cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10155824
title: hospital-lyon

description: Overview
This dataset contains the temporal network of contacts between patients, between patients and healthcare workers (HCWs), and among HCWs in a hospital ward in Lyon, France, from Monday, December 6, 2010, at 1:00 pm to Friday, December 10, 2010, at 2:00 pm. The study included 46 HCWs and 29 patients.
Timestamps were recorded in 20-second intervals. In the original data, each line has the form "t i j Si Sj", where i and j are the anonymous IDs of the persons in contact, Si and Sj are their labels (NUR=paramedical staff, i.e. nurses and nurses' aides; PAT=Patient; MED=Medical doctor; ADM=administrative staff), and the interval during which this contact was active is [ t – 20s, t ]. We form hyperedges through cliques of simultaneous contacts. Specifically, for every unique timestamp in the dataset, we construct a hyperedge for every maximal clique amongst the contact edges that exist for that timestamp. All timestamps are in standard ISO8601 format.
Statistics

number of nodes: 75 (46 HCWs and 29 patients)
number of timestamped hyperedges: 21,398
there is a single connected component of size 75
Source of original data

Hospital ward dynamic contact network
References
If you use this dataset, please cite the following:

Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors. Vanhems et al., PLoS ONE, 2013.
The SocioPatterns collaboration
DOI: 10.5281/zenodo.10155825
title: hospital-lyon

description: Overview
This dataset contains the temporal network of contacts between patients, between patients and healthcare workers (HCWs), and among HCWs in a hospital ward in Lyon, France, from Monday, December 6, 2010, at 1:00 pm to Friday, December 10, 2010, at 2:00 pm. The study included 46 HCWs and 29 patients.
Timestamps were recorded in 20-second intervals. In the original data, each line has the form "t i j Si Sj", where i and j are the anonymous IDs of the persons in contact, Si and Sj are their labels (NUR=paramedical staff, i.e. nurses and nurses' aides; PAT=Patient; MED=Medical doctor; ADM=administrative staff), and the interval during which this contact was active is [ t – 20s, t ]. We form hyperedges through cliques of simultaneous contacts. Specifically, for every unique timestamp in the dataset, we construct a hyperedge for every maximal clique amongst the contact edges that exist for that timestamp. All timestamps are in standard ISO8601 format.
Statistics

number of nodes: 75 (46 HCWs and 29 patients)
number of timestamped hyperedges: 21,398
there is a single connected component of size 75
Source of original data

Hospital ward dynamic contact network
References
If you use this dataset, please cite the following:

Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors. Vanhems et al., PLoS ONE, 2013.
The SocioPatterns collaboration
DOI: 10.5281/zenodo.10211338
title: Software Solutions for Newcomers in Software Projects: a Systematic Literature Review

description: Systematic Literature Review - Supplementary material
DOI: 10.5281/zenodo.10211339
title: Software Solutions for Newcomers in Software Projects: a Systematic Literature Review

description: Systematic Literature Review - Supplementary material
DOI: 10.6084/m9.figshare.22671118
title: (Primary Data Set) _ Cockroach Trackway Measurements for Both Experimental and Fossil Trackway (Symmetric Only for Statistical Analysis)

description: This is the actual dataset containing the characteristics and trackway measurements for each analyzed trackway segment (symmetric only--asymmetric trackways not included). Contains also the picture file number analyzed for each trackway (refer to annotated/drawn photos). For explanation of the parameters/variables used see "(Supplementary Material A) _ Explanation of Cockroach Trackway Characteristics and Measurements Used."
DOI: 10.6084/m9.figshare.22671118.v1
title: (Primary Data Set) _ Cockroach Trackway Measurements for Both Experimental and Fossil Trackway (Symmetric Only for Statistical Analysis)

description: This is the actual dataset containing the characteristics and trackway measurements for each analyzed trackway segment (symmetric only--asymmetric trackways not included). Contains also the picture file number analyzed for each trackway (refer to annotated/drawn photos). For explanation of the parameters/variables used see "(Supplementary Material A) _ Explanation of Cockroach Trackway Characteristics and Measurements Used."
DOI: 10.6084/m9.figshare.22297201
title: (Primary Data Set) _ Trace Parameters for Both Experimental and Fossil Trails and Simple Trackways

