cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14368673
title: HPDmobile H5 Image Data - hub RS4

description: Images from hub RS4 in one home, collected over 15 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368679
title: HPDmobile H5 Image Data - hub RS2

description: Images from hub RS2 in one home, collected over 15 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368661.v1
title: HPDmobile H5 Audio Data

description: Processed audio data, collected over 15 days, from five locations inside the home. Each ten second audio file was mean shifted, full wave rectified, and down-sampled, to remove identifying speech. The results are stored as csv files, by hub and by day.
DOI: 10.6084/m9.figshare.14368676
title: HPDmobile H5 Image Data - hub RS3

description: Images from hub RS3 in one home, collected over 15 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368658.v1
title: HPDmobile H5 Ground Truth Data

description: Ground truth whole-house occupancy information from the home, collected over 15 days. Contains one csv file for each day. "occupied" gives the binary status of the home (0=vacant, 1=occupied), and "number" gives the estimated number of occupants at the time.
DOI: 10.6084/m9.figshare.14368676.v1
title: HPDmobile H5 Image Data - hub RS3

description: Images from hub RS3 in one home, collected over 15 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368682
title: HPDmobile H5 Image Data - hub RS1

description: Images from hub RS1 in one home, collected over 15 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368685
title: HPDmobile H5 Dark Image Lists

description: This zip contains lists of images that were captured, but not included in the data set, because they were very dark (defined as having an average pixel value of less than 10). Lists are separated by hub and by day and stored in csv format.
DOI: 10.6084/m9.figshare.14368694
title: HPDmobile H6 Audio Data

description: Processed audio data, collected over 38 days, from four locations inside the home. Each ten second audio file was mean shifted, full wave rectified, and down-sampled, to remove identifying speech. The results are stored as csv files, by hub and by day.
DOI: 10.6084/m9.figshare.14368673.v1
title: HPDmobile H5 Image Data - hub RS4

description: Images from hub RS4 in one home, collected over 15 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368691
title: HPDmobile H6 Image Data - hub BS5

description: Images from hub BS5 in one home, collected over 38 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368679.v1
title: HPDmobile H5 Image Data - hub RS2

description: Images from hub RS2 in one home, collected over 15 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368682.v1
title: HPDmobile H5 Image Data - hub RS1

description: Images from hub RS1 in one home, collected over 15 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368691.v1
title: HPDmobile H6 Image Data - hub BS5

description: Images from hub BS5 in one home, collected over 38 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368697
title: HPDmobile H6 Dark Image Lists

description: This zip contains lists of images that were captured, but not included in the data set, because they were very dark (defined as having an average pixel value of less than 10). Lists are separated by hub and by day and stored in csv format.
DOI: 10.6084/m9.figshare.14368700
title: HPDmobile H6 Image Zone Labels

description: Contains files for each hub and day with binary zone based occupancy, indicating if a human is present in the image (1) or not (0). These were automatically generated with the yoloV5 object detection algorithm (https://github.com/ultralytics/yolov5), and a subset was manually verified by a human. Overall for the home, the probability of an image labeled as occupied actually containing a human is 90%, and the probability of an image labeled as vacant not containing a person is 97%, based on the validated samples. Individual probabilities by hub vary from the whole house level, and can be found in the meta data file.
DOI: 10.6084/m9.figshare.14368706
title: HPDmobile H6 Ground Truth Data

description: Ground truth whole-house occupancy information from the home, collected over 38 days. Contains one csv file for each day. "occupied" gives the binary status of the home (0=vacant, 1=occupied), and "number" gives the estimated number of occupants at the time.
DOI: 10.6084/m9.figshare.14368685.v1
title: HPDmobile H5 Dark Image Lists

description: This zip contains lists of images that were captured, but not included in the data set, because they were very dark (defined as having an average pixel value of less than 10). Lists are separated by hub and by day and stored in csv format.
DOI: 10.6084/m9.figshare.14368703
title: HPDmobile H6 Environmental Data

