cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13550665
title: Additional file 1 of Split-root systems: detailed methodology, alternative applications, and implications at leaf proteome level

description: Additional file 1: Tables S1–S4. Spreadsheet showing the Log10 fold changes of the proteins related to different biological processes. Table S5. The list of primers used for RT-qPCR.
DOI: 10.5281/zenodo.4430109
title: Dataset for summarization of microblogs

description: This is the dataset used in the following paper:<br> Soumi Dutta, Vibhash Chandra, Kanav Mehra, Asit Kumar Das, Tanmoy Chakraborty, Saptarshi Ghosh. Ensemble Algorithms for Microblog Summarization. IEEE Intelligent Systems (Special Issue on Summarization of Things), vol. 33, no. 3, pp. 4--14, May/June 2018. The dataset contains four sets of tweets posted during four different disaster events, and summaries written by three human annotators for each tweet-set. The dataset can be used for evaluating algorithms for microblog summarization. The included README file giving a detailed description of the dataset.
DOI: 10.5281/zenodo.4430108
title: Dataset for summarization of microblogs

description: This is the dataset used in the following paper:<br> Soumi Dutta, Vibhash Chandra, Kanav Mehra, Asit Kumar Das, Tanmoy Chakraborty, Saptarshi Ghosh. Ensemble Algorithms for Microblog Summarization. IEEE Intelligent Systems (Special Issue on Summarization of Things), vol. 33, no. 3, pp. 4--14, May/June 2018. The dataset contains four sets of tweets posted during four different disaster events, and summaries written by three human annotators for each tweet-set. The dataset can be used for evaluating algorithms for microblog summarization. The included README file giving a detailed description of the dataset.
DOI: 10.5281/zenodo.4384215
title: UC4.5 Microclimate stations dataset

description: These datasets were collected during the period July 2019 - December 2020 in the context of IoF2020-UC4.5 Digital Ecosystem Utilisation (Cyslop). The datasets are stored in xls format. The datasets' file name follows the following structure: &lt;sensor device name&gt;_&lt;deployment location&gt;_&lt;deployment country&gt;_&lt;microclimate sequence id number&gt; Each dataset contains the following information: Timestamp (ISO 8601) Air Humidity (%) Air Temperature (°C) Battery Voltage (mV) Soil Electrical Conductivity (uS) Soil Humidity (%) Soil Salinity (mg/L) Soil TDS (mg/L) Soil Temperature (°C) Super cap Voltage (mV)
description: The datasets may contain time gaps due to seasonal farming practices or sensors unavailability
DOI: 10.5281/zenodo.4384214
title: UC4.5 Microclimate stations dataset

description: These datasets were collected during the period July 2019 - December 2020 in the context of IoF2020-UC4.5 Digital Ecosystem Utilisation (Cyslop). The datasets are stored in xls format. The datasets' file name follows the following structure: &lt;sensor device name&gt;_&lt;deployment location&gt;_&lt;deployment country&gt;_&lt;microclimate sequence id number&gt; Each dataset contains the following information: Timestamp (ISO 8601) Air Humidity (%) Air Temperature (°C) Battery Voltage (mV) Soil Electrical Conductivity (uS) Soil Humidity (%) Soil Salinity (mg/L) Soil TDS (mg/L) Soil Temperature (°C) Super cap Voltage (mV)
description: The datasets may contain time gaps due to seasonal farming practices or sensors unavailability
DOI: 10.5281/zenodo.4430038
title: Machine learning reactive mixing dataset-17

description: Reactive-mixing dataset-5 for machine learning analyses
description: For DIB article
DOI: 10.5281/zenodo.4430037
title: Machine learning reactive mixing dataset-17

description: Reactive-mixing dataset-5 for machine learning analyses
description: For DIB article
DOI: 10.6084/m9.figshare.12744950
title: Synthetic Overhead Images of Wind Turbines Made to Mimic California and Arizona