description: This is the actual dataset containing the characteristics and trace measurements for each analyzed trace segment (unanalyzable traces not included). Contains also the picture file number analyzed for each trace (refer to the folders containing photos for each species in this collection). For explanation of the parameters used see "Supp. Mat. A _ Explanation of Simple Trace Characteristics and Measurements Used.docx"
DOI: 10.6084/m9.figshare.22297201.v1
title: (Primary Data Set) _ Trace Parameters for Both Experimental and Fossil Trails and Simple Trackways

description: This is the actual dataset containing the characteristics and trace measurements for each analyzed trace segment (unanalyzable traces not included). Contains also the picture file number analyzed for each trace (refer to the folders containing photos for each species in this collection). For explanation of the parameters used see "Supp. Mat. A _ Explanation of Simple Trace Characteristics and Measurements Used.docx"
DOI: 10.17632/2tg7ffdr56.1
title: Dataset of the relationship between authentic virtual reality experiences and tourists’ visiting intentions of a tourism destination

description: Dataset of the relationship between authentic virtual reality experiences and tourists’ visiting intentions of a tourism destination
DOI: 10.17632/j6yt2vg7vz.1
title: Εffects of different sound frequencies on intensively farmed fish.

description: Εffects of different sound frequencies on intensively farmed fish.Comet assay method.
DOI: 10.6084/m9.figshare.23210945
title: Misc. Invertebrate Traces Dataset

description: This is the spreadsheet dataset containing the details for each trackway including experimental condition and associated photo number (compare with the trace photos for each species in this collection). Due to dissimilarity with fossil traces from the Coconino Sandstone, these trackways were not analyzed to the extent the other species were. However, the pictures show the variety of traces produced by Okefenokee Swamp fishing spiders (OFS), blueband hermit crabs (HCB), purple olive snails (POV), and western mud nassas (WMN), and may still be useful in future research.
DOI: 10.6084/m9.figshare.23210945.v1
title: Misc. Invertebrate Traces Dataset

description: This is the spreadsheet dataset containing the details for each trackway including experimental condition and associated photo number (compare with the trace photos for each species in this collection). Due to dissimilarity with fossil traces from the Coconino Sandstone, these trackways were not analyzed to the extent the other species were. However, the pictures show the variety of traces produced by Okefenokee Swamp fishing spiders (OFS), blueband hermit crabs (HCB), purple olive snails (POV), and western mud nassas (WMN), and may still be useful in future research.
DOI: 10.17632/fkd6c452w4.1
title: Data_Mörchen et al. 2023

description: Mörchen et al. Manuscript, in Review, iScience Preprint: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4603950 "Orangutan males make increased use of social learning opportunities, when resource availability is high"
DOI: 10.17632/6ydrkdydrw.1
title: the Digital Transformation

description: This data is about an analysis of digital transformation's impact on employee engagement, productivity, and financial performance in organizations
DOI: 10.17632/9sdysmyt6b.1
title: Sovereign spread divergence owing to inflation and redenomination risk countered by unconventional monetary policy in the Eurozone

description: Using a panel vector error correction (VECM) model, we assess sovereign spread divergence in the Eurozone. Recent literature defines redenomination risk as a member state’s unilateral exit from the Eurozone, estimated by the quanto Credit default swaps (CDS) spread (qCDS). We show that sovereign spread divergence is driven not only by the qCDS but also by inflation and US dollar liquidity shocks. We also find evidence that the balance sheet structure better describes the European Central Bank’s (ECB) unconventional monetary policy (UMP) shocks than the shadow rate. Such policy shocks had a greater impact in the early 2020s than between the global financial crisis and the COVID-19 pandemic (2008Q1–2019Q4). Thus, this paper makes a new contribution to the debate on the ECB’s instruments to maintain price stability objectives and avoid divergence of sovereign bond yields while emphasizing cross-central bank swap lines.
DOI: 10.17632/78g2r5dp3k.1
title: Data, R Scripts and Random Forest Models for Winter Catch Crop Monitoring from Sentinel-2 NDVI Time Series in Germany

