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3,947 results for “Requirements”
Dataset for ELGO-DIMITRA Data Management Practices & Requirements: A Scoping Report
<p>This is a comprehensive data repository of the <em>data management survey</em> carried out in Autumn of 2023 through a collaboration between the <a href="https://opensciencestudies.eu/">PHIL_OS</a> project and the <a href="https://agres.elgo.gr/">Research Directorate of the Hellenic Agricultural Organization ELGO-DIMITRA</a>.</p> <p>Please cite as: </p> <blockquote> <p>Tsiroukis F., Leonelli S. and ELGO-DIMITRA (2024) <em>Dataset for ELGO-DIMITRA Data Management Practices & Requirements: A Scoping Report.</em> PHIL_OS Report. DOI: 10.5281/zenodo.14003418</p> </blockquote>
Four Essential Components for FAIR Data: Capability & Category-Specific Requirements
<p>Adapted from Bailo (2019) and Peng (2023), this diagram illustrates FAIR requirements specific to data, metadata, and infrastructure, aligned with the definitions of individual FAIR principles. It highlights the critical role of enterprise capabilities—including processes, systems, standards, tools, and skills—in supporting FAIR data. These four components are essential for systematically enhancing the overall FAIRness of an organization's scientific data collection</p> <p> </p>
The exercise paradox: Avoiding physical inactivity stimuli requires higher response inhibition
<p><strong>Dataset related to the paper on Response inhibition to physical inactivity stimuli using go/no-go tasks. </strong></p> <p>This dataset includes:</p> <p><strong>1) A codebook (including the name of the main variables)</strong></p> <p>--> "code_book_Go_noGo_Miller.xlsx"</p> <p><strong>2) Raw data of the behavioral outcomes (i.e., reaction times) of the affective go/no-go task</strong></p> <p>--> "corrected.behavioral.data.csv"</p> <p>--> "correct_Order.csv"</p> <p><strong>3) Self-reported data </strong></p> <p>--> "Self_report_data.csv"</p> <p><strong>3) EEG data </strong></p> <p>--> "gng_data"</p> <p><strong>5) R script for the data management (i.e., from the raw data to data ready to be analyzed)</strong></p> <p>--> "Data_management_Self_report_go_no_go_Miller.R" for the self-reported data (return the file: "Data_SR_final.RData")</p> <p>--> "Data_management_behav_go_no_go_Miller.R" for the behavioral outcomes (return the file: "Data_GNG_behav.RData")</p> <p>--> Data ready to be analyzed "Data_GNG_final_all.RData"</p> <p><strong>6) Eprime script for the affective go/no-go task ("Go_no_go_task.zip")</strong></p> <p>--> Images depicting physical activity and physical inactivity stimuli were kindly Share by Kullmann et al. (2014)</p> <p><strong>7) R script for the models tested</strong></p> <p><strong>--> "</strong>Models_GoNogo_Miller_VZenodo.R" for behavioral data</p> <p>--> "Models_EEG_GoNogo.R" for EEG data</p>
NICHE Flanders: reference values for the (a)biotic requirements of vegetation types in Flanders, Belgium
<p>This dataset contains site requirements/tolerance limits (or "reference values") for 28 vegetation types found in Flanders. It gives the lower and upper limits or the classes within which these vegetation types can occur, for 7 site factors that determine potential vegetation development. These reference values can be used to determine the potential distribution of the different vegetation types with the ecohydrological model NICHE Flanders (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>, in Dutch).</p> <p>See the Technical info (available in English and Dutch) for more information.</p>
Public charging requirements for battery electric long-haul trucks in Europe: a trip chain approach
<p>Contact details:</p> <p>wasim.shoman at chalmers.se </p> <p>waahh7 at gmail com</p> <p><strong>Abstract of the research:</strong></p> <p>Heavy-duty vehicles (HDV) account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO<sub>2</sub> emissions from road transport. Battery electric trucks (BETs) could be deployed on a large scale to reduce greenhouse gas emissions. However, they require sufficient charging infrastructure to support long-haul operations. Therefore, assessing the required charging locations, energy, and power requirements is critical. We use a trip-chain-based model to derive charging requirements for BETs in long-haul operation (travel times over 4.5 hours or over 360 km distance traveled) for Europe in 2030. We convert an origin-destination (OD) matrix into trip chains combined with European truck driving regulations to derive break and rest stops. We show that an average charging area (defined as a 25´25 km<sup>2</sup> square with each square that could include multiple charging stations and parking lots of multiple charging points) needs to have four to five times more overnight than megawatt charging points. We estimate that about 40,000 overnight charging points (50-100 kW, combined charging system, CCS) and about 9,000 megawatt charging system (MCS, 0.7 – 1.2 MW) points are required for 15% of trucks as BETs in long-haul operation. On average, 8 and 2 CCS and MCS chargers are required per charging area, and each MCS and CCS serve, on average, 11 and 2 BETs daily, respectively. Public charging entails about 110 GWh daily electricity demand in each charging area. The model can be applied to any region with similar data. Future work can consider improving the queuing model, assumptions regarding regional differences of BET penetration, and heterogeneity of truck sizes and utilization.