cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20151827
title: data.xlsx

description: Raw data of the study
DOI: 10.5281/zenodo.5207693
title: Source data for "Single-molecule analysis of actin filament debranching by cofilin and GMF"

description: Source data for "Single-molecule analysis of actin filament debranching by cofilin and GMF"
DOI: 10.5281/zenodo.5207692
title: Source data for "Single-molecule analysis of actin filament debranching by cofilin and GMF"

description: Source data for "Single-molecule analysis of actin filament debranching by cofilin and GMF"
DOI: 10.5683/sp/jq0nlc
title: EdChat Public Tweets

description: This is a data set of 482,251 public tweets and retweets (Twitter IDs) posted by the #edchat online community of educators who discuss current trends in teaching with technology. The data set was collected via Twitter's Streaming API between Feb 1, 2018 and Apr 4, 2018, and was used as part of the research on developing a learning analytics dashboard for teaching and learning with Twitter. Following Twitter's terms of service, the data set only includes unique identifiers of relevant tweets. To collect the actual tweets that are part of this data set, you will need to use one of the available third party tools such as Hydrator or Twarc ("hydrate" function). As part of this release, we are also attaching an enriched version of this data set that contains sentiment and opinion analysis labels that were produced by analyzing each tweet with the help of the NLTK SentimentAnalyzer Python package. *This work was supported in part by eCampusOntario and The Social Sciences and Humanities Research Council of Canada.
DOI: 10.5281/zenodo.6728363
title: Modeling Word Importance in Conversational Transcripts from the Perspective of Deaf and Hard of Hearing Viewers

description: <strong>MaskedPOSAugmentedData.csv</strong> <strong>This file contains word embeddings of 6659 tokens augmented with POS tagging and word importance score. The embedding size for each token is a one dimensional vector of length 768 by 1. Due to augmenting POS tagging, the new feature vector becomes a size of 769 by 1. So now the total feature matrix size is 6659 by 769. In the dataset the final column represents the word importance score. </strong> <br> <strong>MaskedSentenceWithImportanceTag.csv</strong> <strong>This dataset contains the newly generated sentence using standard Masking technique and their corresponding token-wise importance score.</strong><br> <strong>Data cleaning procedure</strong> <strong>Using several steps, we have cleaned the “switchboard corpus” that we are using for this particular study. Here are the steps we followed:</strong> <strong>– Convert all the letter into lower case</strong> <strong>– Punctuation has be removed</strong> <strong>– Numbers or Cardinal values have been converted to text representation </strong> <strong>– We use lemmatization techniques to eradicate the possibility of multiple versions of the same word token.</strong><br> <br> <strong>Annotation Instruction</strong> <strong>If researchers intend to produce additional masked text for further the size of the dataset, we recommend using the method we described in the paper.</strong> <strong>However, if someone wants to manually annotate the words or tokens in a dataset, it is important to remember that annotators need to put a score on each word based on its relative importance within a sentence or the information available around that text. It may not be appropriate to allow annotators to read the whole document first and then conduct annotation. Also using multiple annotators is recommended otherwise interrater agreement may not work well.</strong> <strong>Test and training data splitting</strong> <strong>As described in the paper, during our experiment, we have retained 10% of data as test data and use 90% data to train the models. For proper replication, we recommend reading our paper thoroughly. </strong> <strong>We understand that the dataset size is relatively small for training and testing a model that might be reliable. It is important to remember that data annotation with this particular user group might be challenging. </strong> <strong>N.B: While augmenting the new feature within the dataset, a portion of data has been excluded during the data curation phase. For validation, we have replicated the previous models so that we can measure how the dataset can perform with this newly formed dataset. </strong>
DOI: 10.5281/zenodo.6728362
title: Modeling Word Importance in Conversational Transcripts from the Perspective of Deaf and Hard of Hearing Viewers

