cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5zc68
title: MassIVE MSV000088506 - In situ cell-surface proteomic profiling in mice
title: Shuster SA, Li J, Chon U, Hu MC, Luginbuhl DJ, Udeshi ND, Carey DK, Takeo YH, Xu C, Mani D.R., Han S, Ting AY, Carr SA, Luo L. Cell-surface proteins mediate cell-cell interactions throughout the lifetime of multicellular organisms, yet there are no general methods for profiling them in vertebrate tissues. Here, we present in-situ cell-surface proteome extraction by extracellular labeling (iPEEL) in mice, which enables cell-type-specific profiling of cell-surface proteomes in native tissues. Applying iPEEL to developing and mature cerebellar Purkinje cells revealed a differential enrichment in cell-surface proteins with post-translational processing and synaptic functions in the developing and mature proteomes, respectively. A proteome-instructed in vivo loss-of-function screen identified a critical role for Armadillo-like helical domain-containing protein 4 (Armh4) in Purkinje cell dendrite morphogenesis. Armh4 overexpression also disrupts dendrite morphogenesis; this effect requires its conserved cytoplasmic domain and is augmented by disruption of endocytosis. Our results highlight the utility of cell-surface proteomic profiling in native tissues in identifying regulators of cell-surface signaling events.
DOI: 10.26208/2mgf-tx63
title: PSU DEP Topographic Wetness Index (TWI) 

description: GIS raster datasets displaying Topographic Wetness Index (TWI) for Pennsylvania by County. TWI raster datasets were derived from 2006-2008 LiDAR (Light Detection and Ranging) elevation points produced by the PAMAP Program. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum: NAD83, units: feet); blocks in the southern half are in Pennsylvania State Plane South. Raster spatial resolution is 9.6 ft (approximately 3 m). The TWI, also called Compound Topographic Index (CTI) or Topographic Convergence Index (TCI), is a hydrological-based topographic index that describes the tendency of a cell or area to accumulate and retain water under steady-state conditions. TWI is defined as Ln(Contributing Area/Slope angle). It balances contributing areas capturing the tendency to receive water versus slope angles capturing the tendency to evacuate water. An automated procedure was developed in ArcMap® for the TWI computation. Contributing areas (i.e. cumulative contributing area per unit contour length) were determined based on the D-Infinity model proposed by Tarboton, D. (1997). Lengths were measured considering cell size and whether the direction is adjacent or diagonal. Land surface slope was computed using the Horn’s method. The project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. August 2020.
DOI: 10.6084/m9.figshare.14378609
title: Table S1 Database of Poster Sessions at 2020 Meetings

description: A summary of 46 societal meetings held during 2020 and details about the organization of the meeting, switch to virtual meeting, and poster session details, along with platform and meeting website.
DOI: 10.7303/syn26524615
title: Data resource for manuscript - Characterizing gene expression changes during neurodegeneration in the 3xTg-AD mouse model of Alzheimer’s Disease
DOI: 10.5683/sp2/rkbylj
title: Test dataset 2019-02-28

description: Testing uploads
DOI: 10.5281/zenodo.5725704
title: TiO2 with AMOX and Cipro

description: Data of TiO2 with AMOX and Cipro
DOI: 10.5281/zenodo.5725703
title: TiO2 with AMOX and Cipro

description: Data of TiO2 with AMOX and Cipro
DOI: 10.5281/zenodo.5748559
title: Dataset from: Impact of the electron density and temperature gradient on drift-wave turbulence in LAPD

description: In this paper we present an experimental study of edge turbulence in the Large Plasma Device at UCLA. We utilize a scan of discharge power to show experimentally that turbulent density fluctuations decrease with decreasing density gradient, as predicted for resistive drift wave turbulence. We also observe that the introduction of electron temperature gradients leads to a reduction in the amplitude of the density fluctuations. The electron temperature gradient reduces the low frequency fluctuations up to 10 kHz, while at higher frequencies, a π phase shift in the density-potential cross-phase is observed. We find that the potential fluctuations do not follow the same trends and that the basic Boltzmann relationship between potential and density fluctuations is violated. We also discuss the impact of collisionality and parallel flows.
DOI: 10.5281/zenodo.5754301
title: Fire danger indices historical data from the Copernicus Emergency Management Service