description: This is the supplementary data to the article "Large-scale winter catch crop monitoring with Sentinel-2 time series and machine learning–An alternative to on-site controls?" in Computers and Electronics in Agriculture by C. Schulz, A.-K. Holtgrave, and B. Kleinschmit 2021 (https://doi.org/xxxxxxxx). The data contains a zip-file with the following folders: data (parcels, filled and unfilled time series tables, feature extraction results and prediction results) (csv, shp), model (random forest models for catch crop prediction) (rds), and R (R script files for Random Forest model training and prediction with RStudio) (r). The algorithms and RF models developed for this study were implemented via virtual Docker containers into the timeStamp software prototype which allows for large-scale automatized catch crop analysis on the parcel-level (timestamp.lup-umwelt.de). This software saves the raster data from the GTS² archive as parcel-wise clipped image time series into a PostGIS database. All further processing steps were performed with the statistical computing language R (RStudio Team, 2020). For raster data manipulation within the PostGIS database and downloading NDVI time series, we used the packages rpostgis (Bucklin and Basille, 2019) and RPostgreSQL (Conway et al., 2017). For time series filling and calculation of the predictors, we used the packages zoo (Zeileis et al., 2020), hydroGOF (Zambrano-Bigiarini, 2020), tsoutliers (de Lacalle, 2019), and changepoint (Killick et al., 2016). For RF modelling, we used the package caret (Kuhn et al., 2020).
DOI: 10.17632/78g2r5dp3k.2
title: Data, R Scripts and Random Forest Models for Winter Catch Crop Monitoring from Sentinel-2 NDVI Time Series in Germany

description: The data contains a zip-file with the following folders: a) data (agricultural parcels, filled and unfilled NDVI time series tables, feature extraction tables and prediction results) (csv, shp), b) model (random forest models for catch crop prediction) (rds), and c) R (R script files for Random Forest model training and prediction with RStudio) (r). The algorithms and models developed for this study were implemented via virtual Docker containers into the timeStamp software prototype which allows for large-scale automatized catch crop analysis on the parcel-level (www.timestamp.lup-umwelt.de). timeStamp saves the raster data from the GTS² archive as parcel-wise clipped image time series into a PostGIS database. All further processing steps were performed with the statistical computing language R (RStudio Team, 2020). For raster data manipulation within the PostGIS database and downloading NDVI time series, we used the packages rpostgis (Bucklin and Basille, 2019) and RPostgreSQL (Conway et al., 2017). For time series filling and predictors calculation, we used the packages zoo (Zeileis et al., 2020), hydroGOF (Zambrano-Bigiarini, 2020), tsoutliers (de Lacalle, 2019), and changepoint (Killick et al., 2016). For RF modelling, we used the package caret (Kuhn et al., 2020). The original data for NDVI time series calculation is from the GFZ Time Series System for Sentinel-2 by the German Research Centre for Geosciences, 2020 (https://gitext.gfz-potsdam.de/gts2). The predictors for Random Forest modelling calculated from the NDVI time series are described in the article in the reference section. For further information, we refer to the article mentioned in the references.
DOI: 10.17632/78g2r5dp3k.3
title: Data, R Scripts and Random Forest Models for Winter Catch Crop Monitoring from Sentinel-2 NDVI Time Series in Germany

description: The data contains a zip-file with the following folders: a) data (agricultural parcels, filled and unfilled NDVI time series tables, feature extraction tables and prediction results) (csv, shp), b) model (random forest models for catch crop prediction) (rds), and c) R (R script files for Random Forest model training and prediction with RStudio) (r). The algorithms and models developed for this study were implemented via virtual Docker containers into the timeStamp software prototype which allows for large-scale automatized catch crop analysis on the parcel-level (www.timestamp.lup-umwelt.de). timeStamp saves Sentinel-2 raster data as parcel-wise clipped image time series into a PostGIS database. All further processing steps were performed with the statistical computing language R (RStudio Team, 2020). For raster data manipulation within the PostGIS database and downloading NDVI time series, we used the packages rpostgis (Bucklin and Basille, 2019) and RPostgreSQL (Conway et al., 2017). For time series filling and predictors calculation, we used the packages zoo (Zeileis et al., 2020), hydroGOF (Zambrano-Bigiarini, 2020), tsoutliers (de Lacalle, 2019), and changepoint (Killick et al., 2016). For RF modelling, we used the package caret (Kuhn et al., 2020). The original data for NDVI time series calculation is from the GFZ Time Series System for Sentinel-2 by the German Research Centre for Geosciences, 2020 (https://gitext.gfz-potsdam.de/gts2). The predictors for Random Forest modelling calculated from the NDVI time series are described in the article in the reference section. For further information, we refer to the following article: Schulz, C.; Holtgrave, A.; Kleinschmit, B.: Large-scale winter catch crop monitoring with Sentinel-2 time series and machine learning–An alternative to on-site controls?, Computers and Electronics in Agriculture, Volume 186, 2021, 106173, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2021.106173.
DOI: 10.17632/7rfwyw2tr9.1
title: Data for Ojelade et al. "Capture and discard practices associated with an ornamental fishery affect the metabolic rate and aerobic capacity of three-striped dwarf cichlids Apistogramma trifasciata"

