cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3871752
title: TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 1, October 2019 - April 2020)

description: <strong>TweetsCOV19</strong><strong> </strong>is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. <strong>Metadata</strong> information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong>, and <strong>resolved URLs </strong>are exposed in RDF using established RDF/S vocabularies*. We also provide a <em><strong>tab-separated values (tsv)</strong></em> version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices: Tweet Id: Long. Username: String. Encrypted for privacy issues*. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ). #Followers: Integer. #Friends: Integer. #Retweets: Integer. #Favorites: Integer. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;". Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1"). Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;". Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;". URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;" This dataset consists of <strong>8,151,524 tweets</strong> in total, posted by <strong>3,664,518 users</strong> and reflects the societal discourse about COVID-19 on Twitter in the period of October 2019 until April 2020. To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.6084/m9.figshare.14096621
title: Data_Brandmeier et al_Intercropping in high input agriculture supports arthropod diversity without risking significant yield losses

description: Datasets and R scripts associated with the study "Intercropping in high input agriculture supports arthropod diversity without risking significant yield losses" as part of the project DIVERSify ('Designing innovative plant teams for ecosystem resilience and agricultural sustainability')<br>
DOI: 10.6084/m9.figshare.14096621.v1
title: Data_Brandmeier et al_Intercropping in high input agriculture supports arthropod diversity without risking significant yield losses

description: Datasets and R scripts associated with the study "Intercropping in high input agriculture supports arthropod diversity without risking significant yield losses" as part of the project DIVERSify ('Designing innovative plant teams for ecosystem resilience and agricultural sustainability')<br>
DOI: 10.6084/m9.figshare.14192006
title: Appendix_A systematic mapping study on the application of design thinking in requirement engineering.pdf

description: Hereafter, in this Appendix some relevant content that cannot be included in the paper due to space constraints is presented. In order not to generate confusion, the tables will be listed in this Appendix with the suffix of the letter A in their titles.
DOI: 10.6084/m9.figshare.14192006.v1
title: Appendix_A systematic mapping study on the application of design thinking in requirement engineering.pdf

description: Hereafter, in this Appendix some relevant content that cannot be included in the paper due to space constraints is presented. In order not to generate confusion, the tables will be listed in this Appendix with the suffix of the letter A in their titles.
DOI: 10.5285/76c2a17a-f461-47af-95fc-0f8c0ea6a477
title: Kinetics of enhanced cementation reactions for CO2 leakage remediation and fault healing processes

description: This dataset presents the amount of different magnesium carbonates under different conditions. Here, using batch reactor experiments and mineralogical characterization, we explored magnesite precipitation kinetics in chemically complex fluids whereby the impact of fluid acidity and alkalinity, NaCl, and MgO nanoparticles was investigated. The dataset was created within SECURe project (Subsurface Evaluation of CCS and Unconventional Risks) - https://www.securegeoenergy.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 764531
DOI: 10.6084/m9.figshare.14191874
title: DBturbidity.zip

description: This dataset contains samples of water with different levels of turbidity acquiered on different mobile devices.
DOI: 10.6084/m9.figshare.14191874.v1
title: DBturbidity.zip

description: This dataset contains samples of water with different levels of turbidity acquiered on different mobile devices.
DOI: 10.5517/ccdc.csd.cc23fyyg
title: CCDC 1949836: Experimental Crystal Structure Determination

description: Related Article: Zishuo Zhou, Hongfei Han, Zijing Chen, Rui Gao, Zhen Liu, Jie Su, Nana Xin, Xiaobing Yang, Liangbing Gan|2019|Angew.Chem.,Int.Ed.|58|17690|doi:10.1002/anie.201911631
DOI: 10.5517/ccdc.csd.cc23fyzh
title: CCDC 1949837: Experimental Crystal Structure Determination

description: Related Article: Zishuo Zhou, Hongfei Han, Zijing Chen, Rui Gao, Zhen Liu, Jie Su, Nana Xin, Xiaobing Yang, Liangbing Gan|2019|Angew.Chem.,Int.Ed.|58|17690|doi:10.1002/anie.201911631
DOI: 10.5517/ccdc.csd.cc23fz1l
title: CCDC 1949839: Experimental Crystal Structure Determination

description: Related Article: Zishuo Zhou, Hongfei Han, Zijing Chen, Rui Gao, Zhen Liu, Jie Su, Nana Xin, Xiaobing Yang, Liangbing Gan|2019|Angew.Chem.,Int.Ed.|58|17690|doi:10.1002/anie.201911631
DOI: 10.5517/ccdc.csd.cc23fz2m
title: CCDC 1949840: Experimental Crystal Structure Determination

description: Related Article: Zishuo Zhou, Hongfei Han, Zijing Chen, Rui Gao, Zhen Liu, Jie Su, Nana Xin, Xiaobing Yang, Liangbing Gan|2019|Angew.Chem.,Int.Ed.|58|17690|doi:10.1002/anie.201911631
DOI: 10.5517/ccdc.csd.cc23fz8t
title: CCDC 1949846: Experimental Crystal Structure Determination

