cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4269952
title: Well-being and Productivity of Software Professionals during a Pandemic

description: The COVID-19 pandemic has forced governments worldwide to impose movement restrictions on their citizens. Although critical to reducing the virus' reproduction rate, these restrictions come with far-reaching social and economic consequences. In this paper, we investigate the impact of these restrictions on an individual level among software engineers currently working from home. Although software professionals are accustomed to working with digital tools in their day-to-day work, the abrupt and enforced work-from-home context has resulted in an unprecedented scenario for the software engineering community. In a two-wave longitudinal study (N = 192), we covered over 50 psychological, social, situational, and physiological factors that have previously been associated with well-being or productivity. Examples include anxiety, distractions, psychological and physical needs, office set-up, stress, and work motivation. This design allowed us to identify those variables that explain unique variance in well-being and productivity. Results include (1) the quality of social contacts predicted positively, and stress predicted an individual's well-being negatively when controlling for other variables consistently across both waves; (2) boredom and distractions predicted productivity negatively; (3) productivity was less strongly associated with all predictor variables at time two compared to time one, suggesting that software engineers adapted to the lockdown situation over time; and (4) the longitudinal study did not provide evidence that any predictor variable causal explained variance in well-being and productivity. Overall, we conclude that working from home was <em>per se</em> not a significant challenge for software engineers. Our study can assess the effectiveness of current work-from-home and general well-being and productivity support guidelines and provide tailored insights for software professionals.
DOI: 10.5281/zenodo.4269557
title: Data Set htwddKogRob-InfReal for Localization and Lifelong Mapping

description: This small dataset contains real world log files from a 2.2 km long patrol between two points of a previously known map (see htwddKogRob-InfReal.png | 1px \(\widehat{=} \) 0.1m). The environment changes slightly and there are some dynamic obstacles. The figure (see htwddKogRob-InfReal_path.jpg) shows the path driven by the robots according to the real kilometers driven and the patrol points. The work was first presented in: A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme Date of Publication: 2016/10/6 Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics Publisher: ACTA Press Additionally, we present a video with the proposed algorithm and an insight of this dataset under: youtube.com/AugustDerSmarte https://www.youtube.com/watch?v=26NBFN_XeQg <strong>Instructions for use</strong> The zip archive contains ascii files, which contain the log files of the robot observations and robot poses. Since this data set was recorded in a real environment, the logfile provides only the odometry based robot poses. For further information, please refer to the header of the logfile. To simplify the parsing of the files, you can use these two Java snippets: <strong>Laser Range Measurements:</strong> <pre><code class="language-java"> List&lt;Double&gt; ranges = new ArrayList&lt;&gt;(numOfLaserRays); List&lt;Error&gt; errors = new ArrayList&lt;&gt;(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <strong>Poses:</strong> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre>
description: For further questions please contact frank.bahrmann@htw-dresden.de
DOI: 10.5281/zenodo.4269556
title: Data Set htwddKogRob-InfReal for Localization and Lifelong Mapping

description: This small dataset contains real world log files from a 2.2 km long patrol between two points of a previously known map (see htwddKogRob-InfReal.png | 1px \(\widehat{=} \) 0.1m). The environment changes slightly and there are some dynamic obstacles. The figure (see htwddKogRob-InfReal_path.jpg) shows the path driven by the robots according to the real kilometers driven and the patrol points. The work was first presented in: A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme Date of Publication: 2016/10/6 Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics Publisher: ACTA Press Additionally, we present a video with the proposed algorithm and an insight of this dataset under: youtube.com/AugustDerSmarte https://www.youtube.com/watch?v=26NBFN_XeQg <strong>Instructions for use</strong> The zip archive contains ascii files, which contain the log files of the robot observations and robot poses. Since this data set was recorded in a real environment, the logfile provides only the odometry based robot poses. For further information, please refer to the header of the logfile. To simplify the parsing of the files, you can use these two Java snippets: <strong>Laser Range Measurements:</strong> <pre><code class="language-java"> List&lt;Double&gt; ranges = new ArrayList&lt;&gt;(numOfLaserRays); List&lt;Error&gt; errors = new ArrayList&lt;&gt;(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <strong>Poses:</strong> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre>
description: For further questions please contact frank.bahrmann@htw-dresden.de
DOI: 10.5281/zenodo.4269951
title: Augmented emission maps: 1997 cc 100 kW Euro 3 petrol engine

description: In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code. This specific data set refers to the 1997 cc 100 kW Euro 3 petrol engine that has been applied in the Citroen C4, C5, C8, Xsara, and Xsara Picasso. Peugeot 206, 307, 406, 407, 607, 806, 807, and Expert. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: the engine taxonomy code, total driven kilometers over which the data was gathered, total time in hours over which the data was gathered, the number of vehicles which were tested to create the emission map, the DOI (Digital Object Identifier) reference, Which emission maps are available in the file. The DOI http://doi.org/10.5281/zenodo.4268034 refers to a updated meta-data document that provides the full description of the standardized emission map.
DOI: 10.5281/zenodo.4269950
title: Augmented emission maps: 1997 cc 100 kW Euro 3 petrol engine