description: <strong>MaskedPOSAugmentedData.csv</strong> <strong>This file contains word embeddings of 6659 tokens augmented with POS tagging and word importance score. The embedding size for each token is a one dimensional vector of length 768 by 1. Due to augmenting POS tagging, the new feature vector becomes a size of 769 by 1. So now the total feature matrix size is 6659 by 769. In the dataset the final column represents the word importance score. </strong> <br> <strong>MaskedSentenceWithImportanceTag.csv</strong> <strong>This dataset contains the newly generated sentence using standard Masking technique and their corresponding token-wise importance score.</strong><br> <strong>Data cleaning procedure</strong> <strong>Using several steps, we have cleaned the “switchboard corpus” that we are using for this particular study. Here are the steps we followed:</strong> <strong>– Convert all the letter into lower case</strong> <strong>– Punctuation has be removed</strong> <strong>– Numbers or Cardinal values have been converted to text representation </strong> <strong>– We use lemmatization techniques to eradicate the possibility of multiple versions of the same word token.</strong><br> <br> <strong>Annotation Instruction</strong> <strong>If researchers intend to produce additional masked text for further the size of the dataset, we recommend using the method we described in the paper.</strong> <strong>However, if someone wants to manually annotate the words or tokens in a dataset, it is important to remember that annotators need to put a score on each word based on its relative importance within a sentence or the information available around that text. It may not be appropriate to allow annotators to read the whole document first and then conduct annotation. Also using multiple annotators is recommended otherwise interrater agreement may not work well.</strong> <strong>Test and training data splitting</strong> <strong>As described in the paper, during our experiment, we have retained 10% of data as test data and use 90% data to train the models. For proper replication, we recommend reading our paper thoroughly. </strong> <strong>We understand that the dataset size is relatively small for training and testing a model that might be reliable. It is important to remember that data annotation with this particular user group might be challenging. </strong> <strong>N.B: While augmenting the new feature within the dataset, a portion of data has been excluded during the data curation phase. For validation, we have replicated the previous models so that we can measure how the dataset can perform with this newly formed dataset. </strong>
DOI: 10.17026/dans-2b5-jqjy
title: Veldbrandovens omgeving Asselsestraat, gemeente Apeldoorn

description: onderzoeksrapport
DOI: 10.5281/zenodo.6658889
title: Cubic seed fractals for Molecular-DNA

description: Files to accompany the Geant4 Molecular DNA simulation. https://geant4-dna.github.io/molecular-docs/
DOI: 10.5281/zenodo.6658888
title: Cubic seed fractals for Molecular-DNA

description: Files to accompany the Geant4 Molecular DNA simulation. https://geant4-dna.github.io/molecular-docs/
DOI: 10.6084/m9.figshare.19545112
title: datafile1.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 3(a-b).
DOI: 10.6084/m9.figshare.19545148
title: datafile2.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 3(c-e).
DOI: 10.6084/m9.figshare.19545112.v1
title: datafile1.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 3(a-b).
DOI: 10.6084/m9.figshare.19545160
title: datafile3.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 3(f).
DOI: 10.6084/m9.figshare.19545160.v1
title: datafile3.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 3(f).
DOI: 10.6084/m9.figshare.19545175
title: datafile4.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 5(a-b).
DOI: 10.6084/m9.figshare.19545148.v1
title: datafile2.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 3(c-e).
DOI: 10.6084/m9.figshare.19545175.v1
title: datafile4.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 5(a-b).
DOI: 10.6084/m9.figshare.19545193
title: datafile5.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 5(c-d).
DOI: 10.6084/m9.figshare.19545226
title: datafile7.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 7.
DOI: 10.6084/m9.figshare.19545238
title: datafile8.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 8.
DOI: 10.6084/m9.figshare.19545208.v1
title: datafile6.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 6.
DOI: 10.6084/m9.figshare.19545193.v1
title: datafile5.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 5(c-d).
DOI: 10.6084/m9.figshare.19545208
title: datafile6.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 6.
DOI: 10.6084/m9.figshare.19545250.v1
title: datafile9.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 9.
DOI: 10.6084/m9.figshare.19545286
title: datafile11.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 10(b).
DOI: 10.6084/m9.figshare.19545316
title: datafile12.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 11 (simulated data).
DOI: 10.6084/m9.figshare.19545226.v1
title: datafile7.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 7.
DOI: 10.6084/m9.figshare.19545250
title: datafile9.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 9.
DOI: 10.6084/m9.figshare.19545274
title: datafile10.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 10(a).
DOI: 10.6084/m9.figshare.19545274.v1
title: datafile10.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 10(a).
DOI: 10.6084/m9.figshare.19545286.v1
title: datafile11.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 10(b).
DOI: 10.6084/m9.figshare.19545238.v1
title: datafile8.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 8.
DOI: 10.6084/m9.figshare.19545316.v1
title: datafile12.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 11 (simulated data).
DOI: 10.6084/m9.figshare.19545340
title: datafile13.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 11 (optical phased measurement data).
DOI: 10.6084/m9.figshare.19545340.v1
title: datafile13.csv