description: Related Article: Zishuo Zhou, Hongfei Han, Zijing Chen, Rui Gao, Zhen Liu, Jie Su, Nana Xin, Xiaobing Yang, Liangbing Gan|2019|Angew.Chem.,Int.Ed.|58|17690|doi:10.1002/anie.201911631
DOI: 10.6084/m9.figshare.12130980.v82
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.13373072
title: Biome occupancy data used in "The effect of single biome occupancy on the estimation of biome shifts and the detection of biome conservatism"

description: Data files for running BioGeoBEARS analysis on biomes occupied by New Zealand <i>Chionochloa, Coprosma, Melicytus, Myrsine, Pseudopanax, Poa</i> X, <i>Rytidopserma</i> A, <i>Rytidosperma</i> B, and <i>Veronica</i>. Each clade has a geog file for a multiple biomes per species approach (_geog_file_multi.txt), and a single biome per species approach (_geog_file_single.txt) so analyses can be run both ways. Time stratification to control biome availability uses the areas_allowed.txt and time_periods.txt files, which make Forest always available, Open available from 4 Ma, and Alpine from 1.9 Ma.<br>
DOI: 10.6084/m9.figshare.13373072.v1
title: Biome occupancy data used in "The effect of single biome occupancy on the estimation of biome shifts and the detection of biome conservatism"

description: Data files for running BioGeoBEARS analysis on biomes occupied by New Zealand <i>Chionochloa, Coprosma, Melicytus, Myrsine, Pseudopanax, Poa</i> X, <i>Rytidopserma</i> A, <i>Rytidosperma</i> B, and <i>Veronica</i>. Each clade has a geog file for a multiple biomes per species approach (_geog_file_multi.txt), and a single biome per species approach (_geog_file_single.txt) so analyses can be run both ways. Time stratification to control biome availability uses the areas_allowed.txt and time_periods.txt files, which make Forest always available, Open available from 4 Ma, and Alpine from 1.9 Ma.<br>
DOI: 10.5281/zenodo.4593528
title: Diversity, Equity, and Inclusion: tackling gross under-representation and recognition among talents in Geochemistry and Cosmochemistry - dataset

description: Dataset used in the article entitled "Diversity, Equity, and Inclusion: tackling gross under-representation and recognition among talents in Geochemistry and Cosmochemistry" and available as a preprint https://eartharxiv.org/repository/view/2108/
DOI: 10.5281/zenodo.4593527
title: Diversity, Equity, and Inclusion: tackling gross under-representation and recognition among talents in Geochemistry and Cosmochemistry - dataset

description: Dataset used in the article entitled "Diversity, Equity, and Inclusion: tackling gross under-representation and recognition among talents in Geochemistry and Cosmochemistry" and available as a preprint https://eartharxiv.org/repository/view/2108/
DOI: 10.6084/m9.figshare.14191943
title: MDA-MB-231-B7H3KO-8NQ_20201202.raw

description: B7H3 knocked out MDA-MB-231 cell lines were supplied with 8NQ mutated B7H3. Mass spectrometry was used to quantify the type of proteins in the cells.<br>
DOI: 10.6084/m9.figshare.14191943.v1
title: MDA-MB-231-B7H3KO-8NQ_20201202.raw

description: B7H3 knocked out MDA-MB-231 cell lines were supplied with 8NQ mutated B7H3. Mass spectrometry was used to quantify the type of proteins in the cells.<br>
DOI: 10.5281/zenodo.3942403
title: ODP Leg 135, Hole 834B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.5281/zenodo.3942402
title: ODP Leg 135, Hole 834B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.5281/zenodo.3942405
title: ODP Leg 135, Hole 835B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.5281/zenodo.3942404
title: ODP Leg 135, Hole 835B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.5281/zenodo.3942407
title: ODP Leg 135, Hole 838B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.5281/zenodo.3942406
title: ODP Leg 135, Hole 838B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.5281/zenodo.3942409
title: ODP Leg 135, Hole 839B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.5281/zenodo.3942408
title: ODP Leg 135, Hole 839B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.5281/zenodo.3942411
title: ODP Leg 135, Hole 840B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.5281/zenodo.3942410
title: ODP Leg 135, Hole 840B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.6084/m9.figshare.12459056.v12
title: agnostosDB_dbf02445-20200519

