cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15131/shef.data.8247110.v1
title: Norway

description: The project in Norway was undertaken with a museum and a kindergarten.Participants included (a) a team of researchers, including five educational researchers with different experience and focus but sharing a concern with the design andunderstanding of technology-enhanced spaces for learning; (b) a team of three museum curators and educators who have a specific responsibility for developing science enquiry experiences for young children in the science museum; (c) and four kindergarten educators from a kindergarten in the Oslo area, along with a group of twenty kindergarten children between 5 and 6 years old. The participatory designprocess involved design meetings, as well as observations in the science museum and the kindergarten. In addition, interviews were conducted with museum and kindergarten educators during the process. The workshops were recorded using traditional zoom-lens cameras, as well as wide-angle action cameras, to ensure adequately capturing embodied interaction in both the design and the designed spaces. The team started by observing an existing activity in the museum. Then, they arranged a design meeting with museum staff where the goal was to design a makingactivity. After that, children were observed involved in arts-based activities in the kindergarten and engaging in a new making activity in the museum. Three exploratory design meetings with partners in the project were also held as part of the research study.
DOI: 10.6084/m9.figshare.8247050
title: Fine mapping using whole-genome sequencing sex determination in farmed Nile tilapia

description: Dataset for sex determination in the cultured Nile tilapia population
DOI: 10.6084/m9.figshare.8247017
title: Job Satisfaction among Nepalese nursing faculties

description: This is the data of the study that was done to evaluate the level of satisfaction of Nursing faculties working in different Nursing colleges in Nepal.
DOI: 10.6084/m9.figshare.8247050.v1
title: Fine mapping using whole-genome sequencing for sex determination in farmed Nile tilapia

description: Dataset for sex determination in the cultured Nile tilapia population
DOI: 10.6084/m9.figshare.8247002
title: Social Mobilization Action Consortium: Community Engagement data from the 2014-2016 Sierra Leone Ebola outbreak

description: The Social Mobilization Action Consortium (SMAC) was Sierra Leone’s largest coordinated community engagement initiative during the 2014 - 2016 Ebola outbreak. It worked in all 14 districts in Sierra Leone across &gt;12,000 communities (approximately 70% of all communities), through 2,466 trained Community Mobilizers, a network of 2,000 mosques and churches, and 42 local radio stations.<br><br>We present an extensive dataset of community engagement and monitoring with a focus on over 50,000 SMAC weekly reports collected by Community Mobilizers between December 2014 and September 2015. We share raw data transcribed from paper forms (starting in the Fall of 2014) and taken directly from digital reports (April 2015 and beyond).<br>Reference: Community Engagement during outbreak response: standards, approaches, and lessons from the 2014-2016 Ebola outbreak in Sierra Leone<br><br>Authors: Jamie Bedson, Mohamed F. Jalloh, Danielle Pedi, Saiku M. Bah, Katharine Owen, Allan Oniba, Musa Sangarie, James Fofanah, Mohamed B. Jalloh, Paul Sengeh, Laura A. Skrip, Benjamin M. Althouse, Laurent Hébert-Dufresne<br><br>Available at: https://www.biorxiv.org/content/10.1101/661959v1<br>
DOI: 10.6084/m9.figshare.8247050.v2
title: Fine mapping using whole-genome sequencing sex determination in farmed Nile tilapia

description: Dataset for sex determination in the cultured Nile tilapia population
DOI: 10.6084/m9.figshare.8246993
title: Rawdata_Compositionality.xlsx

description: This is a data set on chimpanzee multimodal communication (gesture + facial expressions).
DOI: 10.6084/m9.figshare.8247050.v4
title: Fine mapping using whole-genome sequencing sex determination in farmed Nile tilapia

description: Dataset for sex determination in the cultured Nile tilapia population
DOI: 10.6084/m9.figshare.8246993.v1
title: Rawdata_Compositionality.xlsx

description: This is a data set on chimpanzee multimodal communication (gesture + facial expressions).
DOI: 10.6084/m9.figshare.8247017.v1
title: Job Satisfaction among Nepalese nursing faculties

description: This is the data of the study that was done to evaluate the level of satisfaction of Nursing faculties working in different Nursing colleges in Nepal.
DOI: 10.6084/m9.figshare.8247050.v3
title: Fine mapping using whole-genome sequencing sex determination in farmed Nile tilapia

description: Dataset for sex determination in the cultured Nile tilapia population
DOI: 10.6084/m9.figshare.8247017.v2
title: Job Satisfaction among Nepalese nursing faculties