description: This Data file supports the publication "Dynamic phase measurement of fast liquid crystal phase modulators". It is the undetlying data for Fig. 11 (optical phased measurement data).
DOI: 10.17632/6gxgfgdfwc.2
title: UV-C COVID

description: SARS-CoV2 TCID50/mL after irradiation with mercury lamp (Hg) or UV-C LED on plastic; tissue, paper, cardboard or steel.
TCID50/mL =0 replaced by value of 1 for graphic representation using a log axis
DOI: 10.17632/jxs697nt9g.1
title: Sarcopenia & Bariatric surgery

description: 28 obese women aged 40.5±9.8 yrs who underwent Roux-en-Y gastric bypass (RYGB) were included. 27 of them were reassessed after 6 months of surgery, and 16 completed the one-year follow-up. Pre-sarcopenia condition was assessed through handgrip strength test and body composition by dual-energy X-ray absorptiometry (DXA). Total body mass, body mass index and lean mass (LM) were collected prior to, 6 and 12 months after RYGB surgery.

All subjects reassessed after 12 months were diagnosed with pre-sarcopenia according to the FNIH classification criteria, while according to the EWGSOP2 Consensus they presented normal values. LM represented 14% of influence on handgrip strength (p=0.049) after 6 months of surgery; however, its influence on strength after 12 months increased to 30% (p=0.028). Therefore, the FNIH classification is the most effective criteria since it uses LM content as the first test, considering that strength capacity needs more time to be affected by the surgical procedure. On the other hand, the EWGSOP2 classification should not be applied to determine loss of LM in younger populations regardless of what may have caused such changes.
DOI: 10.6084/m9.figshare.20152202
title: shape_for_release.txt

description: The shape_for_release.txt file contains de-identified data with mtDNA-CN and additional covariates. The number at the end of each variable denotes when the variable was measured. For example, mtDNAcn_0 is mtDNA_CN measured at baseline (0 months), and mtDNAcn_6 is mtDNA-CN measured at study completion (6 months) <br>
DOI: 10.6084/m9.figshare.20152202.v1
title: shape_for_release.txt

description: The shape_for_release.txt file contains de-identified data with mtDNA-CN and additional covariates. The number at the end of each variable denotes when the variable was measured. For example, mtDNAcn_0 is mtDNA_CN measured at baseline (0 months), and mtDNAcn_6 is mtDNA-CN measured at study completion (6 months) <br>
DOI: 10.5519/qd.3u5tmly1
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5162717 records)
DOI: 10.5281/zenodo.6729070
title: Intraspecific variation and economics spectrum of Phragmites australis in lakeshore wetland of (semi-) arid regions

description: <em>Phragmites australis</em>, as a widely distributed species, has a high degree of intraspecific variation in functional traits and is able to respond to external climatic and environmental changes and adjust its adaptation strategies on time. The plant economic spectrum can reflect the adaptation strategies of resource acquisition and storage of plants in different climatic regions, and provide a scientific basis for understanding the ecological differentiation of plants in different habitats and their adaptation mechanisms. In this study, the morphological traits, nutrient contents and stoichiometric ratios of <em>P. australis</em> in lakes and lakeshore wetlands of semi-arid and arid climatic regions from east to west in Inner Mongolia Plateau were investigated to reveal the variability of plant functional traits at different regional scales and the influencing factors, and to reveal the ecological adaptation strategies of <em>P. australis</em> in different regions through plant economic spectrum. The results showed that soil moisture gradient, geographic location and regional scale effected the intraspecific variation of functional traits of <em>P. australis</em>. At the local scale, soil moisture gradients had opposite effects on the response to functional traits of <em>P. australis</em> in the arid and semi-arid regions. At the regional scale, climatic factors dominated the variation of reed functional traits across the latitudinal gradient, while the correlation with soil properties was not significant (<em>P</em> &gt; 0.05). Plant economic spectrum theory is also applicable to the functional traits of various organs and whole plants of <em>P. australis</em> populations at different regional scales, and the acquisition and assimilation of resources is conservative in arid regions, while in semi-arid regions it is an acquisition strategy. This study provides a new understanding of the ecological niche differentiation and drivers of plant populations and ecological adaptation strategies of species at the regional scale, and provides a theoretical basis for the restoration and reconstruction of degraded wetland ecosystems.
DOI: 10.5281/zenodo.6729069
title: Intraspecific variation and economics spectrum of Phragmites australis in lakeshore wetland of (semi-) arid regions