description: In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code. This specific data set refers to the 1997 cc 100 kW Euro 3 petrol engine that has been applied in the Citroen C4, C5, C8, Xsara, and Xsara Picasso. Peugeot 206, 307, 406, 407, 607, 806, 807, and Expert. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: the engine taxonomy code, total driven kilometers over which the data was gathered, total time in hours over which the data was gathered, the number of vehicles which were tested to create the emission map, the DOI (Digital Object Identifier) reference, Which emission maps are available in the file. The DOI http://doi.org/10.5281/zenodo.4268034 refers to a updated meta-data document that provides the full description of the standardized emission map.
DOI: 10.5281/zenodo.4269965
title: Augmented emission maps: 1199 cc 55 kW Euro 4 petrol engine

description: In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code. This specific data set refers to the 1199 cc 55 kW Euro 4 petrol engine that has been applied in the Opel Corsa, Agila, and Astra. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: the engine taxonomy code, total driven kilometers over which the data was gathered, total time in hours over which the data was gathered, the number of vehicles which were tested to create the emission map, the DOI (Digital Object Identifier) reference, Which emission maps are available in the file. The DOI http://doi.org/10.5281/zenodo.4268034 refers to a updated meta-data document that provides the full description of the standardized emission map.
DOI: 10.5281/zenodo.4269964
title: Augmented emission maps: 1199 cc 55 kW Euro 4 petrol engine

description: In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code. This specific data set refers to the 1199 cc 55 kW Euro 4 petrol engine that has been applied in the Opel Corsa, Agila, and Astra. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: the engine taxonomy code, total driven kilometers over which the data was gathered, total time in hours over which the data was gathered, the number of vehicles which were tested to create the emission map, the DOI (Digital Object Identifier) reference, Which emission maps are available in the file. The DOI http://doi.org/10.5281/zenodo.4268034 refers to a updated meta-data document that provides the full description of the standardized emission map.
DOI: 10.5258/soton/d1331
title: Dataset for: Improving livestock feed safety and infection prevention: Removal of bacterial contaminants from hay using cold water, bubbles and ultrasound

description: Dataset supports: Chong, W. Y., Cox, C., Secker, T., Keevil, C., & Leighton, T. (2021). Improving livestock feed safety and infection prevention: Removal of bacterial contaminants from hay using cold water, bubbles and ultrasound . Ultrasonics Sonochemistry, 71, [105372].
DOI: 10.6084/m9.figshare.13228361
title: Cupressus dupreziana chorology

description: Distribution map of Saharan Cypress (<i>Cupressus dupreziana</i>)<br><br>These maps were produced by combining numerous and heterogeneous data collected from atlas monographs providing complete species distribution maps, from national to regional atlases, occurrence geo-databases, scientific and grey literature.<br>The maps were created using ESRI shapefiles (*.shp, *.shx, *.dbf, *.prj files) archived in the ZIP file. Species range is mapped with polygon features (name suffix "plg"), which define continuous areas of occupancy of the species, and with point features (name suffix "pnt"), which identify more fragmented and isolated populations. If synanthropic occurrences are reported outside the species natural range, additional point and/or polygon shapefiles are also present (suffix "syn").<br>Polygon borders delimiting species ranges are generalized across the mainland and sea boundaries. This offers the possibility to mask sea areas or to clip and extract the terrestrial range parts using GIS data layers of the users' choice. An additional version of polygon ranges are clipped with a coastline (name suffix "clip"), which have been derived from Natural Earth dataset "Admin 0 - Countries" 1:50M version 4.1.0 (https://www.naturalearthdata.com).<br><br>Please cite as:<br>Caudullo, G., Welk, E., San-Miguel-Ayanz, J., 2017. Chorological maps for the main European woody species. Data in Brief 12, 662-666. DOI: doi.org/10.1016/j.dib.2017.05.007<br><br>Additional information and used references are on 'supplementary materials' document:<br>https://doi.org/10.6084/m9.figshare.5091901<br><br>Chorological maps are part of the "European Atlas of Forest Tree Species" project:<br>https://w3id.org/mtv/FISE-Comm/v01
DOI: 10.6084/m9.figshare.13228361.v1
title: Cupressus dupreziana chorology