description: June, July August consolidated data. Generated using Copernicus Climate Change Service information 2021.
description: Generated using Copernicus Climate Change Service information 2021.
description: {"references": ["Copernicus Programme"]}
DOI: 10.5281/zenodo.5752389
title: Collection of Tweets from Switzerland sampled 2015 - 2021

description: This dataset contains a collection of 15 million Tweets from Switzerland between 2015 and 2021. The time frame between beginning of 2020 and Mai 2021 containing Tweets during the beginning of the COVID-19 pandemic is sampled without any gaps. The following publication used this dataset: Delbiaggio K, Hauser CJ, Kaufmann M (2018) The proximity bias of communication recorded on Twitter in Switzerland. In: Grasjö U, Karlsson C, Bernhard I (eds) Geography, Open Innovation and Entrepreneurship. Edward Elgar Publishing Limited, Northampton MA
DOI: 10.5281/zenodo.5752388
title: Collection of Tweets from Switzerland sampled 2015 - 2021

description: This dataset contains a collection of 15 million Tweets from Switzerland between 2015 and 2021. The time frame between beginning of 2020 and Mai 2021 containing Tweets during the beginning of the COVID-19 pandemic is sampled without any gaps. The following publication used this dataset: Delbiaggio K, Hauser CJ, Kaufmann M (2018) The proximity bias of communication recorded on Twitter in Switzerland. In: Grasjö U, Karlsson C, Bernhard I (eds) Geography, Open Innovation and Entrepreneurship. Edward Elgar Publishing Limited, Northampton MA
DOI: 10.3886/e148481
title: Data and code for: "Estimating Social Preferences and Gift Exchange at Work"

description: This deposit contains code and data for "Estimating Social Preferences and Gift Exchange at Work".<br>Abstract: We design three field experiments to estimate the nature and magnitude of workers’ social preferences towards their employers. The experiments vary the return to the employer and employer generosity towards workers (“gifts”). Including variation in pay rates—either piece rates or wage rates—allows us to estimate the elasticity of work outcomes and benchmark the workers' social preferences toward the employer. The first experiment measures productivity—units of output produced in a fixed amount of time. The second and third experiments measure the willingness to do extra work. We document that productivity is rather unresponsive to financial incentives, while the willingness to perform extra work is very responsive. In terms of social preferences, we document, first, that workers exert effort for their employer even in the absence of private incentives, but are insensitive to the return to the employer. This result is consistent with models of warm glow or social norms regarding work effort, but not pure altruism towards the employer. Second, while we do not detect any effect of the gifts in the productivity experiment, we find moderate positive impacts in the extra-work experiments. We show that this difference is partly explained by the disparate elasticities of productivity and extra work.
description: One-time workers; Student workers; Online Sample.
DOI: 10.3886/e148481v1
title: Data and code for: "Estimating Social Preferences and Gift Exchange at Work"

description: This deposit contains code and data for "Estimating Social Preferences and Gift Exchange at Work".<br>Abstract: We design three field experiments to estimate the nature and magnitude of workers’ social preferences towards their employers. The experiments vary the return to the employer and employer generosity towards workers (“gifts”). Including variation in pay rates—either piece rates or wage rates—allows us to estimate the elasticity of work outcomes and benchmark the workers' social preferences toward the employer. The first experiment measures productivity—units of output produced in a fixed amount of time. The second and third experiments measure the willingness to do extra work. We document that productivity is rather unresponsive to financial incentives, while the willingness to perform extra work is very responsive. In terms of social preferences, we document, first, that workers exert effort for their employer even in the absence of private incentives, but are insensitive to the return to the employer. This result is consistent with models of warm glow or social norms regarding work effort, but not pure altruism towards the employer. Second, while we do not detect any effect of the gifts in the productivity experiment, we find moderate positive impacts in the extra-work experiments. We show that this difference is partly explained by the disparate elasticities of productivity and extra work.
description: One-time workers; Student workers; Online Sample.
DOI: 10.5281/zenodo.5753476
title: WikiMed and PubMedDS: Two large-scale datasets for medical concept extraction and normalization research