description: <b>Overview</b><br>This is a set of synthetic overhead imagery of wind turbines that was created with CityEngine, and was made to qualitatively match overhead images of wind turbines from California and Arizona. There are corresponding labels that provide the class, x and y coordinates, and height and width (YOLOv3 format) of the ground truth bounding boxes for each wind turbine in the images. These labels are named similarly to the images (e.g. wnd_xview_bkg_sd0_1.png will have the label titled wnd_xview_bkg_sd0_1.txt). The images are contained in syn_CA_AZ_xview_bkg_shdw_scatter_uniform_50_wnd_v1 and the labels are contained in syn_CA_AZ_xview_bkg_shdw_scatter_uniform_50_wnd_v1.<br><b>Use</b><br>This dataset is meant as supplementation to training an object detection model on overhead images of wind turbines. This data can be added to the training set to potentially improve the performance of the model, especially if the model is being tested on small wind turbines or in desert regions. <br><br><b>Why</b><br>This dataset was created to study the use of synthetic imagery when doing cross-domain testing (training on one geographic region and then testing on a much different geographic region). When training the YOLOv3 model on the Overhead Imagery of Wind Turbines dataset, we noticed qualitatively that the model struggled much more on the images from California and Arizona. This is because there are small wind turbines present in these states. Because of the size of these small turbines, there is less information for the model to identify them, and oftentimes the only human-noticeable information is the shadows of the turbines. This dataset was then designed to try to improve the model's performance on these regions and type of turbines. In our experiment, the baseline training (real overhead images in the Overhead Imagery of Wind Turbines dataset) set contained all of the images not from California and Arizona. The testing set contained all of the images from California and Arizona. This model was trained and evaluated, and then this synthetic imagery was added to the training set and the performance was once again evaluated on the testing set of just data from California and Arizona.<br><br><b>Method</b><br>The process for creating the dataset involved selecting background images from https://figshare.com/articles/dataset/Power_Plant_Satellite_Imagery_Dataset/5307364 that were not contained in the Overhead Imagery of Wind Turbines dataset, and did not have much infrastructure that would make the scene seem unrealistic. Then, a script was used to select these at random and uniformly generate 3D models of both small and large wind turbines over the image and then position the virtual camera to save four 608x608 pixel images. This process was repeated with the same random seed, but with no background image and the wind turbines colored as black. Next, these black and white images were converted into ground truth labels by grouping the black pixels in the images.
DOI: 10.6084/m9.figshare.12744902
title: Synthetic Overhead Images and Ground Truth Labels of Large Wind Turbines

description: <b>Overview</b><br>This is a set of synthetic overhead imagery of wind turbines that was created with CityEngine. There are corresponding labels that provide the class, x and y coordinates, and height and width (YOLOv3 format) of the ground truth bounding boxes for each wind turbine in the images. These labels are named similarly to the images (e.g. wnd_xview_bkg_sd0_1.png will have the label titled wnd_xview_bkg_sd0_1.txt). The images are contained in syn_uspp_bkg_shdw_scatter_uniform_60_wnd_v1 and the labels are contained in syn_uspp_bkg_shdw_scatter_uniform_60_wnd_v1_txt_xcycwh.<br><b>Use</b><br>This dataset is meant as supplementation to training an object detection model on overhead images of wind turbines. It can be added to the training set of an object detection model to potentially improve performance when using the model on real overhead images of wind turbines.<br><br><b>Why</b><br>This dataset was created to examine the utility of adding synthetic imagery to the training set of an object detection model to improve performance on rare objects. Since wind turbines are both very rare in number and sparse, this makes acquiring data very costly. This synthetic imagery is meant to solve this issue by automating the generation of new training data. The use of synthetic imagery can also be applied to the issue of cross-domain testing, where the model lacks training data on a particular region and consequently struggles when used on that region.<br><br><b>Method</b><br>The process for creating the dataset involved selecting background images from https://figshare.com/articles/dataset/Power_Plant_Satellite_Imagery_Dataset/5307364 that were not contained in the Overhead Imagery of Wind Turbines dataset, and did not have much infrastructure that would make the scene seem unrealistic. Then, a script was used to select these at random and uniformly generate 3D models of large wind turbines over the image and then position the virtual camera to save four 608x608 pixel images. This process was repeated with the same random seed, but with no background image and the wind turbines colored as black. Next, these black and white images were converted into ground truth labels by grouping the black pixels in the images.
DOI: 10.6084/m9.figshare.12744977
title: Overhead Imagery of Wind Turbines