description: This is the data of the study that was done to evaluate the level of satisfaction of Nursing faculties working in different Nursing colleges in Nepal.
DOI: 10.6084/m9.figshare.8246900
title: BayesianAccountMemoryText_tar_parts

description: Code, data files, etc. for a research project on a Bayesian model of memory for text<br>I've made every effort to make this entire research project fully reproducible. Everything from the creation of the stimuli for the behavioural experiment, the sample size determination of the experiment, the creation of the BNC corpus for the topic modelling, the code the topic modelling, the computational modelling of the alternative (alternative to topic modelling) computational models, the post-processing of the computational models's data, the pre-processing of all the behavioural and other data prior to the final regression analyses, the final Bayesian regression analyses, and the final to-be-peer-reviewed and to-hopefully-be-published with all the tables, figures etc are all reproducible using the scripts and data in this repository.<br>The sub-directories here are:<br><i>data-analysis</i>: R, Jags, Stan/brms code for Bayesian multilevel logistic regression.<i>data-processing</i>: Python code for pre-processing experimental data and preparing computational models' predictions for use in the regression analyses.<i>make-corpus</i>: Mostly Python code for the creation of the BNC corpus to be used in the probabilistic topic modelling.<i>sample-size-determination</i>: Mostly Python (calling R code) code for determining sample size for the behavioural experiment.<i>stimuli-generation</i>: Mostly Python code for generating the to-be-memorized texts and the memory word lists in the behavioural experiments.<i>topic-modelling</i>: Probabilistic topic modelling using the Gustav topic modelling toolbox.<i>manuscript</i>: The RMarkdown and LaTeX manuscript <br><br>Clicking *Download All* will download a zip archive. Unzip this, and that should give you the following 4 large files.<br> * BayesianAccountMemoryText_tar_part_aa * BayesianAccountMemoryText_tar_part_ab * BayesianAccountMemoryText_tar_part_ac * BayesianAccountMemoryText_tar_part_ad<br>These are `split`ed parts of a large `.tar` archive. On a Unix/Linux system (including Mac OSX andpresumably with Windows if you use Cygwin or other options), they can be rejoined as follows.<br>cat BayesianAccountMemoryText_tar_part_* &gt; BayesianAccountMemoryText.tar<br><br>Now, `BayesianAccountMemoryText.tar` is the a `tar` archive and can be extracted as follows.<br>mkdir BayesianAccountMemoryTexttar xf BayesianAccountMemoryText.tar -C BayesianAccountMemoryText<br><br>
DOI: 10.6084/m9.figshare.8246879
title: Assessment of the effects of repeated doses of potassium iodide intake during pregnancy on male and female rat offspring using metabolomics and lipidomics

description: Preparedness for nuclear accident responsiveness includes interventions to protect pregnancies against prolonged exposure to radioactive iodine. The aim of this study was to investigate a new design consisting of repeated administration of potassium iodide (KI, 1 mg/kg) for 8 days in late pregnancy gestational day 9–16 (GD9–GD16) in rats. The later-life effects of this early-life iodine thyroid blocking (ITB) strategy were assessed in offspring two months afterbirth. Functional behavioral tests including forced swimming test (FST) and rotarod test (RRT) in rats of both genders showed lower FST performance in KI-treated females and lower RRT performance in KI-treated male pups. This performance decline was associated with metabolic disruptions in cortex involving amino acid metabolism, tyrosine metabolism, as well as docosahexaenoic acid (DHA) lipids and signaling lipids in males and females. Beyond these behavior-associated metabolic changes, a portion of the captured metabolome (17–25%) and lipidome (3.7–7.35%) remained sensitive to <i>in utero</i> KI prophylactic treatment in both cortex and plasma of post-weaning rats, with some gender-related variance. Only part of these disruptions was attributed to lower levels of TSH and T4 (males only). The KI-induced metabolic shifts involved a broad spectrum of functions encompassing metabolic and cell homeostasis and cell signaling functions. Irrespective Regardless of gender and tissues, the predominant effects of KI affected neurotransmitters, amino acid metabolism, and omega-3 DHA metabolism. Taken together, data demonstrated that repeated daily KI administration at 1 mg/kg/day for 8 days during late pregnancy failed to protect the mother-fetus against nuclear accident radiation. <b>Abbreviations:</b> CV-ANOVA: Cross-validation analysis of variance; DHA: Docosahexaenoic acid; FST: Forced swimming test; FT3: plasma free triiodothyronine; FT4: plasma free thyroxine; GD: Gestational day; ITB: Iodine thyroid blocking; KI: potassium iodide; LC/MS: Liquid chromatography coupled with mass spectrometry; MTBE: Methyl tert-butyl ether; m/z: mass-to-charge ratio; PLS-DA: Partial least squares–discriminant analysis; PRIODAC: Repeated stable iodide prophylaxis in accidental radioactive releases; RRT: Rotarod test; TSH: Thyroid-stimulating hormone; VIP: Variable importance in projection.
DOI: 10.6084/m9.figshare.8246867
title: High-resolution linkage and quantitative trait locus mapping using an interspecific cross between Argopecten irradians irradians (♀) and A. purpuratus (♂)