description: Two large-scale, automatically-created datasets of medical concept mentions, linked to the Unified Medical Language System (UMLS). <strong>WikiMed</strong> Derived from Wikipedia data. Mappings of Wikipedia page identifiers to UMLS Concept Unique Identifiers (CUIs) was extracted by crosswalking Wikipedia, Wikidata, Freebase, and the NCBI Taxonomy to reach existing mappings to UMLS CUIs. This created a 1:1 mapping of approximately 60,500 Wikipedia pages to UMLS CUIs. Links to these pages were then extracted as mentions of the corresponding UMLS CUIs. WikiMed contains: 393,618 Wikipedia page texts 1,067,083 mentions of medical concepts 57,739 unique UMLS CUIs Manual evaluation of 100 random samples of WikiMed found 91% accuracy in the automatic annotations at the level of UMLS CUIs, and 95% accuracy in terms of semantic type. <strong>PubMedDS</strong> Derived from biomedical literature abstracts from PubMed. Mentions were automatically identified using distant supervision based on Medical Subject Heading (MeSH) headers assigned to the papers in PubMed, and recognition of medical concept mentions using the high-performance scispaCy model. MeSH header codes are included as well as their mappings to UMLS CUIs. PubMedDS contains: 13,197,430 abstract texts 57,943,354 medical concept mentions 44,881 unique UMLS CUIs Comparison with existing manually-annotated datasets (NCBI Disease Corpus, BioCDR, and MedMentions) found 75-90% precision in automatic annotations. Please note this dataset is <em>not </em>a comprehensive annotation of medical concept mentions in these abstracts (only mentions located through distant supervision from MeSH headers were included), but is intended as data for <em>concept n</em><em>ormalization</em> research. Due to its size, PubMedDS is distributed as 30 individual files of approximately 1.5 million mentions each. <strong>Data format</strong> Both datasets use JSON format with one document per line. Each document has the following structure: <pre><code class="language-json">{ "_id": "A unique identifier of each document", "text": "Contains text over which mentions are ", "title": "Title of Wikipedia/PubMed Article", "split": "[Not in PubMedDS] Dataset split: &lt;train/test/valid&gt;", "mentions": [ { "mention": "Surface form of the mention", "start_offset": "Character offset indicating start of the mention", "end_offset": "Character offset indicating end of the mention", "link_id": "UMLS CUI. In case of multiple CUIs, they are concatenated using '|', i.e., CUI1|CUI2|..." }, {} ] }</code></pre>
description: Creation of these datasets was supported in part by National Science Foundation Grants (IIS 1917668 and IIS 1822831), Dow Chemical, and the National Library of Medicine of the National Institutes of Health under award number T15 LM007059.
DOI: 10.5281/zenodo.5755153
title: Wikipedia Articles with Quality label FA, GA, A, B and C

description: This dataset contains a filtered collection of good quality Wikipedia Articles: * FA: Featured Articles * GA: Good Articles * Quality lables A, B and C
DOI: 10.5281/zenodo.5755152
title: Wikipedia Articles with Quality label FA, GA, A, B and C

description: This dataset contains a filtered collection of good quality Wikipedia Articles: * FA: Featured Articles * GA: Good Articles * Quality lables A, B and C
DOI: 10.25934/pr00004464.0
title: Available IPD datapackage for study 'A Phase 4 Comparison of Duloxetine Dosing Strategies in the Treatment of Korean Patients With Major Depressive Disorder'

description: The purpose of this study is to assess nausea severity in response to four different drug dosing strategies of Duloxetine (30 mg with food, 60 mg with food, 30 mg without food, and 60 mg without food) in Korean patients with major depressive disorder (MDD).
DOI: 10.25573/serc.15043920
title: Dataset: Carbon sequestration across Northeastern, Florida Coastal Wetlands

description: In 2018, we established plots in dominant salt marsh vegetation and dominant mangrove vegetation for 3 wetland sites along the Northeastern coast of Florida that vary in latitude and mangrove abundance. Soil carbon concentrations were examined across 3 sites in plots dominated by either mangrove or marsh plants with samples taken at 3 difference depths (5cm, 15cm, 30cm). Note: Some samples at the lower end of our bulk density range samples may exhibit decreased bulk density due to shelly soils at one of our sites.
DOI: 10.25573/serc.15043920.v1
title: Dataset: Carbon sequestration across Northeastern, Florida Coastal Wetlands

description: In 2018, we established plots in dominant salt marsh vegetation and dominant mangrove vegetation for 3 wetland sites along the Northeastern coast of Florida that vary in latitude and mangrove abundance. Soil carbon concentrations were examined across 3 sites in plots dominated by either mangrove or marsh plants with samples taken at 3 difference depths (5cm, 15cm, 30cm). Note: Some samples at the lower end of our bulk density range samples may exhibit decreased bulk density due to shelly soils at one of our sites.
DOI: 10.5281/zenodo.5755155
title: WikiMed and PubMedDS: Two large-scale datasets for medical concept extraction and normalization research

