Chandra X-ray Observatory ObsId 61232
DOI: 10.25574/61234
title: Chandra X-ray Observatory ObsId 61234
DOI: 10.25574/61235
title: Chandra X-ray Observatory ObsId 61235
DOI: 10.25574/61236
title: Chandra X-ray Observatory ObsId 61236
DOI: 10.25574/61239
title: Chandra X-ray Observatory ObsId 61239
DOI: 10.25574/61240
title: Chandra X-ray Observatory ObsId 61240
DOI: 10.25574/61242
title: Chandra X-ray Observatory ObsId 61242
DOI: 10.25574/61244
title: Chandra X-ray Observatory ObsId 61244
DOI: 10.25574/61246
title: Chandra X-ray Observatory ObsId 61246
DOI: 10.25574/61248
title: Chandra X-ray Observatory ObsId 61248
DOI: 10.25502/azha-8m10/d
title: Etude de préférences des consommateurs de Riz 

description: IITA with the commitment of other CGIAR centers (CIAT, ICRAF and IFPRI), provide support for creation of a conducive environment to intra-regional trade, management of agricultural resources, and promotion of a regional agricultural research for development program including Burundi.
DOI: 10.6084/m9.figshare.20794252
title: 20projects.rar

description: <strong>This file contains all data of 20 projects.</strong>
DOI: 10.6084/m9.figshare.20794252.v1
title: 20projects.rar

description: <strong>This file contains all data of 20 projects.</strong>
DOI: 10.6084/m9.figshare.19745380
title: Crous et al. (2022) Fungal Planet description sheets: 1383–1435 - Persoonia 48: 261–371

description: Alignments and phylogenetic trees processed through Mesquite and belonging to new species described in this Fungal Planet set contributed by the Westerdijk Fungal Biodiversity Institute, Utrecht, the Netherlands. See the figure legends associated with each file in the original publication for more details.
DOI: 10.6084/m9.figshare.19745380.v1
title: Crous et al. (2022) Fungal Planet description sheets: 1383–1435 - Persoonia 48: 261–371

description: Alignments and phylogenetic trees processed through Mesquite and belonging to new species described in this Fungal Planet set contributed by the Westerdijk Fungal Biodiversity Institute, Utrecht, the Netherlands. See the figure legends associated with each file in the original publication for more details.
DOI: 10.6084/m9.figshare.19333667
title: Murray Global Intertidal Change Classification v1.1 (1984-2016)

description: This dataset contains global maps of tidal flat ecosystems produced via a supervised classification of 707,528 Landsat Archive images. Each pixel was classified into tidal flat, permanent water or other with reference to a globally distributed set of training data. This product depicts tidal flat ecosystems around the global coastline. Pixels classified as tidal flat in the analysis represent several types of tidal flat ecosystems, including unconsolidated fine-grain sediments (tidal mudflats), unconsolidated coarse-grain sediments (tidal sand flats), and consolidated sediments, organic material or rocks (wide tidal rock-platforms), while excluding spectral signatures indicating the presence of vegetation dominated intertidal ecosystems such as mangroves and vegetated marshes. The analysis aimed to identify pixels that are subject to regular tidal inundation, and therefore may also include other intertidal systems where intertidal dynamics are observable.
DOI: 10.6084/m9.figshare.19334465
title: Murray Global Intertidal Change Classification v1.2 (1999-2019)

description: This dataset contains global maps of tidal flat ecosystems produced via a supervised classification of Landsat Archive images. Each pixel was classified into tidal flat, permanent water or other with reference to a globally distributed set of training data. This product depicts tidal flat ecosystems around the global coastline. Pixels classified as tidal flat in the analysis represent several types of tidal flat ecosystems, including unconsolidated fine-grain sediments (tidal mudflats), unconsolidated coarse-grain sediments (tidal sand flats), and consolidated sediments, organic material or rocks (wide tidal rock-platforms), while excluding spectral signatures indicating the presence of vegetation dominated intertidal ecosystems such as mangroves and vegetated marshes. The analysis aimed to identify pixels that are subject to regular tidal inundation, and therefore may also include other intertidal systems where intertidal dynamics are observable.
DOI: 10.6084/m9.figshare.19334465.v1
title: Murray Global Intertidal Change Classification v1.2 (1999-2019)