description: The agnostosDB (dbf02445-20200519) is a comprehensive dataset of microbial gene clusters (GCs) from genomes and metagenomes. It contains 5,287,759 GCs and more than 280M genes coming from the bacterial and archaeal Genome Taxonomy Database (GTDB) genomes, and from five large-scale metagenomic projects: 583 marine metagenomes from Tara Oceans expedition (TARA), Malaspina expedition, Ocean Sampling Day (OSD), Global Ocean Sampling Expedition (GOS), complemented with 1,246 metagenomes from the Human Microbiome Project (HMP) phase I and II. The dataset is described in Vanni et al. 2020.<br>Additional and more detailed information about the dataset creation and some of its applications, can be found at https://dark.metagenomics.eu/.Related to the agnostosDB is the agnostos-wf, a snakemake workflow stored in the GitHub repository https://github.com/functional-dark-side/agnostos-wf. The agnostos-wf allows to search the agnostosDB gene cluster HMM profiles and/or to integrate new sequence data (genes/contigs) in it.
DOI: 10.5281/zenodo.3942413
title: ODP Leg 135, Hole 841C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.5281/zenodo.3942412
title: ODP Leg 135, Hole 841C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 135: Lau Basin
DOI: 10.20351/29gd20081017
title: CASCADES-1 Cruise, RV García del Cid

description: CASCADES-1 Cruise (29GD20081017) carried out on the Research Vessel García del Cid in 2008.
DOI: 10.6084/m9.figshare.14191724
title: Career Transition to Research Leadership in Africa Transcript - Interview 5.pdf

description: Career transition to research leadership, transcript of qualitative interview with research leader 5
DOI: 10.6084/m9.figshare.14192081
title: Supplementary Material for: Are Breast Cancer Nomograms Still Valid to Predict the Need for Axillary Dissection?

description: <b><i>Background:</i></b> Nomograms can help in estimating the nodal status among clinically node-negative patients. Yet their validity in external cohorts over time is unknown. If the nodal stage can be estimated preoperatively, the need for axillary dissection can be decided. <b><i>Objectives:</i></b> The aim of this study was to validate three existing nomograms predicting 4 or more axillary lymph node metastases. <b><i>Method:</i></b> The risk for ≥4 lymph node metastases was calculated for n = 529 eligible breast cancer patients using the nomograms of Chagpar et al. [Ann Surg Oncol. 2007;14:670–7], Katz et al. [J Clin Oncol. 2008;26(13):2093–8], and Meretoja et al. [Breast Cancer Res Treat. 2013;138(3):817–27]. Discrimination and calibration were calculated for each nomogram to determine their validity. <b><i>Results:</i></b> In this cohort, the AUC values for the Chagpar, Katz, and Meretoja models were 0.79 (95% CI 0.74–0.83), 0.87 (95% CI 0.83–0.91), and 0.82 (95% CI 0.76–0.86), respectively, showing good discrimination between patients with and without high nodal burdens. <b><i>Conclusion:</i></b> This study presents support for the use of older breast cancer nomograms and confirms their current validity in an external population.
DOI: 10.20351/29gd20080718
title:  PERMILMED-I Cruise, RV García del Cid

description: PERFILMED-I Cruise (29GD20080718) carried out on the Research Vessel García del Cid in 2008
DOI: 10.6084/m9.figshare.14191802
title: Supplementary Material for: Primordial Germ Cell Migration and Histological and Molecular Characterization of Gonadal Differentiation in Pachón Cavefish <b><i>Astyanax mexicanus</i></b>

description: The genetic regulatory network governing vertebrate gonadal differentiation appears less conserved than previously thought. Here, we investigated the gonadal development of <i>Astyanax mexicanus</i> Pachón cavefish by looking at primordial germ cells (PGCs) migration and proliferation, gonad histology, and gene expression patterns. We showed that PGCs are first detected at the 80% epiboly stage and then reach the gonadal primordium at 1 day post-fertilization (dpf). However, in contrast to the generally described absence of PGCs proliferation during their migration phase, PGCs number in cavefish doubles between early neurula and 8–9 somites stages. Combining both gonadal histology and <i>vasa</i> (germ cell marker) expression patterns, we observed that ovarian and testicular differentiation occurs around 65 dpf in females and 90 dpf in males, respectively, with an important inter-individual variability. The expression patterns of <i>dmrt1</i>, <i>gsdf</i>, and <i>amh</i> revealed a conserved predominant male expression during cavefish gonadal development, but none of the ovarian differentiation genes, i. e., <i>foxl2a</i>, <i>cyp19a1a</i>, and <i>wnt4b</i> displayed an early sexually dimorphic expression, and surprisingly all these genes exhibited predominant expression in adult testes. Altogether, our results lay the foundation for further research on sex determination and differentiation in <i>A. mexicanus</i> and contribute to the emerging picture that the vertebrate sex differentiation downstream regulatory network is less conserved than previously thought, at least in teleost fishes.
DOI: 10.17632/p9gdmnt3ph.1
title: Mors & Waguespack 2021, "Fast success and slow failure: the process speed of dispersed research teams"

description: Replication materials for Mors & Waguespack, forthcoming 2021, "Fast success and slow failure: the process speed of dispersed research teams", Research Policy For further information see "readme.pdf"