description: Environmental data readings: temperature (degree C), relative humidity (rH%), indoor CO2 equivalent (ppm), total volatile organic compounds (ppb), illuminance (lux). Readings were collected every ten seconds from four different common areas inside the single family residence, for a period of 38 days. This zip folder contains csv files for each of the four hubs, for every day.
DOI: 10.6084/m9.figshare.14368709
title: HPDmobile H6 Image Data - hub BS4

description: Images from hub BS4 in one home, collected over 38 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368712
title: HPDmobile H6 Image Data - hub BS3

description: Images from hub BS3 in one home, collected over 38 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368715
title: HPDmobile H6 Image Data - hub BS2

description: Images from hub BS2 in one home, collected over 38 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368700.v1
title: HPDmobile H6 Image Zone Labels

description: Contains files for each hub and day with binary zone based occupancy, indicating if a human is present in the image (1) or not (0). These were automatically generated with the yoloV5 object detection algorithm (https://github.com/ultralytics/yolov5), and a subset was manually verified by a human. Overall for the home, the probability of an image labeled as occupied actually containing a human is 90%, and the probability of an image labeled as vacant not containing a person is 97%, based on the validated samples. Individual probabilities by hub vary from the whole house level, and can be found in the meta data file.
DOI: 10.6084/m9.figshare.14368706.v1
title: HPDmobile H6 Ground Truth Data

description: Ground truth whole-house occupancy information from the home, collected over 38 days. Contains one csv file for each day. "occupied" gives the binary status of the home (0=vacant, 1=occupied), and "number" gives the estimated number of occupants at the time.
DOI: 10.6084/m9.figshare.14368802
title: HPDmobile Metadata

description: Additional information about the homes and data collected.
DOI: 10.6084/m9.figshare.14368709.v1
title: HPDmobile H6 Image Data - hub BS4

description: Images from hub BS4 in one home, collected over 38 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368712.v1
title: HPDmobile H6 Image Data - hub BS3

description: Images from hub BS3 in one home, collected over 38 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.6084/m9.figshare.14368715.v1
title: HPDmobile H6 Image Data - hub BS2

description: Images from hub BS2 in one home, collected over 38 days. Images are grayscale, 32-by-32 pixels, and were taken every second.
DOI: 10.5281/zenodo.3865860
title: BIRAFFE2: The 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems

description: This is our 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (<strong>BIRAFFE2</strong>). It is a dataset consisting of <em><strong>electrocardiogram (ECG)</strong></em>, <em><strong>galvanic skin response (GSR)</strong></em>, changes in <em><strong>facial expression</strong></em> signals and <em><strong>hand movements</strong></em> (represented by gamepad's accelerometer and gyroscope) recorded during affect elicitation by means of <em><strong>audio-visual stimuli</strong></em> (from IADS and IAPS databases) and our proof-of-concept three-level <em><strong>emotion evoking game</strong></em>. All the signals were captured using portable and low-cost equipment: BITalino (r)evolution kit for ECG and GSR and Creative Live! web camera for face photos (further analyzed by MS Face API). Besides the signals, the dataset consists also of <em><strong>subjects' self-assessment</strong></em> of their affective state after each stimuli (in the <em><strong>valence and arousal dimensions</strong></em>), <em><strong>"Big Five" personality traits</strong></em> assessment (using NEO-FFI inventory), and <em><strong>game involvement</strong></em>-related metrics (using GEQ questionnaire). For detailed description see <em>BIRAFFE2-HAIW2020-paper.pdf</em>.<br> For preview of the files before downloading the whole dataset see <em>sample-SUB211-[...]</em> files. All documents and papers that report on research that uses the BIRAFFE dataset should acknowledge this by <strong>citing the paper</strong>:<br> Krzysztof Kutt, Dominika Drążyk, Maciej Szelążek, Szymon Bobek and Grzegorz J. Nalepa (2020). The BIRAFFE2 Experiment. Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems. <em>HAIW2020: Human-AI Interaction Workshop</em>. Submitted, in review.<br> <em>(reference will be updated when the paper will be finally published)</em>
description: The BIRAFFE dataset is part of the work carried out by the AfCAI Research Group in Kraków, Poland. If you want to know more about us, see: https://afcai.re/
description: {"references": ["Krzysztof Kutt, Dominika Dr\u0105\u017cyk, Maciej Szel\u0105\u017cek, Szymon Bobek and Grzegorz J. Nalepa (2020). \"The BIRAFFE2 Experiment. Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems\". HAIW2020: Human-AI Interaction Workshop. arXiv:2007.15048"]}
DOI: 10.6084/m9.figshare.16843834
title: Supplementary Material for: Diabetes As an Independent Risk Factor for Stroke Recurrence in Ischemic Stroke Patients: An Updated Meta-Analysis