description: Distribution map of Saharan Cypress (<i>Cupressus dupreziana</i>)<br><br>These maps were produced by combining numerous and heterogeneous data collected from atlas monographs providing complete species distribution maps, from national to regional atlases, occurrence geo-databases, scientific and grey literature.<br>The maps were created using ESRI shapefiles (*.shp, *.shx, *.dbf, *.prj files) archived in the ZIP file. Species range is mapped with polygon features (name suffix "plg"), which define continuous areas of occupancy of the species, and with point features (name suffix "pnt"), which identify more fragmented and isolated populations. If synanthropic occurrences are reported outside the species natural range, additional point and/or polygon shapefiles are also present (suffix "syn").<br>Polygon borders delimiting species ranges are generalized across the mainland and sea boundaries. This offers the possibility to mask sea areas or to clip and extract the terrestrial range parts using GIS data layers of the users' choice. An additional version of polygon ranges are clipped with a coastline (name suffix "clip"), which have been derived from Natural Earth dataset "Admin 0 - Countries" 1:50M version 4.1.0 (https://www.naturalearthdata.com).<br><br>Please cite as:<br>Caudullo, G., Welk, E., San-Miguel-Ayanz, J., 2017. Chorological maps for the main European woody species. Data in Brief 12, 662-666. DOI: doi.org/10.1016/j.dib.2017.05.007<br><br>Additional information and used references are on 'supplementary materials' document:<br>https://doi.org/10.6084/m9.figshare.5091901<br><br>Chorological maps are part of the "European Atlas of Forest Tree Species" project:<br>https://w3id.org/mtv/FISE-Comm/v01
DOI: 10.21227/gk5q-gw14
title: Dataset for IoT Assisted Human Posture Recognition

description: The ability of detecting human postures is particularly important in several fields like ambient intelligence, surveillance, elderly care, and human-machine interaction. Most of the earlier works in this area are based on computer vision. However, mostly these works are limited in providing real time solution for the detection activities. Therefore, we are currently working toward the Internet of Things (IoT) based solution for the human posture recognition. To achieve the objective, in the first phase, dataset of 37419 samples has been prepared consisting of healthy and unhealthy postures with sitting, standing and sleeping activities. In the second phase, this dataset will be used to classify different human postures as healthy and unhealthy using machine learning algorithm.
DOI: 10.21227/13t6-tt42
title: Dataset for IoT Assisted Human Posture Recognition

description: The ability of detecting human postures is particularly important in several fields like ambient intelligence, surveillance, elderly care, and human-machine interaction. Most of the earlier works in this area are based on computer vision. However, mostly these works are limited in providing real time solution for the detection activities. Therefore, we are currently working toward the Internet of Things (IoT) based solution for the human posture recognition. To achieve the objective, in the first phase, dataset of 37419 samples has been prepared consisting of healthy and unhealthy postures with sitting, standing and sleeping activities. In the second phase, this dataset will be used to classify different human postures as healthy and unhealthy using machine learning algorithm.
DOI: 10.5281/zenodo.4269973
title: Augmented emission maps: 1242 cc 48 kW Euro 4 petrol engine

description: In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code. This specific data set refers to the 1242 cc 48 kW Euro 4 petrol engine that has been applied in the Fiat Punto, Doblo. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: the engine taxonomy code, total driven kilometers over which the data was gathered, total time in hours over which the data was gathered, the number of vehicles which were tested to create the emission map, the DOI (Digital Object Identifier) reference, Which emission maps are available in the file. The DOI http://doi.org/10.5281/zenodo.4268034 refers to a updated meta-data document that provides the full description of the standardized emission map.
DOI: 10.5281/zenodo.4269810
title: EPSRC HEED Data Repository: Lantern Monitoring System

description: The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energy for Displacement research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. As part of the project, we deployed Lantern Monitoring Systems in Nyabiheke camp, Rwanda. The aim was to (a) evaluate lantern usage pattern – static or mobile (b) evaluate lantern charge and discharge pattern to understand consumption behaviour. The mobile lantern monitors comprise of a D.light S30 solar lantern fitted with an Arduino-based monitoring device. The most integral part of the device is the Arduino MKR GSM 1400 board connected to an ADXL345 inertial motion unit sensor. The ADXL is used to generate activity and freefall interrupts based on acceleration readings when the lantern is in use. These interrupts are, in turn, processed to calculate the step count of the user. Additionally, the voltage of lantern battery is measured using an in-house designed voltage monitor to evaluate the discharging and charging patterns. The updated values of step count, rate of change of steps and device and lantern battery voltage are stored only if a significant change in the step count is detected. The device is packaged within the lantern casing and powered through a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh. The study was conducted in 2 phases. In phase 1 (03 July 2019 to 30 September 2019), data was collected from 60 lanterns and stored locally on SD card as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. Moreover, several incidents of theft and device failures were reported leading to loss of data. In phase 2 (09 October 2019 to 18 December 2019), the design of lantern monitors was modified to fix these issues. The data was collected from 54 lanterns data and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board and internal watchdog timer was used to reset the device in case of failures. While certain failures persisted, the data yield was considerably higher than phase 1 of the study. The data from both phases of study is deposited here along with the metadata.
DOI: 10.5281/zenodo.4269972
title: Augmented emission maps: 1242 cc 48 kW Euro 4 petrol engine

description: In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code. This specific data set refers to the 1242 cc 48 kW Euro 4 petrol engine that has been applied in the Fiat Punto, Doblo. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: the engine taxonomy code, total driven kilometers over which the data was gathered, total time in hours over which the data was gathered, the number of vehicles which were tested to create the emission map, the DOI (Digital Object Identifier) reference, Which emission maps are available in the file. The DOI http://doi.org/10.5281/zenodo.4268034 refers to a updated meta-data document that provides the full description of the standardized emission map.