description: The full linkage map of <i>Argopecten irradians irradians </i>and <i>A. purpuratus. </i>The full sequences of all markers on the linkage maps. The R codes for QTL mapping.
DOI: 10.6084/m9.figshare.8246879.v1
title: Assessment of the effects of repeated doses of potassium iodide intake during pregnancy on male and female rat offspring using metabolomics and lipidomics

description: Preparedness for nuclear accident responsiveness includes interventions to protect pregnancies against prolonged exposure to radioactive iodine. The aim of this study was to investigate a new design consisting of repeated administration of potassium iodide (KI, 1 mg/kg) for 8 days in late pregnancy gestational day 9–16 (GD9–GD16) in rats. The later-life effects of this early-life iodine thyroid blocking (ITB) strategy were assessed in offspring two months afterbirth. Functional behavioral tests including forced swimming test (FST) and rotarod test (RRT) in rats of both genders showed lower FST performance in KI-treated females and lower RRT performance in KI-treated male pups. This performance decline was associated with metabolic disruptions in cortex involving amino acid metabolism, tyrosine metabolism, as well as docosahexaenoic acid (DHA) lipids and signaling lipids in males and females. Beyond these behavior-associated metabolic changes, a portion of the captured metabolome (17–25%) and lipidome (3.7–7.35%) remained sensitive to <i>in utero</i> KI prophylactic treatment in both cortex and plasma of post-weaning rats, with some gender-related variance. Only part of these disruptions was attributed to lower levels of TSH and T4 (males only). The KI-induced metabolic shifts involved a broad spectrum of functions encompassing metabolic and cell homeostasis and cell signaling functions. Irrespective Regardless of gender and tissues, the predominant effects of KI affected neurotransmitters, amino acid metabolism, and omega-3 DHA metabolism. Taken together, data demonstrated that repeated daily KI administration at 1 mg/kg/day for 8 days during late pregnancy failed to protect the mother-fetus against nuclear accident radiation. <b>Abbreviations:</b> CV-ANOVA: Cross-validation analysis of variance; DHA: Docosahexaenoic acid; FST: Forced swimming test; FT3: plasma free triiodothyronine; FT4: plasma free thyroxine; GD: Gestational day; ITB: Iodine thyroid blocking; KI: potassium iodide; LC/MS: Liquid chromatography coupled with mass spectrometry; MTBE: Methyl tert-butyl ether; m/z: mass-to-charge ratio; PLS-DA: Partial least squares–discriminant analysis; PRIODAC: Repeated stable iodide prophylaxis in accidental radioactive releases; RRT: Rotarod test; TSH: Thyroid-stimulating hormone; VIP: Variable importance in projection.
DOI: 10.6084/m9.figshare.8246900.v1
title: BayesianAccountMemoryText_tar_partad

description: # Code, data files, etc. for a research project on a Bayesian model of memory for text<br>I've made every effort to make this entire research project fully reproducible. Everything from the creation of the stimuli for the behavioural experiment, the sample size determination of the experiment, the creation of the BNC corpus for the topic modelling, the code the topic modelling, the computational modelling of the alternative (alternative to topic modelling) computational models, the post-processing of the computational models's data, the pre-processing of all the behavioural and other data prior to the final regression analyses, the final Bayesian regression analyses, and the final to-be-peer-reviewed and to-hopefully-be-published with all the tables, figures etc are all reproducible using the scripts and data in this repository.<br>The sub-directories here are:<br>* data-analysis: R, Jags, Stan/brms code for Bayesian multilevel logistic regression.* data-processing: Python code for pre-processing experimental data and preparing computational models' predictions for use in the regression analyses.* make-corpus: Mostly Python code for the creation of the BNC corpus to be used in the probabilistic topic modelling.* sample-size-determination: Mostly Python (calling R code) code for determining sample size for the behavioural experiment.* stimuli-generation: Mostly Python code for generating the to-be-memorized texts and the memory word lists in the behavioural experiments.* topic-modelling: Probabilistic topic modelling using the Gustav topic modelling toolbox.* manuscript: The RMarkdown and LaTeX manuscript
DOI: 10.6084/m9.figshare.8246864
title: Broadband Norway