</p> <p><strong>The methodology:</strong></p> <p>We develop a method to place charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by HDV is identified using the ETISplus dataset. We develop a travel pattern for the HDV to convert flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving HDV. Break and rest locations for all moving HDVs are aggregated to suggest energy requirements if assuming these HDVs are BETs. The aggregated energy to charge stopped BETs is used to identify the number and type of chargers within each suggested charging station.</p> <p><strong>Datasets details</strong></p> <p>The presented datasets contain spatial information for generating charger stations with specifications according to charging needs. The datasets contain information about: Transport network model and edges, Transported flows, routes and flow center information data, region centers, and Planned transport infrastructure. </p> <p>The first dataset titled 'ChargerLocations' contains information about the locations of suggested charging stations, the number and type of chargers, and the number of visited electrified trucks in 2030. It is a shapefile with the following details for its fields:</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> <td>Data Type</td> <td>Unit</td> </tr> <tr> <td>DTN30/MainDTN</td> <td> number of electrified trucks in 2030</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChE30</td> <td> charged energy in Mega watt-hour from all charging (fast and slow)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>ChERM</td> <td> charged energy in Megawatt hour with slow charging only (rest)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_R</td> <td> number of electrified trucks using slow chargers (rest)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChEBM</td> <td> charged energy in Megawatt hour with fast charging only (break)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_B</td> <td> number of electrified trucks using fast chargers (break)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NSCh2pD</td> <td> number of slow chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NFCh30m</td> <td> number of fast chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>TotCha</td> <td> Total number of chargers</td> <td>integer </td> <td>number</td> </tr> </tbody> </table> <p> </p> <p>The second dataset titled (RestandBreaksPoints.shp) with information about the rest and break point locations. The dataset includes detailes about stop type, number of stopped trucks, and required charged energy. The dataset is a shapefile with "shp" format. </p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data Type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>ID_origin_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_origin_region</p> </td> <td> <p>National name of NUTS-3 region of origin</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ID_destination_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_destination_<br> region</p> </td> <td> <p>National name of NUTS-3 destination region</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Rest</p> </td> <td> <p>A value of ”1” indicates a rest stop</p> </td> <td> <p>Boolean</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Break</p> </td> <td> <p>A value of ”1” indicates a break stop</p> </td> <td> <p>Boolean</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ChaDisKM</p> </td> <td> <p>Charged range within a trip for stopped the truck</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>ChaEnekWh</p> </td> <td> <p>Charged energy within a trip for stopped the truck</p> </td> <td> <p>Float</p> </td> <td> <p>KWh</p> </td> </tr> <tr> <td> <p>MainDTN</p> </td> <td> <p>Number of stopped trucks for the main electrification scenario (15%)</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>ChE30M</p> </td> <td> <p>Charged energy for all stopped trucks</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>ChERM</p> </td> <td> <p>Charged energy for the trucks stopping for rest</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>MDTN_R</p> </td> <td> <p>Number of trucks stopping for rest</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>ChEBM</p> </td> <td> <p>Charged energy for the trucks stopping for break</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>MDTN_B</p> </td> <td> <p>Number of trucks stopping for break</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>X, Y coordinates</p> </td> <td> <p>geometry</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p>The following dataset titled 'flowFile' with information about the transported flow between regions and the transported routes. The dataset is in "CSV" format. Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X):</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>ID_origin_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_origin_region</p> </td> <td> <p>National name of NUTS-3 region of origin</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ID_destination_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_destination_<br> region</p> </td> <td> <p>National name of NUTS-3 destination region</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Edge_path_E_road</p> </td> <td> <p>List of the <em>network edge IDs</em> of the shortest path between the O-D pair, determined with Dijkstra's algorithm</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Distance_from_origin_<br> region_to_E_road</p> </td> <td> <p>Distance from the geometric centre of the origin region to the closest network node</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Distance_within_E_<br> road</p> </td> <td> <p>Distance of the shortest edge path between the O-D pair</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Distance_from_E_<br> road_to_destination_<br> region</p> </td> <td> <p>Distance from the geometric centre of the destination region to the closest network node</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Total_distance</p> </td> <td> <p>Sum of <em>Distance_from_origin_region_to_E_road, Distance_within_E_road</em> and <em>Distance_from_E_road_to_destination_region</em></p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2010</p> </td> <td> <p>Number of trucks that drive between the O-D pair in 2010</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2019</p> </td> <td> <p>Number of trucks that drive between the O-D pair after they had been scaled to 2019</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2030</p> </td> <td> <p>Number of trucks that drive between the O-D pair according to the forecast for 2030</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2010</p> </td> <td> <p>Number of tons that are transported between the O-D pair in 2010 according to ETISplus</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2019</p> </td> <td> <p>Number of tons that are transported between the O-D pair after they had been scaled to 2019</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2030</p> </td> <td> <p>Number of tons that are transported between the O-D pair according to the forecast for 2030</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> </tbody> </table> <p>Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Network_Node_ID</p> </td> <td> <p>Unique network node ID</p> </td> <td> <p>Integer (6 digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Network_Node_X</p> </td> <td> <p>Longitude of the location of network node</p> </td> <td> <p>Float</p> </td> <td> <p>Degrees</p> </td> </tr> <tr> <td> <p>Network_Node_Y</p> </td> <td> <p>Latitude of the location of network node</p> </td> <td> <p>Float</p> </td> <td> <p>Degrees</p> </td> </tr> <tr> <td> <p>ETISplus_Zone_ID</p> </td> <td> <p>ID of the NUTS-3 region in which the network node is located</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Country</p> </td> <td> <p>Unique country code of the country in which the network node is located (country codes are defined by ETISplus)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>Description of variables used in the network edges list (Updated_04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Network_Edge_ID</p> </td> <td> <p>Unique edge ID</p> </td> <td> <p>Integer<br> (7 digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Manually_Added</p> </td> <td> <p>Determines whether an edge had been manually added to the network (1) or not (0)</p> </td> <td> <p>Binary-integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Distance</p> </td> <td> <p>Length of the network edge</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Network_Node_A_ID</p> </td> <td> <p>Unique ID of the network node that defines one end point of the network edge</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Network_Node_B_ID</p> </td> <td> <p>Unique ID of the network node that defines one end point of the network edge</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_2019</p> </td> <td> <p>Number of trucks that drive on the edge in 2019 (both highway directions combined)</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_2030</p> </td> <td> <p>Number of trucks that drive on the edge in 2030 (both highway directions combined)</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Analysis of two Methods for Aircraft Fuel Requirement Calculations in the Context of a novel Methodological Framework for LCA of Sustainable Aviation
