cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e118569v1
title: Two Computational Models for Analyzing Political Attention in Social Media

description: Using the Twitter Search API, we collected all tweets posted by official MC accounts (voting members only) during the 115th U.S. Congress which ran January 3, 2017 to January 3, 2019. We identified MCs' Twitter user names by combining the lists of MC social media accounts from the United States project (https://github.com/unitedstates/congress-legislators), George Washington Libraries (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/UIVHQR), and the Sunlight Foundation (https://sunlightlabs.github.io/congress/index.html\#legislator-spreadsheet). <br><br>Throughout 2017 and 2018, we used the Twitter API to search for the user names in this composite list and retrieved the accounts' most recent tweets. Our final search occurred on January 3, 2019, shortly after the 115th U.S. Congress ended. In all, we collected 1,485,834 original tweets (i.e., we excluded retweets) from 524 accounts. The accounts differ from the total size of Congress because we included tweet data for MCs who resigned (e.g., Ryan Zinke) and those who joined off cycle (e.g., Rep. Conor Lamb); we were also unable to confirm accounts for every state and district.<br>Twitter prohibits us from sharing the full tweet text, and so we have included tweet IDs when possible.<br><br><br>
DOI: 10.3886/e119828
title: Effect of Self-compassion and Mindfulness on Eudaimonic Well-being

description: Objectives: The aim of the study was to determine the effects of mindfulness and self-compassion on eudaimonic well-being of Indian university students. Only a few researchers have investigated the effects of self-compassion and mindfulness on eudaimonic well-being. Moreover, these effects have not been well understood among the Indian student population. Methods: It was hypothesized that a statistically significant correlation between self-compassion and eudaimonic well-being, and a statistically significant correlation between mindfulness and eudaimonic well-being would be obtained. The sample consisted of 133 students (Mage = 20.18; SD = 1.35) with 36.8% (n = 49) males and 63.2% (n = 84) females; 91.0% (n = 121) undergraduate students and 9.0% (n = 12) postgraduate students. Data was collected using the Questionnaire for Eudaimonic well-being (QEWB), the Mindful Attention Awareness Scale (MAAS), and the Self-compassion Scale (SCS). Multiple linear regression was used to analyse data. Results: Data analysis revealed a significantly strong relationship between the outcome variable eudaimonic well-being and the two predictor variables together, mindfulness and self-compassion (R = .54). The proportion of variance in the outcome variable which could be explained by the model (the predictor variables) was 28.6% (R2 = .286), suggesting that the model is a good predictor of the outcome variable. Conclusion: The study indicated that mindfulness and self-compassion are essential psychological and emotional resources which could prove beneficial in enhancing meaningful experiences and strengthening eudaimonia.
description: Response Rates: A representative sample of the university population was selected by inviting 170 participants from various undergraduate as well as post-graduate courses of Indian universities. An initial sample was obtained as 147 students volunteered to participate in the study pursuing either undergraduate or post-graduate degrees in the following courses: Life Sciences, Medical Sciences, Social Sciences, Law, Engineering, Pharmacy, Communication, Chartered Accountancy and Arts. 14 cases (9.52%) of the sample presenting missing values in the questionnaires were revealed after an initial review of data, therefore, they were excluded from the sample. The final number of participants equalled 133 students (Mage = 20.18; SD = 1.35) with 36.8% (n = 49) males and 63.2% (n = 84) females; 91.0% (n = 121) undergraduate students and 9.0% (n = 12) postgraduate students
description: Presence of Common Scales: 1.Questionnaire for Eudaimonic well-being (QEWB) (Waterman et al., 2010)<br>2. The Mindful Attention Awareness Scale (MAAS) by Brown and Ryan (2003)<br>3. The Self-compassion Scale (SCS) by Neff (2003)
description: mail questionnaire; on-site questionnaire; self-enumerated questionnaire;
description: University Students<br>18-25 years of age<br>Pursuing Undergraduate or Postgraduate courses<br>Nationality: Indian<br>Studying in universities in India.
description: Convenience sampling
DOI: 10.3886/e116171v2-24865
title: National Poll on Healthy Aging (NPHA), [United States], October 2017