description: eye-tracking data
DOI: 10.6084/m9.figshare.8246867.v2
title: High-resolution linkage and quantitative trait locus mapping using an interspecific cross between Argopecten irradians irradians (♀) and A. purpuratus (♂)

description: The full linkage map of <i>Argopecten irradians irradians </i>and <i>A. purpuratus. </i>The full sequences of all markers on the linkage maps. The R codes for QTL mapping.
DOI: 10.6084/m9.figshare.8246900.v3
title: BayesianAccountMemoryText_tar_parts

description: Code, data files, etc. for a research project on a Bayesian model of memory for text<br>I've made every effort to make this entire research project fully reproducible. Everything from the creation of the stimuli for the behavioural experiment, the sample size determination of the experiment, the creation of the BNC corpus for the topic modelling, the code the topic modelling, the computational modelling of the alternative (alternative to topic modelling) computational models, the post-processing of the computational models's data, the pre-processing of all the behavioural and other data prior to the final regression analyses, the final Bayesian regression analyses, and the final to-be-peer-reviewed and to-hopefully-be-published with all the tables, figures etc are all reproducible using the scripts and data in this repository.<br>The sub-directories here are:<br><i>data-analysis</i>: R, Jags, Stan/brms code for Bayesian multilevel logistic regression.<i>data-processing</i>: Python code for pre-processing experimental data and preparing computational models' predictions for use in the regression analyses.<i>make-corpus</i>: Mostly Python code for the creation of the BNC corpus to be used in the probabilistic topic modelling.<i>sample-size-determination</i>: Mostly Python (calling R code) code for determining sample size for the behavioural experiment.<i>stimuli-generation</i>: Mostly Python code for generating the to-be-memorized texts and the memory word lists in the behavioural experiments.<i>topic-modelling</i>: Probabilistic topic modelling using the Gustav topic modelling toolbox.<i>manuscript</i>: The RMarkdown and LaTeX manuscript <br><br>Clicking *Download All* will download a zip archive. Unzip this, and that should give you the following 4 large files.<br> * BayesianAccountMemoryText_tar_part_aa * BayesianAccountMemoryText_tar_part_ab * BayesianAccountMemoryText_tar_part_ac * BayesianAccountMemoryText_tar_part_ad<br>These are `split`ed parts of a large `.tar` archive. On a Unix/Linux system (including Mac OSX andpresumably with Windows if you use Cygwin or other options), they can be rejoined as follows.<br>cat BayesianAccountMemoryText_tar_part_* &gt; BayesianAccountMemoryText.tar<br><br>Now, `BayesianAccountMemoryText.tar` is the a `tar` archive and can be extracted as follows.<br>mkdir BayesianAccountMemoryTexttar xf BayesianAccountMemoryText.tar -C BayesianAccountMemoryText<br><br>
DOI: 10.6084/m9.figshare.8246867.v1
title: High-resolution linkage and quantitative trait locus mapping using an interspecific cross between Argopecten irradians irradians (♀) and A. purpuratus (♂)

description: The full linkage map of <i>Argopecten irradians irradians </i>and <i>A. purpuratus. </i>The full sequences of all markers on the linkage maps. The R codes for QTL mapping.
DOI: 10.6084/m9.figshare.8246900.v2
title: BayesianAccountMemoryText_tar_parts

description: Code, data files, etc. for a research project on a Bayesian model of memory for text<br>I've made every effort to make this entire research project fully reproducible. Everything from the creation of the stimuli for the behavioural experiment, the sample size determination of the experiment, the creation of the BNC corpus for the topic modelling, the code the topic modelling, the computational modelling of the alternative (alternative to topic modelling) computational models, the post-processing of the computational models's data, the pre-processing of all the behavioural and other data prior to the final regression analyses, the final Bayesian regression analyses, and the final to-be-peer-reviewed and to-hopefully-be-published with all the tables, figures etc are all reproducible using the scripts and data in this repository.<br>The sub-directories here are:<br><i>data-analysis</i>: R, Jags, Stan/brms code for Bayesian multilevel logistic regression.<i>data-processing</i>: Python code for pre-processing experimental data and preparing computational models' predictions for use in the regression analyses.<i>make-corpus</i>: Mostly Python code for the creation of the BNC corpus to be used in the probabilistic topic modelling.<i>sample-size-determination</i>: Mostly Python (calling R code) code for determining sample size for the behavioural experiment.<i>stimuli-generation</i>: Mostly Python code for generating the to-be-memorized texts and the memory word lists in the behavioural experiments.<i>topic-modelling</i>: Probabilistic topic modelling using the Gustav topic modelling toolbox.<i>manuscript</i>: The RMarkdown and LaTeX manuscript
DOI: 10.6084/m9.figshare.8246846
title: Glutelin and prolamin in rice grains