description: This dataset contains global maps of tidal flat ecosystems produced via a supervised classification of Landsat Archive images. Each pixel was classified into tidal flat, permanent water or other with reference to a globally distributed set of training data. This product depicts tidal flat ecosystems around the global coastline. Pixels classified as tidal flat in the analysis represent several types of tidal flat ecosystems, including unconsolidated fine-grain sediments (tidal mudflats), unconsolidated coarse-grain sediments (tidal sand flats), and consolidated sediments, organic material or rocks (wide tidal rock-platforms), while excluding spectral signatures indicating the presence of vegetation dominated intertidal ecosystems such as mangroves and vegetated marshes. The analysis aimed to identify pixels that are subject to regular tidal inundation, and therefore may also include other intertidal systems where intertidal dynamics are observable.
DOI: 10.25502/ccvm-xg93/d
title: Etude socioéconomique sur les Aflatoxines

description: IITA with the commitment of other CGIAR centers (CIAT, ICRAF and IFPRI), provide support for creation of a conducive environment to intra-regional trade, management of agricultural resources, and promotion of a regional agricultural research for development program including Burundi.
DOI: 10.6084/m9.figshare.20789191.v1
title: coco-c

description: This dataset is coco corruption. We use sixteen corruption method to corrupt coco dataset.
DOI: 10.5281/zenodo.6412403
title: Data from: Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity

description: <strong>Short description</strong> This repository contains the relevant data and code used for the analyses of the scientific publication: "<em>Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity</em>", published in the Journal of Applied Ecology. For further details please see the original article and its supplementary materials. <strong>Organization of the data</strong> The repository contains two main folders: 1. Target indicators & spatial analysis <em>‘target indicators.csv’</em>: Measured variables that have been quantified at the CROSSLINK field sampling campaign in the Zwalm catchment (EPT taxa richness, diatoms functional evenness, cotton-strip assay). <em>‘bio-suitability segments.csv’</em>: Biophysical suitability for food production of the arable land for each riparian segment of the Zwalm. <em>‘spatial analysis.xlsx’</em>: Results of the Zwalm spatial analyses addressing land-use and physiographic properties of the (1) local riparian corridors; (2) full riparian corridors within in the upstream catchments and (3) total upstream catchment areas for each sampling site. <em>‘Summary model development Zwalm.pptx’</em>: Additional information on the models that have been used in the CoMOLA optimization framework. 2. CoMOLA input & parameterisation The files in this folder can be used for the parameterisation of the Python tool CoMOLA (Strauch et al., 2019). Source for CoMOLA, including user manual: https://github.com/michstrauch/CoMOLA <em>‘config.ini’</em>: Basic configuration file of CoMOLA (needs to be adjusted to local settings) <em>‘input’ folder</em>: Includes the CoMOLA input files that have been used in our study. See CoMOLA manual for more details on each file. <em>‘models’ folder</em>: Includes the Python code of the models that are used for the calculation of all target indicators within the optimization framework (‘Zwalm_4_Models_v1_utf8.py’). The sub-folders ‘GIS_temp_files’ and ‘Input’ contain all files that are needed and have been used to run the Python code.
description: This research was conducted as part of the CROSSLINK project funded through the 2015–2016 BiodivERsA COFUND call for research proposals. National funders: the Swedish Research Council for Sustainable Development (FORMAS, project 2016-01945) and the Swedish Environmental Protection Agency; The Research Council of Norway (NFR, project 264499); The Research Foundation of Flanders (FWO, project G0H6516N), Belgium; the Romanian National Authority for Scientific Research and Innovation (CCCDI–UEFISCDI, project BiodivERsA3-2015-49-CROSSLINK, within PNCDI III); and the Federal Ministry of Education and Research (BMBF, project FKZ: 01LC1621A), Germany.
DOI: 10.5281/zenodo.6412402
title: Data from: Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity

description: <strong>Short description</strong> This repository contains the relevant data and code used for the analyses of the scientific publication: "<em>Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity</em>", published in the Journal of Applied Ecology. For further details please see the original article and its supplementary materials. <strong>Organization of the data</strong> The repository contains two main folders: 1. Target indicators & spatial analysis <em>‘target indicators.csv’</em>: Measured variables that have been quantified at the CROSSLINK field sampling campaign in the Zwalm catchment (EPT taxa richness, diatoms functional evenness, cotton-strip assay). <em>‘bio-suitability segments.csv’</em>: Biophysical suitability for food production of the arable land for each riparian segment of the Zwalm. <em>‘spatial analysis.xlsx’</em>: Results of the Zwalm spatial analyses addressing land-use and physiographic properties of the (1) local riparian corridors; (2) full riparian corridors within in the upstream catchments and (3) total upstream catchment areas for each sampling site. <em>‘Summary model development Zwalm.pptx’</em>: Additional information on the models that have been used in the CoMOLA optimization framework. 2. CoMOLA input & parameterisation The files in this folder can be used for the parameterisation of the Python tool CoMOLA (Strauch et al., 2019). Source for CoMOLA, including user manual: https://github.com/michstrauch/CoMOLA <em>‘config.ini’</em>: Basic configuration file of CoMOLA (needs to be adjusted to local settings) <em>‘input’ folder</em>: Includes the CoMOLA input files that have been used in our study. See CoMOLA manual for more details on each file. <em>‘models’ folder</em>: Includes the Python code of the models that are used for the calculation of all target indicators within the optimization framework (‘Zwalm_4_Models_v1_utf8.py’). The sub-folders ‘GIS_temp_files’ and ‘Input’ contain all files that are needed and have been used to run the Python code.
description: This research was conducted as part of the CROSSLINK project funded through the 2015–2016 BiodivERsA COFUND call for research proposals. National funders: the Swedish Research Council for Sustainable Development (FORMAS, project 2016-01945) and the Swedish Environmental Protection Agency; The Research Council of Norway (NFR, project 264499); The Research Foundation of Flanders (FWO, project G0H6516N), Belgium; the Romanian National Authority for Scientific Research and Innovation (CCCDI–UEFISCDI, project BiodivERsA3-2015-49-CROSSLINK, within PNCDI III); and the Federal Ministry of Education and Research (BMBF, project FKZ: 01LC1621A), Germany.
DOI: 10.6084/m9.figshare.20788189.v1
title: 20 projects' raw data

description: This file contains the 20 projects' raw data, including pull request, commit, issue and issue event.
DOI: 10.15468/aytrf6
title: A Checklist of Flora and Fauna of Ikot-Abasi, Akwa-Ibom State, Nigeria

description: This is a checklist of biodiversity of coastal areas of Ikot-Abasi, Akwa-Ibom state, Nigeria.
DOI: 10.6084/m9.figshare.20788189
title: OSS Linguistic Behavior Data

description: <strong>1. Corpus_labelled_20_projects.rar contains all data of 20 projects. All analyses were supported with the data in it.</strong> <strong>2. Raw Data.rar contains the 20 projects' raw data, including pull requests, commits, issue comments and issue events.</strong> <strong>3. adj+adv data.xlsx contains the data supporting comparisons with Google 1-gram dataset.</strong> Column 1,6: Adjective/Adverb word Column 2: The number of the word occurred in the 20 projects Column 3,8: The number of words in the 20 projects Column 4,9: The frequency of the word occurred in the 20 projects Column 5,10: The frequency of the word in Google 1-gram dataset
DOI: 10.6084/m9.figshare.20788189.v2
title: data

description: This file contains the 20 projects' raw data, including pull request, commit, issue and issue event. <strong>This file contains all data of 20 projects.</strong> <strong>adj+adv data.xlsx contains the results of comparisons with Google 1-gram dataset.</strong> Column 1,6: Adjective/Adverb word Column 2: The number of the word occurred in the 20 projects Column 3,8: The number of words in the 20 projects Column 4,9: The frequency of the word occurred in the 20 projects Column 5,10: The frequency of the word in Google 1-gram dataset
DOI: 10.6084/m9.figshare.20290155
title: LLSM Dataset for Virtual and Augmented reality

description: LLSM Dataset for Virtual and Augmented Reality. This dataset includes data generated by Lattice Light Sheet Microscopy. Image metadata is described in the file: FilesDescription_LLSM_Datasets_VR_AR.xlsx. <br> This dataset includes: Rab5(early endosomes), Plasma Membrane CellMask (endosomal pathway) and CD63-Turquoise.2 (late-multivesicular endosomes). <br> <strong>Lattice light-sheet microscopy. </strong>LLSM was done on a commercialized version of a previously described setup4 from 3i (Denver, USA). Cells were scanned incrementally through a 20 μm long light sheet in 500 nm steps using a fast piezoelectric flexure stage equivalent to ∼271 nm or 600 nm steps using a fast piezoelectric flexure stage equivalent to ∼325 nm, with respect to the detection objective and were imaged using a sCMOS camera (Orca-Flash 4.0; Hamamatsu, Bridgewater, NJ). Excitation was achieved with 488- (Coherent, Santa Clara, CA), 560- or 642-nm diode lasers (MPB Communications, Canada) through an excitation objective (Special Optics 28.6× 0.7 NA 3.74-mm water-dipping lens) and detected via a Nikon CFI Apo LWD 25× 1.1 NA water-dipping objective with a 2.5× tube lens with a final pixel size of 104 nm. Lattice light-sheet imaging was performed using an excitation pattern of outer NA equal to 0.55 and inner NA equal to 0.493. Acquired data were deskewed, a necessary step to realigned image frames, then deconvolved using LLSpy (https://github.com/tlambert03/LLSpy).
DOI: 10.6084/m9.figshare.20796274
title: 新建文件夹 (4).rar