description: <b><i>Introduction:</i></b> Stroke and its recurrence and diabetes will increase in incidence as the population ages globally. This study explores the relationship between diabetes and stroke recurrence to understand if diabetes is an independent predictor for stroke recurrence in ischemic stroke (IS) patients. <b><i>Methods:</i></b> We conducted a systematic review and meta-analysis of studies on the effect of diabetes on stroke recurrence among patients with IS. We searched population-based studies published before 15th February 2021 in PubMed and EMBASE following PRISMA guidelines. Random-effects estimates of the pooled hazard ratio (HR) and 95% confidence intervals (CIs) of each study were generated. A funnel plot and an Egger test were performed to evaluate publication bias. All statistical analyses were conducted in the R software 4.0.1 and Stata 16.0. <b><i>Results:</i></b> The search identified 3,121 citations, of which 27 studies met inclusion criteria. Diabetes was associated with a significant risk of stroke recurrence in all IS patients (pooled HR, 1.50; 95% CI: 1.36–1.65; <i>I</i><sup>2</sup> = 61.0%). Similar results were found in lacunar stroke patients with diabetes (pooled HR, 1.65; 95% CI: 1.41–1.92; <i>I</i><sup>2</sup> = 22.0%). Moreover, we found that the risk of recurrent IS among patients of IS with diabetes was higher than that in those without diabetes (pooled HR, 1.53; 95% CI: 1.30–1.81; <i>I</i><sup>2</sup> = 74.0%). <b><i>Conclusion:</i></b> Diabetes is an independent risk factor for stroke recurrence among patients with IS.
DOI: 10.6084/m9.figshare.13341686
title: DEseq2 comparisons between Gli1-het and Gli1-null neural stem cells upon demyelination

description: Tables with data including RNA quality, barcode lane statistics, RNA read counts, alignment statistics, DEseq2 expression counts and the data comparison between the groups
DOI: 10.6084/m9.figshare.13341686.v1
title: DEseq2 comparisons between Gli1-het and Gli1-null neural stem cells upon demyelination

description: Tables with data including RNA quality, barcode lane statistics, RNA read counts, alignment statistics, DEseq2 expression counts and the data comparison between the groups
DOI: 10.6084/m9.figshare.15060714.v1
title: Codes for TopHat, HTSeqCount, DESeq2 and Plotly 3-D PCA

description: The codes used for aligning the RNAseq reads to the mm10 mouse reference genome with TopHat v2.1.1, quantifying the read counts using HTSeqCount, comparison of the gene expression using DESeq2, and PCA analysis using Plotly 3-D PCA.
DOI: 10.6084/m9.figshare.15060714
title: Codes for TopHat, HTSeqCount, DESeq2 and Plotly 3-D PCA

description: The codes used for aligning the RNAseq reads to the mm10 mouse reference genome with TopHat v2.1.1, quantifying the read counts using HTSeqCount, comparison of the gene expression using DESeq2, and PCA analysis using Plotly 3-D PCA.
DOI: 10.5281/zenodo.5579575
title: C3D Motion Capture Data of Hand Movements