description: By tapping into the perspectives of older adults and their caregivers, the University of Michigan National Poll on Healthy Aging (NPHA) helps inform the public, health care providers, policymakers, and advocates on issues related to health, health care and health policy affecting Americans 50 years of age and older. The poll is designed as a recurring, nationally representative household survey of U.S. adults, which allows assessment of issues in a timely fashion. Launched in spring 2017, the NPHA is modeled after the highly successful University of Michigan C.S. Mott Children's Hospital National Poll on Children's Health. The NPHA grew out of a strong interest in aging-related issues among many members of the University of Michigan Institute for Healthcare Policy and Innovation (IHPI), which brings together more than 600 faculty who study health, health care and the impacts of health policy. IHPI directs the poll which is sponsored by AARP and Michigan Medicine, the University of Michigan academic medical center.<br><br>This project represents the second wave or fielding of NPHA in October 2017. This is a new sample of participants and cannot be joined or merged with the data from NPHA Wave 1.
description: Three survey weights are included with the dataset, one for the full population of 50-80 year old respondents, one for those 50-64, and one for those ages 65-80. Weights should be applied to interpret population-level rates. See user guide for additional information about the weights.
description: Response Rates: The main survey completion rate for respondents ages 50-64 was 70%. The main survey completion rate for respondents ages 65-80 was 75%.
description: web-based survey; The data files listed in this study for NPHA wave 2 represent the same variables/content, but have been converted to a variety of file formats for user convenience. <br><br>NPHA Wave 1 can be found by visiting the study page, National Poll on Healthy Aging (NPHA), [United States], April 2017 (ICPSR 37305): https://doi.org/10.3886/ICPSR37305.v1.
description: Adults ages 50-80 living in non-institutionalized settings in the United States of America. Smallest Geographic Unit: Smallest geographic unit: State
description: Survey respondents were sampled from a nationally representative panel (KnowledgePanel from Ipsos, formerly GfK). See user guide for details about respondent selection and sampling.
DOI: 10.21227/yww1-q371
title: Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks

description: Non-contrast computed tomography (NCCT) is commonly used for volumetric follow-up assessment of ischemic strokes. However, manual lesion segmentation is time-consuming and subject to high inter-observer variability. The aim of this study was to develop and establish a baseline convolutional neural network (CNN) model for automatic NCCT lesion segmentation. A total of 252 multi center clinical NCCT datasets, acquired from 22 centers, and corresponding manual segmentations were used to train (204 datasets) and validate (48 datasets) a 3D multi scale CNN model for lesion segmentation. Post processing methods were implemented to improve the CNN-based lesion segmentations. The final CNN model and post processing method was evaluated using 39 out of distribution holdout test datasets, acquired at seven centers that did not contribute to the training or validation datasets. Each test image was segmented by two or three neuroradiologists. The Dice similarity coefficient (DSC) and predicted lesion volumes were used to evaluate the segmentations. The CNN model achieved a mean DSC score of 0.47 on the validation NCCT datasets. Post-processing significantly improved the DSC to 0.50 (P&lt;0.01). On the holdout test set, the CNN model achieved a mean DSC score of 0.42, which was also significantly improved to 0.45 (P&lt;0.05) by post processing. Importantly, the automatically segmented lesion volumes were not significantly different from the lesion volumes determined by the expert observers (P&gt;0.05) and showed excellent agreement with manual lesion segmentation volumes (intraclass correlation coefficient, ICC = 0.88). The proposed CNN model can automatically and reliably segment ischemic stroke lesions in clinical NCCT datasets. Post processing techniques can further improve accuracy. As the model was trained and evaluated on datasets from multiple centers, it is broadly applicable and is publicly available.
DOI: 10.21227/jzgr-r916
title: Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks

description: This dataset contains the trained model that accompanies the publication of the same name: Anup Tuladhar*, Serena Schimert*, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert, "Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 94871-94879, 2020, doi:10.1109/ACCESS.2020.2995632. *: Co-first authors Publication AbstractNon-contrast computed tomography (NCCT) is commonly used for volumetric follow-up assessment of ischemic strokes. However, manual lesion segmentation is time-consuming and subject to high inter-observer variability. The aim of this study was to develop and establish a baseline convolutional neural network (CNN) model for automatic NCCT lesion segmentation. A total of 252 multi center clinical NCCT datasets, acquired from 22 centers, and corresponding manual segmentations were used to train (204 datasets) and validate (48 datasets) a 3D multi scale CNN model for lesion segmentation. Post processing methods were implemented to improve the CNN-based lesion segmentations. The final CNN model and post processing method was evaluated using 39 out of distribution holdout test datasets, acquired at seven centers that did not contribute to the training or validation datasets. Each test image was segmented by two or three neuroradiologists. The Dice similarity coefficient (DSC) and predicted lesion volumes were used to evaluate the segmentations. The CNN model achieved a mean DSC score of 0.47 on the validation NCCT datasets. Post-processing significantly improved the DSC to 0.50 (P&lt;0.01). On the holdout test set, the CNN model achieved a mean DSC score of 0.42, which was also significantly improved to 0.45 (P&lt;0.05) by post processing. Importantly, the automatically segmented lesion volumes were not significantly different from the lesion volumes determined by the expert observers (P&gt;0.05) and showed excellent agreement with manual lesion segmentation volumes (intraclass correlation coefficient, ICC = 0.88). The proposed CNN model can automatically and reliably segment ischemic stroke lesions in clinical NCCT datasets. Post processing techniques can further improve accuracy. As the model was trained and evaluated on datasets from multiple centers, it is broadly applicable and is publicly available.
DOI: 10.21227/m1ga-0n60
title: Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks

description: This dataset contains the trained model that accompanies the publication of the same name: Anup Tuladhar*, Serena Schimert*, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert, "Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 94871-94879, 2020, doi:10.1109/ACCESS.2020.2995632. *: Co-first authors Publication AbstractNon-contrast computed tomography (NCCT) is commonly used for volumetric follow-up assessment of ischemic strokes. However, manual lesion segmentation is time-consuming and subject to high inter-observer variability. The aim of this study was to develop and establish a baseline convolutional neural network (CNN) model for automatic NCCT lesion segmentation. A total of 252 multi center clinical NCCT datasets, acquired from 22 centers, and corresponding manual segmentations were used to train (204 datasets) and validate (48 datasets) a 3D multi scale CNN model for lesion segmentation. Post processing methods were implemented to improve the CNN-based lesion segmentations. The final CNN model and post processing method was evaluated using 39 out of distribution holdout test datasets, acquired at seven centers that did not contribute to the training or validation datasets. Each test image was segmented by two or three neuroradiologists. The Dice similarity coefficient (DSC) and predicted lesion volumes were used to evaluate the segmentations. The CNN model achieved a mean DSC score of 0.47 on the validation NCCT datasets. Post-processing significantly improved the DSC to 0.50 (P&lt;0.01). On the holdout test set, the CNN model achieved a mean DSC score of 0.42, which was also significantly improved to 0.45 (P&lt;0.05) by post processing. Importantly, the automatically segmented lesion volumes were not significantly different from the lesion volumes determined by the expert observers (P&gt;0.05) and showed excellent agreement with manual lesion segmentation volumes (intraclass correlation coefficient, ICC = 0.88). The proposed CNN model can automatically and reliably segment ischemic stroke lesions in clinical NCCT datasets. Post processing techniques can further improve accuracy. As the model was trained and evaluated on datasets from multiple centers, it is broadly applicable and is publicly available.
DOI: 10.21227/ve4z-x857
title: Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks

description: This dataset contains the trained model that accompanies the publication of the same name: Anup Tuladhar*, Serena Schimert*, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert, "Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 94871-94879, 2020, doi:10.1109/ACCESS.2020.2995632. *: Co-first authors Publication AbstractNon-contrast computed tomography (NCCT) is commonly used for volumetric follow-up assessment of ischemic strokes. However, manual lesion segmentation is time-consuming and subject to high inter-observer variability. The aim of this study was to develop and establish a baseline convolutional neural network (CNN) model for automatic NCCT lesion segmentation. A total of 252 multi center clinical NCCT datasets, acquired from 22 centers, and corresponding manual segmentations were used to train (204 datasets) and validate (48 datasets) a 3D multi scale CNN model for lesion segmentation. Post processing methods were implemented to improve the CNN-based lesion segmentations. The final CNN model and post processing method was evaluated using 39 out of distribution holdout test datasets, acquired at seven centers that did not contribute to the training or validation datasets. Each test image was segmented by two or three neuroradiologists. The Dice similarity coefficient (DSC) and predicted lesion volumes were used to evaluate the segmentations. The CNN model achieved a mean DSC score of 0.47 on the validation NCCT datasets. Post-processing significantly improved the DSC to 0.50 (P&lt;0.01). On the holdout test set, the CNN model achieved a mean DSC score of 0.42, which was also significantly improved to 0.45 (P&lt;0.05) by post processing. Importantly, the automatically segmented lesion volumes were not significantly different from the lesion volumes determined by the expert observers (P&gt;0.05) and showed excellent agreement with manual lesion segmentation volumes (intraclass correlation coefficient, ICC = 0.88). The proposed CNN model can automatically and reliably segment ischemic stroke lesions in clinical NCCT datasets. Post processing techniques can further improve accuracy. As the model was trained and evaluated on datasets from multiple centers, it is broadly applicable and is publicly available.
DOI: 10.15139/s3/qsnend
title: Eswatini MEASURE CMIS Eval_clientexit2017

description: Baseline client exit survey was conducted as part of a case study evaluating the client management information system (CMIS) in Eswatini. The CMIS currently has out-patient service modules covering the main health programs including: family planning, anti-retroviral treatment (ART) for HIV positive patients, ante-natal care and prevention of mother-to-child transmission (ANC/PMTCT), tuberculosis, child welfare, and out-patient curative services. Given the potential benefits of the CMIS in Swaziland and the plan to implement it in all public facilities nationally, this provides an opportunity to conduct research on the rollout of the project focusing on efficiency gains, data availability and quality, service delivery, and health outcomes. This case study focuses on the ante-natal care (ANC) department.
DOI: 10.25625/3dvvvn/ubyzuc
title: DK-SorSoilModelData.RData

description: :unav
DOI: 10.18112/openneuro.ds001997.v1.0.0
title: CEREBRuM
DOI: 10.25550/1-711j
title: Activity of Altai neural crest enhancer in E11.5 transgenic mouse embryo, orthologous to human enhancer near RABGEF1.

description: Altai neural crest enhancer with reproducible activity in the frontonasal process, maxillary prominence of the 1st branchial arch, periocular mesenchyme, and cranial nerves.
DOI: 10.5525/gla.researchdata.827
title: Time Displacement Series of a Silica Bead Optically Trapped in Water

description: This is the time/displacement series of a 10 micron radius silica bead optically trapped in water at room temperature. Trap stiffness 2.5 micro-Newtons/metre. There are three columns of tab delimited data: time, x position and y position. Time is in seconds, position is in micrometers. This data is used in conjunction with Complex_Viscosity_from_PSD.m.
DOI: 10.20375/0000-000b-ca38-3
title: Wirkungsrte_Mitwirkende-Universitäts-Professoren.kml
DOI: 10.17025/1604992
title: Data Release and Project Closeout

description: A dataset containing procedures to: 1.) Make a dataset public 2.) Closeout a project on the Data Hub
DOI: 10.13012/b2idb-9614170_v1
title: Data for: How do moral values differ in tweets on social movements?