description: <b>Overview</b><br>This is a set of overhead images of wind turbines with corresponding YOLOv3 formatted labels for object detection. These labels contain the class, x and y coordinates and the height and width of the bounding boxes for each wind turbine in the corresponding image. These images and the labels are contained in original_images_and_labels.<br><b>Why</b><br>Deep learning can help with the analysis of energy infrastructure. Extending this work to more types of energy infrastructure can create a pipeline for in-depth energy infrastructure analysis that could provide information for energy access decision makers to chose how to provide electricity to a non-electrified region (through grid extension, micro-grids or localized power generation). <br><br><b>Method</b>The images in original_images_and_labels were taken from https://figshare.com/articles/Power_Plant_Satellite_Imagery_Dataset/5307364. These images were then hand labeled and converted into formatted labels, which are also contained in original_images_and_labels. This data was then preprocessed into smaller images with dimensions of 608x608 and their corresponding labels with the same YOLOv3 format of class, x, y, height, width. These smaller images are contained in wind_turbine_images and their labels are contained in wind_turbine_labels.<br>
DOI: 10.5517/ccdc.csd.cc24lwsf
title: CCDC 1984365: Experimental Crystal Structure Determination

description: Related Article: Yuichi Kitagawa, Marina Kumagai, Pedro Paulo Ferreira da Rosa, Koji Fushimi, Yasuchika Hasegawa|2020|Chem.-Eur.J.|27|264|doi:10.1002/chem.202002628
DOI: 10.5517/ccdc.csd.cc2598xp
title: CCDC 2004922: Experimental Crystal Structure Determination

description: Related Article: Yuichi Kitagawa, Marina Kumagai, Pedro Paulo Ferreira da Rosa, Koji Fushimi, Yasuchika Hasegawa|2020|Chem.-Eur.J.|27|264|doi:10.1002/chem.202002628
DOI: 10.5517/ccdc.csd.cc2598zr
title: CCDC 2004924: Experimental Crystal Structure Determination

description: Related Article: Yuichi Kitagawa, Marina Kumagai, Pedro Paulo Ferreira da Rosa, Koji Fushimi, Yasuchika Hasegawa|2020|Chem.-Eur.J.|27|264|doi:10.1002/chem.202002628
DOI: 10.6084/m9.figshare.13546637
title: Baseline Dataset

description: <b>Overview</b><br>This is a set of overhead images of wind turbines with corresponding YOLOv3 formatted labels for object detection. These labels contain the class, x and y coordinates and the height and width of the bounding boxes for each wind turbine in the corresponding image.<br><b>Why</b><br>Deep learning can help with the analysis of energy infrastructure. Extending this work to more types of energy infrastructure can create a pipeline for in-depth energy infrastructure analysis that could provide information for energy access decision makers to choose how to provide electricity to a non-electrified region (through grid extension, micro-grids or localized power generation). <br><br><b>Method</b>The majority of the images were taken from https://figshare.com/articles/Power_Plant_Satellite_Imagery_Dataset/5307364. These images were then hand labeled and converted into formatted labels, which are also contained in original_images_and_labels. This data was then preprocessed into smaller images with dimensions of 608x608 and their corresponding labels with the same YOLOv3 format of class, x, y, height, width. These values (except for class value) have relative values from 0-1 that are proportional to the size of the images. These smaller images and labels are what are contained in the dataset. These images have resolutions varying from 0.6-1m.<br>Additional images were collected through the NAIP imagery available on Earth OnDemand and then hand-labeled.<br>
DOI: 10.6084/m9.figshare.12587564
title: Data from: Effects of host plant growth form on dropping behaviour in leaf beetles.