description: This C3D motion capture data supplements the article <em>"Electromyography-Based Validation of a Musculoskeletal Hand Model"</em> from Melzner and Engelhardt et al. (2021) published at the Journal of Biomechanical Engineering (https://doi.org/10.1115/1.4052115). The following manuscript describes the exact experimental set-up of the data acquisition as well as the gained motion capture data provided as C3D-files. If you are using the provided dataset, please reference the corresponding paper in the Journal of Biomechanical Engineering.
DOI: 10.5281/zenodo.5579574
title: C3D Motion Capture Data of Hand Movements

description: This C3D motion capture data supplements the article <em>"Electromyography-Based Validation of a Musculoskeletal Hand Model"</em> from Melzner and Engelhardt et al. (2021) published at the Journal of Biomechanical Engineering (https://doi.org/10.1115/1.4052115). The following manuscript describes the exact experimental set-up of the data acquisition as well as the gained motion capture data provided as C3D-files. If you are using the provided dataset, please reference the corresponding paper in the Journal of Biomechanical Engineering.
DOI: 10.25402/cer.16844122
title: Supplemental Material: Antibacterial data synthesis challenges: a systematic review of treatments for complicated gram-negative urinary tract infections.docx

description: Table S1. MEDLINE and EMBASE search strings. Table S2. COCHRANE LIBRARY (CENTRAL and CDSR) search strings. Table S3. PICOTS eligibility criteria for the systematic literature review. Table S4. Risk of bias assessment using the Cochrane Collaboration Tool. Table S5. Patient baseline characteristics. Figure S1. PRISMA flow diagram.
DOI: 10.25402/cer.16844122.v1
title: Supplemental Material: Antibacterial data synthesis challenges: a systematic review of treatments for complicated gram-negative urinary tract infections.docx

description: Table S1. MEDLINE and EMBASE search strings. Table S2. COCHRANE LIBRARY (CENTRAL and CDSR) search strings. Table S3. PICOTS eligibility criteria for the systematic literature review. Table S4. Risk of bias assessment using the Cochrane Collaboration Tool. Table S5. Patient baseline characteristics. Figure S1. PRISMA flow diagram.
DOI: 10.5519/qd.2rvxunem
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.25646/9166
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.6084/m9.figshare.16844653
title: Supplementary Material for: Development and External Validation of a Model for Predicting Sufficient Filter Lifespan in Anticoagulation-Free Continuous Renal Replacement Therapy Patients

description: <b><i>Background:</i></b> Anticoagulation-free continuous renal replacement therapy (CRRT) was recommended by the current clinical guideline for patients with increased bleeding risk and contraindications of citrate. Nevertheless, anticoagulation-free CRRT yielded heterogeneous filter lifespan. Furthermore, the specific cutoff values for traditional coagulation parameters to predict sufficient filter lifespan of anticoagulation-free CRRT have not yet been determined. The purpose of our present study was to develop and validate a model for predicting sufficient filter lifespan in anticoagulation-free CRRT patients. <b><i>Methods:</i></b> Patients who underwent anticoagulation-free CRRT in our center between June 2013 and June 2019 were retrospectively included. The primary outcome was sufficient filter lifespan (≥24 h). Thirty-seven predictors were included for modeling based on their clinical significance and previous reports. The final model was developed by using multivariable logistic regression analysis and was validated in a separate external cohort. <b><i>Results:</i></b> The development cohort included 170 patients. Sufficient filter lifespan was observed in 80 patients. Thirteen variables were independent predictors for sufficient filter lifespan by logistic regression: body temperature, mean arterial pressure, activated partial thromboplastin time, direct bilirubin, alkaline phosphatase, blood urea nitrogen, vasopressor use, body mass index, white blood cell, platelet count, D-dimer, uric acid, and pH. The area under the curve (AUC) of the stepwise model and internal validation model was 0.82 (95% confidence interval [CI] [0.76–0.88]) and 0.8 (95% CI [0.74–0.87]), respectively. The positive predictive value and the negative predictive value of the stepwise model were 0.77 and 0.79, respectively. The validation cohort included 44 eligible patients and the AUC of the external validation model was 0.82 (95% CI [0.69–0.96]). <b><i>Conclusions:</i></b> The use of a prediction model instead of an assessment based only on coagulation parameters could facilitate the identification of the patients with filter lifespan of ≥24 h when they accepted anticoagulation-free CRRT.
DOI: 10.5281/zenodo.5589437
title: Diatom responses to the extreme 2015/2016 El Niño in a changing Northern Antarctic Peninsula: phytoplankton chemotaxonomic dataset