description: Two large-scale, automatically-created datasets of medical concept mentions, linked to the Unified Medical Language System (UMLS). <strong>WikiMed</strong> Derived from Wikipedia data. Mappings of Wikipedia page identifiers to UMLS Concept Unique Identifiers (CUIs) was extracted by crosswalking Wikipedia, Wikidata, Freebase, and the NCBI Taxonomy to reach existing mappings to UMLS CUIs. This created a 1:1 mapping of approximately 60,500 Wikipedia pages to UMLS CUIs. Links to these pages were then extracted as mentions of the corresponding UMLS CUIs. WikiMed contains: 393,618 Wikipedia page texts 1,067,083 mentions of medical concepts 57,739 unique UMLS CUIs Manual evaluation of 100 random samples of WikiMed found 91% accuracy in the automatic annotations at the level of UMLS CUIs, and 95% accuracy in terms of semantic type. <strong>PubMedDS</strong> Derived from biomedical literature abstracts from PubMed. Mentions were automatically identified using distant supervision based on Medical Subject Heading (MeSH) headers assigned to the papers in PubMed, and recognition of medical concept mentions using the high-performance scispaCy model. MeSH header codes are included as well as their mappings to UMLS CUIs. PubMedDS contains: 13,197,430 abstract texts 57,943,354 medical concept mentions 44,881 unique UMLS CUIs Comparison with existing manually-annotated datasets (NCBI Disease Corpus, BioCDR, and MedMentions) found 75-90% precision in automatic annotations. Please note this dataset is <em>not </em>a comprehensive annotation of medical concept mentions in these abstracts (only mentions located through distant supervision from MeSH headers were included), but is intended as data for <em>concept n</em><em>ormalization</em> research. Due to its size, PubMedDS is distributed as 30 individual files of approximately 1.5 million mentions each. <strong>Data format</strong> Both datasets use JSON format with one document per line. Each document has the following structure: <pre><code class="language-json">{ "_id": "A unique identifier of each document", "text": "Contains text over which mentions are ", "title": "Title of Wikipedia/PubMed Article", "split": "[Not in PubMedDS] Dataset split: &lt;train/test/valid&gt;", "mentions": [ { "mention": "Surface form of the mention", "start_offset": "Character offset indicating start of the mention", "end_offset": "Character offset indicating end of the mention", "link_id": "UMLS CUI. In case of multiple CUIs, they are concatenated using '|', i.e., CUI1|CUI2|..." }, {} ] }</code></pre> <strong>Version history</strong> Version Notes 1.0.0 Initial release 1.0.1 Corrected duplication error in WikiMed.zip file
description: Creation of these datasets was supported in part by National Science Foundation Grants (IIS 1917668 and IIS 1822831), Dow Chemical, and the National Library of Medicine of the National Institutes of Health under award number T15 LM007059.
DOI: 10.5281/zenodo.5753475
title: WikiMed and PubMedDS: Two large-scale datasets for medical concept extraction and normalization research