description: Matsubara & Sugiura (2021) Effects of host plant growth form on dropping behaviour in leaf beetles. Biological Journal of the Linnean Society (https://doi.org/10.1093/biolinnean/blaa226)<br>
DOI: 10.6084/m9.figshare.12587564.v1
title: Data from: Effects of host plant growth form on dropping behaviour in leaf beetles.

description: Matsubara & Sugiura (2021) Effects of host plant growth form on dropping behaviour in leaf beetles. Biological Journal of the Linnean Society (https://doi.org/10.1093/biolinnean/blaa226)<br>
DOI: 10.6084/m9.figshare.13551143
title: Knowledge, attitudes, and practices (KAP) regarding coronavirus disease among patients visiting eye hospitals of province number two, Nepal

description: The dataset present the data collected from patients visiting selected eye hospitals of province number two, Nepal about <b>Knowledge, attitudes, and practices (KAP) regarding coronavirus disease </b>
DOI: 10.6084/m9.figshare.13551143.v1
title: Knowledge, attitudes, and practices (KAP) regarding coronavirus disease among patients visiting eye hospitals of province number two, Nepal

description: The dataset present the data collected from patients visiting selected eye hospitals of province number two, Nepal about <b>Knowledge, attitudes, and practices (KAP) regarding coronavirus disease </b>
DOI: 10.5281/zenodo.4430224
title: Important contribution of N2O5 hydrolysis to the daytime nitrate in Xi'an, China during haze periods: isotopic analysis and WRF-Chem model simulation

description: To quantify the contributions of major formation pathways to nitrate, a field observation, coupling with a WRF-Chem model simulation, was conducted in Xi’an, an inland megacity of China. Here is the dataset of ions, stable isotope compositions of nitrogen and oxygen of nitrate, T, RH and the results simulated by WRF-Chem model.
DOI: 10.6084/m9.figshare.13551149
title: Research dataset for Wang et al., 2021.xls

description: <b>Major and trace element analyses of lamprophyres from the Shandong peninsula, China</b><br>
DOI: 10.5281/zenodo.4428246
title: Wikidata Dump My little Dump

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4428245
title: Wikidata Dump My little Dump

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.15161/oar.it/73397
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.4428672
title: Wikidata Dump Images

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4428671
title: Wikidata Dump Images

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4428674
title: Wikidata Dump Images

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4428673
title: Wikidata Dump Images

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.6084/m9.figshare.13551170
title: Perceptions of PrEP risk compensation amongst MSM

description: Qualitative interviews for project: Perceptions of PrEP risk compensation amongst MSM
DOI: 10.6084/m9.figshare.13370498.v4
title: An ATAC-seq atlas of chromatin accessibility in rat tissues

description: The Norway rat (Rattus norvegicus) serves as a valuable model for investigating complex disorders and social behaviors due to its available previous researches and easily to characterize and quantify the different type of social behavior. However, a comprehensive profiling of epigenetic regulation in rats is lacking, which is essential for a deeper understanding of diseases and social behaviors. Here, we profiled chromatin accessibility using ATAC-seq (Assay for Transposase-Accessible Chromatin coupled with next-generation sequencing) of 10 body tissues and 12 brain regions from adult rats and produced a large collection of datasets with replicates. This comprehensive ATAC-seq atlas is consisted of 187,930 accessible elements. In addition, by comparing the open chromatin landscapes among rat tissues, a total of 24,238 tissue-specific peaks and a list of putative tissue-specific transcription factors are unbiasedly identified. We further showed that many established tissue-specific transcriptional characteristics have been recapitulated in this ATAC-seq atlas, indicating that our study may pave the way for future mechanistic discoveries in diseases and social behaviors using rat as a model.
DOI: 10.6084/m9.figshare.13551176
title: Interactions BridgeDb ID Mapping Database (20210109)