description: Data from the paper entitled "Capture and discard practices associated with an ornamental fishery affect the metabolic rate and aerobic capacity of three-striped dwarf cichlids Apistogramma trifasciata". Fishing causes the direct removal of individuals from wild populations but can also cause a physiological disturbance in fish that are released or discarded after capture. While the sublethal physiological effects of fish capture have been well-studied in commercial and recreational fisheries, this issue has been overlooked for fisheries in the ornamental fish trade, where it is common to capture fish from the wild and then discard non-target species. We examined the metabolic response to capture and discard procedures in the three-stripe dwarf cichlid Apistogramma trifasciata, a popular Amazonian aquarium species that nonetheless may be discarded when not a target species. Individuals (n = 34) were tagged and exposed to each of four treatments designed to simulate typical procedures during the capture and discard process: 1) a non-handling control; 2) netting; 3) netting + 30 seconds of air exposure; and 4) netting + 60 seconds of air exposure. Metabolic rates were then estimated using intermittent-flow respirometry, immediately following each treatment then throughout recovery overnight. Increasing amounts of netting and air-exposure caused an acute increase in oxygen uptake and decrease in available aerobic scope. In general, recovery occurred quickly, with rapid decreases in oxygen uptake within the first 30 minutes post-stress handling. Notably, however, male fish exposed to netting + 60 seconds of air exposure showed a delayed response whereby available aerobic scope was constrained below 75% of maximum until around 4-6 hours post-stress. In addition, larger fish showed a greater initial increase in oxygen uptake post-stress and slower rates of recovery. The results suggest that in the period following discard, this species may experience a reduced aerobic capacity for additional behavioral/physiological responses including feeding, territory defence, and predator avoidance. These results are among the first to examine the impacts of discard practices in the ornamental fishery and suggest that ecophysiological research can provide valuable insight toward increasing sustainable practices in this global trade. File contain data plus a metadata tab explaining each column and the units of measurement.
DOI: 10.17632/87skfwv67x.1
title: data_of_telenutrition

description: telenutrition article data
DOI: 10.17632/gwjdhw6rw3.1
title: Comparative transcriptomics of CanDef-20 fed Helicoverpa armigera

description: The analysis was carried out to decipher the effect of defensin peptide (CanDef-20) ingestion on the insect pest H. armigera. Recombinant CanDef-20 was fed to H. armigera larvae. EV derived proteins were fed to the larvae and that set was treated as control. These two sets were used for the comparative transcriptomics analysis.
DOI: 10.17632/43s22jvj8w.1
title: ANTIMICROBIAL, BARRIER, AND MECHANICAL PROPERTIES OF BIOCOMPOSITES PREPARED FROM CARROT POMACE AND WHEAT GLUTEN WITH VARIED EUGENOL CONTENT

description: The dataset encompasses comprehensive raw data pertaining to the antimicrobial, barrier, and mechanical properties of films. These films were meticulously crafted by blending carrot pomace with wheat gluten and polyglycerol-3 plasticizer in a mortar with pestle. The formulations were then enriched with different concentrations (0%, 3%, and 5%) of eugenol, a naturally occurring antimicrobial compound derived from essential oils. The final step involved the mixtures compression molding in a hot press at 90 °C for 5 minutes under 5 tons of pressure, resulting in the creation of the films for which detailed data is presented in this dataset.
DOI: 10.17632/xt69cnwh56.2
title: 1Hz GPS Tracking Data