description: Glutelin and prolamin in grains of four rice cultivars
DOI: 10.6084/m9.figshare.8246843
title: Glutelin and prolamin in rice grains

description: Glutelin and prolamin in grains of four rice cultivars
DOI: 10.6084/m9.figshare.8246843.v1
title: Glutelin and prolamin in rice grains

description: Glutelin and prolamin in grains of four rice cultivars
DOI: 10.6084/m9.figshare.8246846.v1
title: Glutelin and prolamin in rice grains

description: Glutelin and prolamin in grains of four rice cultivars
DOI: 10.6084/m9.figshare.8246837
title: Glutelin and prolamin accumulation in rice grains.xlsx

description: Glutelin and prolamin in grains of four rice cultivars
DOI: 10.6084/m9.figshare.8246837.v2
title: Glutelin and prolamin accumulation in rice grains.xlsx

description: Glutelin and prolamin in grains of four rice cultivars
DOI: 10.6084/m9.figshare.8246837.v1
title: Glutelin and prolamin accumulation in rice grains.xlsx

description: Glutelin and prolamin in grains of four rice cultivars
DOI: 10.6084/m9.figshare.8246780
title: Sleep and health behaviors among diabetic and non-diabetic groups

description: Data on sleep and health behaviors among diabetic and non diabetic groups
DOI: 10.6084/m9.figshare.8246780.v2
title: Sleep and health behaviors among diabetic and non-diabetic groups

description: Data on sleep and health behaviors among diabetic and non diabetic groups
DOI: 10.6084/m9.figshare.8246780.v1
title: Sleep and health behaviors among diabetic and non-diabetic groups

description: Data on sleep and health behaviors among diabetic and non diabetic groups
DOI: 10.6084/m9.figshare.8246699
title: National Geographic Data Visualization Challenge

description: TrashVisualization.R<br>R code that merges and analyzes all of the data. <br>SizesOfObjects:<br>Table of sizes of objects we compare in the VR. <br>WPP2017_POP_F01_1_TOT:<br>United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, DVD Edition.<br><br>Population:<br>Cleaned population data from UN data set above taking only 2015.<br>1260352_SupportingFile:<br>Jambeck JR, Geyer R, Wilcox C, Siegler TR, Perryman M, Andrady A, et al. Marine pollution. Plastic waste inputs from land into the ocean. Science. 2015 Feb 13;347(6223):768–71.<br>DetailedSummary-Earth (+1-2):Coastal Cleanup Day Data from 2016-2018<br> https://www.coastalcleanupdata.org/<br><br>WCD:<br>World Cleanup Day Data for 2018https://www.letsdoitworld.org/wp-content/uploads/2019/01/WCD_2018_Waste_Report_FINAL_26.01.2019.pdf<br><br>Anything with the word "Key":<br>A key used for merging country names between data sets.<b> </b>
DOI: 10.6084/m9.figshare.8246540
title: Additional file 1: of Current phototherapy practice on Java, Indonesia

description: Intensity data of all phototherapy devices from participating hospitals. (XLSX 86 kb)
DOI: 10.6084/m9.figshare.8246540.v1
title: Additional file 1: of Current phototherapy practice on Java, Indonesia

description: Intensity data of all phototherapy devices from participating hospitals. (XLSX 86 kb)
DOI: 10.6084/m9.figshare.8246501
title: CCDB_7298.jpg

description: Coronal mouse brain slice stained with Fluoromyelin(green), DiI(red), and TO-PRO3 which label myelin, blood vessels, and cells respectively
DOI: 10.6084/m9.figshare.8246501.v1
title: CCDB_7298.jpg

description: Coronal mouse brain slice stained with Fluoromyelin(green), DiI(red), and TO-PRO3 which label myelin, blood vessels, and cells respectively
DOI: 10.6084/m9.figshare.8246465
title: Questionnaire for study on ADMA levels in Sudanese with Essential Hypertension

description: This is a Questionnaire which was used by a study to look for association between ADMA levels and Essential Hypertension in the Sudanese population . The questionnaire is about demographic info. also about presence of hypertension, family history, duration of disease, medication , other co-morbidities and other risk factors like smoking and alcohol
DOI: 10.6084/m9.figshare.8246345
title: Data for figures

description: data for figures
DOI: 10.6084/m9.figshare.8246345.v1
title: Data for figures

description: data for figures