description: Two large-scale, automatically-created datasets of medical concept mentions, linked to the Unified Medical Language System (UMLS). <strong>WikiMed</strong> Derived from Wikipedia data. Mappings of Wikipedia page identifiers to UMLS Concept Unique Identifiers (CUIs) was extracted by crosswalking Wikipedia, Wikidata, Freebase, and the NCBI Taxonomy to reach existing mappings to UMLS CUIs. This created a 1:1 mapping of approximately 60,500 Wikipedia pages to UMLS CUIs. Links to these pages were then extracted as mentions of the corresponding UMLS CUIs. WikiMed contains: 393,618 Wikipedia page texts 1,067,083 mentions of medical concepts 57,739 unique UMLS CUIs Manual evaluation of 100 random samples of WikiMed found 91% accuracy in the automatic annotations at the level of UMLS CUIs, and 95% accuracy in terms of semantic type. <strong>PubMedDS</strong> Derived from biomedical literature abstracts from PubMed. Mentions were automatically identified using distant supervision based on Medical Subject Heading (MeSH) headers assigned to the papers in PubMed, and recognition of medical concept mentions using the high-performance scispaCy model. MeSH header codes are included as well as their mappings to UMLS CUIs. PubMedDS contains: 13,197,430 abstract texts 57,943,354 medical concept mentions 44,881 unique UMLS CUIs Comparison with existing manually-annotated datasets (NCBI Disease Corpus, BioCDR, and MedMentions) found 75-90% precision in automatic annotations. Please note this dataset is <em>not </em>a comprehensive annotation of medical concept mentions in these abstracts (only mentions located through distant supervision from MeSH headers were included), but is intended as data for <em>concept n</em><em>ormalization</em> research. Due to its size, PubMedDS is distributed as 30 individual files of approximately 1.5 million mentions each. <strong>Data format</strong> Both datasets use JSON format with one document per line. Each document has the following structure: <pre><code class="language-json">{ "_id": "A unique identifier of each document", "text": "Contains text over which mentions are ", "title": "Title of Wikipedia/PubMed Article", "split": "[Not in PubMedDS] Dataset split: &lt;train/test/valid&gt;", "mentions": [ { "mention": "Surface form of the mention", "start_offset": "Character offset indicating start of the mention", "end_offset": "Character offset indicating end of the mention", "link_id": "UMLS CUI. In case of multiple CUIs, they are concatenated using '|', i.e., CUI1|CUI2|..." }, {} ] }</code></pre> <strong>Version history</strong> Version Notes 1.0.0 Initial release 1.0.1 Corrected duplication error in WikiMed.zip file
description: Creation of these datasets was supported in part by National Science Foundation Grants (IIS 1917668 and IIS 1822831), Dow Chemical, and the National Library of Medicine of the National Institutes of Health under award number T15 LM007059.
DOI: 10.6084/m9.figshare.3761562.v2062
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.34740/kaggle/dsv/2883732
title: ACLED Bangladesh
DOI: 10.25934/pr00006545.0
title: Available IPD datapackage for study 'A Randomized, Open-Label, Phase 2 Study of Abemaciclib Plus Tamoxifen or Abemaciclib Alone, in Women With Previously Treated Hormone Receptor-Positive, HER2-Negative, Metastatic Breast Cancer'

description: The main purpose of this study is to evaluate the safety and efficacy of abemaciclib plus tamoxifen or abemaciclib alone in women with previously treated hormone receptor-positive (HR+), human epidermal growth factor receptor 2 negative (HER2-), metastatic breast cancer.
DOI: 10.5281/zenodo.5755825
title: Com.TiO2 and N-TiO2 datasets

description: Com.TiO2 and N-TiO2 datasets for Amoxicillin and Ciproflaxin photodegradation
DOI: 10.5281/zenodo.5755824
title: Com.TiO2 and N-TiO2 datasets

description: Com.TiO2 and N-TiO2 datasets for Amoxicillin and Ciproflaxin photodegradation
DOI: 10.25934/00007122
title: Evaluation of the Pharmacokinetics and Safety of Brigatinib in Patients with Chronic Hepatic Impairment and Matched Healthy Subjects

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00007123
title: Evaluation of the Pharmacokinetics and Safety of Brigatinib in Patients with Chronic Renal Impairment and Matched Healthy Subjects

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5517/ccdc.csd.cc21h04q
title: CCDC 1891252: Experimental Crystal Structure Determination

description: Related Article: Xin Wang, Jinhuan Dong, Xianxiu Xu, Bo Tang|2021|Org.Lett.|23|9063|doi:10.1021/acs.orglett.1c03314
DOI: 10.5517/ccdc.csd.cc21j3nc
title: CCDC 1892322: Experimental Crystal Structure Determination

description: Related Article: Xin Wang, Jinhuan Dong, Xianxiu Xu, Bo Tang|2021|Org.Lett.|23|9063|doi:10.1021/acs.orglett.1c03314
DOI: 10.5517/ccdc.csd.cc24862q
title: CCDC 1974082: Experimental Crystal Structure Determination

description: Related Article: Qi-Fa Chen, Ze-Yu Cheng, Rong-Zhen Liao, Ming-Tian Zhang|2021|J.Am.Chem.Soc.|143|19761|doi:10.1021/jacs.1c08078
DOI: 10.5517/ccdc.csd.cc24f8gb
title: CCDC 1978961: Experimental Crystal Structure Determination

description: Related Article: Stefanie Beck, Christoph Jessen, Andreas J. Kornath|2021|ChemistryOpen|10|1160|doi:10.1002/open.202100229