description: To date, GPS tracking data for minibus taxis has only been captured at a sampling frequency of once per minute. This is the first GPS tracking data captured on a per-second (1 Hz) basis. Minibus taxi paratransit vehicles in South Africa are notorious for their aggressive driving behaviour characterised by rapid acceleration/deceleration events, which can have a large effect on vehicle energy consumption. Infrequent sampling cannot capture these micro-mobility patterns, thus missing out on their effect on vehicle energy consumption (kWh/km). We hypothesised that to construct high fidelity estimates of vehicle energy consumption, higher resolution data that captures several samples per movement would be needed. Estimating the energy consumption of an electric equivalent (EV) to an internal combustion engine (ICE) vehicle is requisite for stakeholders to plan an effective transition to an EV fleet. Energy consumption was calculated following the kinetic model outline in "The bumpy ride to electrification: High fidelity energy consumption estimates for minibus taxi paratransit vehicles in South Africa". Six tracking devices were used to record GPS data to an SD card at a frequency of 1Hz. The six recording devices are based on the Arduino platform and powered from alkaline battery packs. The device can therefore operate independently of any other device during tests. The acquired data is separately processed after the completion of data recording. Data captured is initiated with the press of a button, and terminated once the vehicle reached the destination. Each recorded trip creates an isolated file. This allows for different routes to be separately investigated and compared to other recordings made on the same route. There are 62 raw trip files, all found in the attached 'raw data' folder under the corresponding route and time of day in which they were captured. The raw data includes date, time, velocity, elevation, latitude, longitude, heading, number of satellites connected, and signal quality. Data was recorded on three routes, in both directions, for a total of six distinct routes. Each route had trips recorded in the morning (before 11:30AM) , afternoon (11:30AM-4PM) and evening (after 4PM). The processed data is available in the 'Processed Data' folder. In addition to the raw data, these processed data files include the displacement between observations, calculated using Geopy's geodesic package, and the estimated energy provided by the vehicle's battery for propulsion, braking, and offload work. The python code for the kinetic model can be found in the attached GitHub link https://github.com/ChullEPG/Bumpy-Ride. Future research can use this data to develop standard driving cycles for paratransit vehicles, and to improve the validity of micro-traffic simulators that are used to simulate per-second paratransit vehicle drive cycles between minutely waypoints.
DOI: 10.17632/zcdvg3v7mz.1
title: Effects of Chinese herbal medicine on pregnancy outcomes of women with PCOS undergoing in vitro fertilization and embryo transfer

description: Effects of Chinese herbal medicine on pregnancy outcomes of women with PCOS undergoing in vitro fertilization and embryo transfer
DOI: 10.17632/sxyjnhvbm5.2
title: Tugai wetland maps and validation data at the Syr Darya floodplain based on MODIS and Landsat 7 NDVI time series

description: The data contains a zip-file with the validation point data (shp), mapping results (tiff, kml, txt), the area of interest (shp), and the Tugai wetlands NDVI reference profile (csv). The wetland maps are results from a linear regression-based wetland detection analysis with Landsat 7 and MODIS time series data (Original Source: United States Geological Survey.) The trend and breakpoint detection maps are results of a time series analysis with the BFAST package (Verbesselt et al., 2010) which was done in the R statistical computing language (RStudio Team, 2020). The mean annual reference NDVI profile for Tugai wetlands was calculated for ten ground truth samples of riparian forest stands at the Syr Darya-Turkestan Nature Park. The validation data at the three 1500 km² large regions Kyzylorda, Zhanakorgan, and the Syr Darya-Turkestan Nature Park was derived from visual interpretation of very high spatial resolution Digital Globe images via Google Earth. This dataset is a contribution of the ForeCeA-project “Riparian forests in arid regions of Central Asia under pressure by use and limited water resources: definition of tipping points to ensure sustainable management” funded by the German Ministry of Research and Education (01LC1707C). If you use this data, please cite this article: Schulz, C.; Kleinschmit, B. Monitoring the Condition of Wetlands in the Syr Darya Floodplain—How Healthy Are the Tugai Forests in Kazakhstan? Forests 2023, 14, 2305. https://doi.org/10.3390/f14122305.
DOI: 10.17632/d9dz4yvgf3.1
title: Vertigo

description: These datasets were used to develop and verify a XGBoost model as a classification tool for diagnosing different types of peripheral vertigo.
DOI: 10.17632/d9dz4yvgf3.2
title: Diagnosis of peripheral vertigo

description: These datasets were used to develop and verify a XGBoost model as a classification tool for diagnosing different types of peripheral vertigo.