description: This dataset contains the results of phytoplankton chemotaxonomic groups derived from the HPLC/CHEMTAX analysis. The data were collected during the late spring and mid-late summer (November, January and February, respectively) of 2015/2016 along the Northern Antarctic Peninsula. This dataset includes an additional content in respect to diatom biomass, collected between 2008-2017 during the late summer (February) along the Northern Antarctic Peninsula.
DOI: 10.5281/zenodo.5589436
title: Diatom responses to the extreme 2015/2016 El Niño in a changing Northern Antarctic Peninsula: phytoplankton chemotaxonomic dataset

description: This dataset contains the results of phytoplankton chemotaxonomic groups derived from the HPLC/CHEMTAX analysis. The data were collected during the late spring and mid-late summer (November, January and February, respectively) of 2015/2016 along the Northern Antarctic Peninsula. This dataset includes an additional content in respect to diatom biomass, collected between 2008-2017 during the late summer (February) along the Northern Antarctic Peninsula.
DOI: 10.5281/zenodo.1485362
title: Impact of algorithm choice in morphological phylogenetic analysis with inapplicable data

description: Files to accompany Brazeau <em>et al</em>. (2019), describing the impact of using our new algorithm for handling inapplicable data. Details of the datasets analysed are give at ms609.github.io/ExploreInapplicable/r_dataset_details.html
description: {"references": ["Brazeau, M. D., T. Guillerme, and M. R. Smith. 2019. An algorithm for morphological phylogenetic analysis with inapplicable data. Systematic Biology, Accepted."]}
DOI: 10.5281/zenodo.1485361
title: Impact of algorithm choice in morphological phylogenetic analysis with inapplicable data

description: Files to accompany Brazeau <em>et al</em>. (2019), describing the impact of using our new algorithm for handling inapplicable data. Details of the datasets analysed are give at ms609.github.io/ExploreInapplicable/r_dataset_details.html
description: {"references": ["Brazeau, M. D., T. Guillerme, and M. R. Smith. 2019. An algorithm for morphological phylogenetic analysis with inapplicable data. Systematic Biology, Accepted."]}
DOI: 10.5281/zenodo.5584517
title: Dataset: Selection of high-trait emotivity affects the volume of sensory and emotional-related brain regions in male Japanese quails

description: Data used to produce the manuscript, <em>Selection of high-trait emotivity affects the volume of sensory and emotional-related brain regions in male Japanese quails.</em> A preprint can be found here: 10.5281/zenodo.5585278. The template and atlas created in the work can found here: 10.5281/zenodo.4700522. The <em>MRI.zip</em> file contains 20 anatomical MRI images of the quail brain and their atlas-based segmentations. If these segmentations are viewed with ITK-Snap then the <em>siwiaszczyk_LUT-ITK-SNAP_v2.txt</em> file can be used as a Look-Up-Table to display the correct label for each region. The <em>final-stats</em>* files contain the R code used to analyse the volume data, which can be found in the file <em>region-volumes.csv</em>. The <em>region-structures.csv</em> file is used in the R code to label the regions.
description: This project was supported by the APR Neuro2Co grant (Convention 2017 00117257; coordinator Elodie Chaillou, France) from Région Centre Val de Loire. Marine Siwiaszczyk's PhD was funded by Région Centre Val de Loire and Beauval Nature Foundation. Cyril Poupon and Raissa Yebga Hot received funding from the European Union's Horizon 2020 Framework Program for Research and Innovation under the specific Grants No. 945539 (HBP SGA3). The authors have no relevant financial or non-financial interests to disclose.