description: BridgeDb ID mapping database for interactions, using Rhea (version 116, 2020-12-02) as data source.<br>Quality control was performed with BridgeDb (2.3.3) against version 2020-10-07 [https://doi.org/10.6084/m9.figshare.13187585.v1], including 2 changes to the regex patterns for Reactome and EcoCyc, see https://github.com/bridgedb/BridgeDb/pull/162 .<br><br><br>
DOI: 10.25646/7832
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.5281/zenodo.4423275
title: Dataset: Information content of ultraviolet-reflecting color patches and visual perception of body coloration in the Tyrrhenian wall lizard Podarcis tiliguerta

description: These are the data sets and R script corresponding to the scientific publication with the same title and authors. Description of these files is available in the file Note.pdf
DOI: 10.5281/zenodo.4415694
title: Dataset: Information content of ultraviolet-reflecting color patches and visual perception of body coloration in the Tyrrhenian wall lizard Podarcis tiliguerta

description: These are the data sets and R script corresponding to the scientific publication with the same title and authors. Description of these files is available in the file Note.pdf
DOI: 10.21227/p4xy-0z69
title: Time series univariate

description: Time series univariate
DOI: 10.21227/mhc0-jb60
title: Time series univariate

description: Time series univariate
DOI: 10.5281/zenodo.4430488
title: The first insight into the patterns of size and shape variation of a microcerberid isopod, Supplementary files

description: Supplementary files of the manuscript, "The first insight into the patterns of size and shape variation of a microcerberid isopod" in Water with tables and software save files.
DOI: 10.5281/zenodo.4430487
title: The first insight into the patterns of size and shape variation of a microcerberid isopod, Supplementary files

description: Supplementary files of the manuscript, "The first insight into the patterns of size and shape variation of a microcerberid isopod" in Water with tables and software save files.
DOI: 10.6084/m9.figshare.13550267
title: Curated sequence database "Life-OF-Mick" first used in Cornet, Magain et al. Exploring syntenic conservation across genomes for phylogenetic studies of organisms subjected to horizontal gene transfers: a case study with Cyanobacteria.

description: This archive is a database of 272 complete proteomes broadly sampled from the three domains of Life. Used in various projects of the Unit of Eukaryotic Phylogenomics (ULiège, Belgium), it is internally referred to as "Life-OF-Mick".<br>The main file is a FASTA file containing 2,176,699 protein sequences: <b>lifeofmick.fasta.gz</b>.<br><br>These correspond to the full conceptual translations of:- 50 archaeal genomes- 115 bacterial genomes- 107 eukaryotic genomes<br>The lineages (following NCBI Taxonomy as of Sep 2016) of the 272 organisms are given in the file <b>lifeofmick.tsv</b>.<br>Archaeal and bacterial proteomes were sampled from Ensembl Bacteria (release 30), whereas eukaryotic proteomes were hand-picked from various public genome portals (i.e., Ensembl Protists, EuPathDB, JGI, NCBI, pico-PLAZA) and institutional websites. Details of the eukaryotic sources (including download links) are available in the file <b>lifeofmick-euka-src.tsv</b>, while archaeal and bacterial download links are provided in the files <b>lifeofmick-arch-src.tsv</b> and <b>lifeofmick-bact-src.tsv</b>, respectively.<br>Eukaryotic proteomes were completed by organellar sequence data (mitochondrial and plastid genomes) fetched from the NCBI databases. A table of these complementations can be found in the file <b>lifeofmick-euka-org-compl.tsv</b>.<br>Finally, eukaryotic proteomes were dereplicated using CD-HIT and a global identity threshold of 99%.<br>Be careful that the "complete proteomes" of <i>Cryptomonas paramecium</i>, <i>Chroomonas mesostigmatica</i>, <i>Lotharella oceanica</i> and <i>Paulinella chromatophora</i> actually correspond to the "plastid" genomes only.