description: <em>Phragmites australis</em>, as a widely distributed species, has a high degree of intraspecific variation in functional traits and is able to respond to external climatic and environmental changes and adjust its adaptation strategies on time. The plant economic spectrum can reflect the adaptation strategies of resource acquisition and storage of plants in different climatic regions, and provide a scientific basis for understanding the ecological differentiation of plants in different habitats and their adaptation mechanisms. In this study, the morphological traits, nutrient contents and stoichiometric ratios of <em>P. australis</em> in lakes and lakeshore wetlands of semi-arid and arid climatic regions from east to west in Inner Mongolia Plateau were investigated to reveal the variability of plant functional traits at different regional scales and the influencing factors, and to reveal the ecological adaptation strategies of <em>P. australis</em> in different regions through plant economic spectrum. The results showed that soil moisture gradient, geographic location and regional scale effected the intraspecific variation of functional traits of <em>P. australis</em>. At the local scale, soil moisture gradients had opposite effects on the response to functional traits of <em>P. australis</em> in the arid and semi-arid regions. At the regional scale, climatic factors dominated the variation of reed functional traits across the latitudinal gradient, while the correlation with soil properties was not significant (<em>P</em> &gt; 0.05). Plant economic spectrum theory is also applicable to the functional traits of various organs and whole plants of <em>P. australis</em> populations at different regional scales, and the acquisition and assimilation of resources is conservative in arid regions, while in semi-arid regions it is an acquisition strategy. This study provides a new understanding of the ecological niche differentiation and drivers of plant populations and ecological adaptation strategies of species at the regional scale, and provides a theoretical basis for the restoration and reconstruction of degraded wetland ecosystems.
DOI: 10.5683/sp/triqel
title: Solidarity by Association: The Unionization of Faculty, Academic Librarians and Support Staff at Carleton University (1973-1976) Transcript [Finn]

description: Finn speaks about her involvement in the certification of the Carleton University Support Staff Association (CUSSA) in the mid 1970s. She was a key support staff union organizer from 1973 to 1976. She describes her working life at Carleton for support staff, particularly women in administrative and clerical jobs. She also discusses the trigger events that led to support for unionization, including the lay off of two employees in March 1975, as well as the challenges faced in overcoming opposition to unionization from support staff who did not believe that unions were appropriate for them, as white-collar workers. She describes the context in which this significant shift in occupational status took place, including the gendered the nature of the university workplace. In particular, she talks about working conditions for female support staff on campus and the status of women in general. Uncorrected transcript (approximately 30 p.) from the original audio tape. Number of sessions: 1 Length of interview: Approximately 2 hours. Place of interview: The foyer of her apartment building, Ottawa, Ontario Date of interview: March 22, 2011 Language of interview: English Name of transcriber: Teevi Mackay Date of transcription: July - August, 2011 Software used for transcription: NVivo (2017-01-11)
DOI: 10.18141/1787670
title: Datasets for IWG Report on Energy Communities

description: Datasets associated with figures in the Initial Report to the President on Empowering Workers Through Revitalizing Energy Communities (available at: https://netl.doe.gov/sites/default/files/2021-04/Initial%20Report%20on%20Energy%20Communities_Apr2021.pdf) from the Interagency Working Group for Coal and Power Plant Communities. These data align to the White House’s The Interagency Working Group on Coal and Power Plant Communities and Economic Revitalization, https://energycommunities.gov/