description: We are sharing the tweet IDs of four social movements: #BlackLivesMatter, #WhiteLivesMatter, #AllLivesMatter, and #BlueLivesMatter movements. The tweets are collected between May 1st, 2015 and May 30, 2017. We eliminated the location to the United States and focused on extracting the original tweets, excluding the retweets. Recommended citations for the data: Rezapour, R. (2019). Data for: How do Moral Values Differ in Tweets on Social Movements?. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9614170_V1 and Rezapour, R., Ferronato, P., and Diesner, J. (2019). How do moral values differ in tweets on social movements?. In 2019 Computer Supported Cooperative Work and Social Computing Companion Publication (CSCW’19 Companion), Austin, TX.
DOI: 10.21421/d2/jgiqhv
title: Micro level household survey data on employment for generation - 2 in Telangana state during 2001-2014
DOI: 10.25828/drdb-p626
title: Keiskammahoek Rural Survey 1948-1950
DOI: 10.15482/usda.adc/1518465
title: Map of Soil Erosion Risk for the Mancos Shale Formation

description: This product used the rangeland hydrology and erosion model (RHEM) to map erosion risks affecting water quality of the Colorado River that originate on the Mancos Shale formation in Utah, Colorado, New Mexico, and Arizona. The Mancos Shale is a significant source of salinity, and a portion of that salt load derives from erosion of rangeland soils. Inputs to RHEM included digital elevation data, statistically modeled maps of soil properties, the LANDFIRE vegetation map, Landsat and MODIS satellite imagery, field data from the Rangeland National Resource Inventory program of the U.S. Natural Resources Conservation Service, and rainfall data from Atlas 14 of the U.S. National Atmospheric and Oceanographic Administration. RHEM predicted sediment yield at a 30 m spatial resolution for a storm with a 60-minute duration whose intensity corresponded to a 25-year return frequency. Results corresponded reasonably with prior field experiments that used the Walnut Gulch Rainfall Simulator (WGRS), with a Spearman’s rank-order correlation of 0.76 for cumulative sediment yield after 20 minutes of rainfall. This high-resolution map of erosion risk developed from RHEM can help to prioritize specific areas for more intensive study and action. This dataset represents areas of the Mancos Shale geological formation that do not have high levels of urban or agricultural development and which have slopes less than 35%.
DOI: 10.25573/data.10042550.v3
title: Agua Salud, Celestino Tower_Net Radiation 1 of 3

description: Agua Salud Celestino Tower, Net RadiationLocation 9.21142N, 79.72656W<br>Parameters: <br> albedo (soil albedo), 0-1 (statistics calculated only for data between 7am and 5pm)<br> dnwlwr (down-welling long wave radiation), W/m2<br> dnwsr (down-welling short wave radiation), W/m2<br> netlwr (net long wave radiation), W/m2<br> netswr (net short wave radiation), W/m2<br> netrad (net radiation), W/m2<br> radt (radiometer temperature), C<br> rdnwlwr (raw down-welling long wave radiation), W/m2<br> rupwlwr (raw upwelling long wave radiation), W/m2<br> upwlwr (upwelling long wave radiation), W/m2<br> upwswr (upwelling short wave radiation), W/m2<br> soilt (soil temperature), C<br><br>Established in 2015 and is located at the eastern edge of the Soberania Nation Park, in an area of pastures and forest patches. The tower is 40ft (12.2m) tall
DOI: 10.26304/366-u1497d-salinity
title: Expedition 366 Hole U1497D Salinity data file

description: Hole U1497D SALINITY - Salinity for Expedition 366 Hole U1497D
DOI: 10.25810/5dxz-xm93.4
title: Latin American Newspaper Coverage of Climate Change or Global Warming, 2004-2018 - July 2018

description: The Media and Climate Change Observatory Data monitors 74 sources (across newspapers, radio and TV) in 38 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://sciencepolicy.colorado.edu/icecaps/research/media_coverage/index.html. These data include Excel (.xlsx), CSV (.csv), and plain-text README files (.txt).
DOI: 10.7483/opendata.cms.snnp.rgi6
title: /BJetPlusX/Run2012B-22Jan2013-v1/AOD
DOI: 10.18716/nmrshiftdb2/10886
title: NMR data for (3S,5S,6R,9R)-3-Hydroxy-5,6-epoxy--ionol-9-O--glucopyranoside; Phlomuroside
DOI: 10.5439/1492259
title: ARM: XSAPRCWRHI

description: xsapr