description: comments
DOI: 10.6084/m9.figshare.20796274.v1
title: 新建文件夹 (4).rar

description: comments
DOI: 10.6084/m9.figshare.20501757
title: HD2_test_nc.nc

description: Netcdf testdata for gcm_toolkit tests
DOI: 10.6084/m9.figshare.20501757.v3
title: HD2_test_nc.nc

description: Netcdf testdata for gcm_toolkit tests
DOI: 10.6084/m9.figshare.20501757.v2
title: HD2_test_nc.nc

description: Netcdf testdata for gcm_toolkit tests
DOI: 10.5285/e5e5c4c7-af59-25df-e053-17d1a68b7f87
title: Temperature data from a public dive log application, acquired from recreational divers' dive computers in the northern Red Sea (2000-2017).

description: A collection of raw dive computer data (latitude, longitude, date, time, minimum temperature and maximum depth) logged by unknown recreational divers and uploaded to a publicly available online dive logging application (divelogs.de) was exported and filtered for further analysis. The data were spatially restricted to the northern Red Sea: 23-30&#176;N, 32-39.4&#176;E. Only dives within standard recreational depths (maximum dive depth less than or equal to 40 m), years with more than 75 dives per year and with a spread of dives across most months were retained (2000 to 2017). The data were processed in R version 4.2, using the tidyverse suite of packages. A 15 arc-second (approximately 0.5 km) resolution bathymetric grid of the area was downloaded from GEBCO, allowing bathymetric depths associated with each dive location to be found using the marmap get.depth function in R. Data were collected as part of Celia Marlowe's PhD project at the University of East Anglia, which aimed to assess the precision and accuracy of water temperature profiles collected from devices commonly carried by Scuba divers. The PhD project was part of the Next Generation Unmanned Systems Science (NEXUSS) Centre for Doctoral Training, funded by the Natural Environment Research Council (NERC) and the Engineering and Physical Science Research Council (EPSRC) (NE/N012070/1), and was additionally supported by Cefas Seedcorn (DP901D). This dataset is available under the Creative Commons CC BY-NC licence (non-commercial use: https://creativecommons.org/licenses/by-nc/4.0/).
DOI: 10.15124/61bc25e8-346f-4d8b-859c-b3c253ec58dd
title: 1 km x 1 km Gridded district wise emissions of VOCs from Paddy stubble burning In Punjab and Haryana in North India

description: Emissions of VOCs and atmospheric pollutants from Paddy stubble burning in Punjab and Haryana states of India during 2017-18. Data is spatially resolved at 1 km x 1 km resolution using the districtwise crop production statistics and stubble burning practices.
DOI: 10.6084/m9.figshare.20789191.v2
title: coco-c

description: This dataset is coco corruption. We use sixteen corruption method to corrupt coco dataset.
DOI: 10.6084/m9.figshare.20789191
title: COCO-C AND BDD-C

description: This dataset is coco corruption and bdd corruption. This is the supplementary material for the paper " <strong>Benchmarking Object Detection Robustness against Real-World Corruptions</strong> ". <br>
DOI: 10.6084/m9.figshare.20789191.v3
title: coco-c

description: This dataset is coco corruption. We use sixteen corruption method to corrupt coco dataset.
DOI: 10.5281/zenodo.6807897
title: Gene catalogue of the industrial sugarcane ethanol bioprocess microbiome

description: Catalogue of 296,257 unique microbial gene sequences (in fasta format) from metagenomic samples collected across all steps of the industrial process. For more details, please refer to the publication.
DOI: 10.5281/zenodo.6807896
title: Gene catalogue of the industrial sugarcane ethanol bioprocess microbiome

description: Catalogue of 296,257 unique microbial gene sequences (in fasta format) from metagenomic samples collected across all steps of the industrial process. For more details, please refer to the publication.