<p>This Microsoft Excel file contains equations to compare different approaches to calculate fuel efficiency ("energy use" in [MJ/t*km]) of aircraft over a specific distance at a specific payload. </p> <p>Two approaches are compared: A novel approach by <a href="10.1016/j.scitotenv.2023.163881" target="_blank" rel="noopener">Su-ungkavatin et al.</a> and the more established approach well documented by eg. <a href="https://www.fzt.haw-hamburg.de/pers/Scholz/arbeiten/TextBurzlaff.pdf" target="_blank" rel="noopener">Burzlaff</a> or <a href="http://www.aircraftmonitor.com/uploads/1/5/9/9/15993320/aircraft_payload_range_analysis_for_financiers___v2.pdf" target="_blank" rel="noopener">Ackert</a>.</p> <p>This work augments a Letter to the Editor we submitted to the journal <a href="https://www.sciencedirect.com/journal/science-of-the-total-environment" target="_blank" rel="noopener">Science of the Total Environment</a>.</p>
Replication data for: "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?"
<p>The dataset accompanies the scientific article "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?" and can be used to reproduce the findings presented in this article. This dataset consists of two components, namely (i) the corpus data involving the Dutch semi-copular verb "raken" and (ii) an R analysis script to reproduce the computational steps.</p>
Proteins required for stereocilia elongation during mammalian hair cell development ensure precise and steady heights during adult life
<p>This dataset contains all source data for Hartig <em>et al </em>2024, PNAS, including:</p> <p>Data files</p> <p>Raw images and TDT ABR/DPOAE files</p> <p>ROIS and raw measurements from quantifications in ImageJ</p> <p>R scripts for data visualization and statistics</p> <p>Reports of statistical analyses including diagnostic qq plots and distributions</p>
Research Data Management and Sharing for images: beautiful fountains require ugly piping!
<p>The consensus is clear: research data funded by public resources should be shared. Globally, the advantages of sharing research data are widely recognized. It promotes transparency and validation, reduces redundant efforts, accelerates discovery, enhances equity, and increases the impact of research through collaboration and efficient use of resources.</p> <p>Image data, however, presents unique challenges. Advanced technologies produce large, multimodal, and multiplexed datasets that span multiple targets across various spatiotemporal scales.</p> <p>This image data comes from a range of sources—such as optical, electron microscopy, and medical imaging—each with specific technical requirements. Managing this complexity is a daunting task without global metadata standardization as well as robust Research Data Management and Sharing (RDMS) cyberinfrastructure to bring it all together.</p> <p>The figure illustrates a common issue: while the importance of the <strong>“beautiful fountains”</strong> of scientific discoveries and medical treatments is widely understood, fewer people recognize the <strong>need to invest in building the often ignored “ugly plumbing” </strong>required to build a strong RDMS cyberinfrastructure.</p> <p> </p>
The Role of Informal Communication in Building Shared Understanding of Non-Functional Requirements in Remote Continuous Software Engineering
<p><strong>Study Information</strong></p> <p>We conducted an ethnography-informed case study of a remote software organization that adopts CSE practices to explore how the organization builds a shared understanding of NFRs. Our study uses semi-structured interviews with a period of observations to answer the following research questions:</p> <p> </p> <ol> <li> <p>How does a remote software organization that adopts CSE practices reach a shared understanding of NFRs?</p> </li> <li> <p>What are the limitations to the shared understanding of NFRs in a remote software organization that adopts CSE practices?</p> </li> <li> <p>What organizational practices for remote collaboration supported a shared understanding of NFRs?</p> </li> </ol> <p> </p> <p>In our study, we refer to our partner organization as Alpha. We used ethnography-informed methods to study Alpha's practices and processes and how they approach a shared understanding of NFRs in their product development. </p> <p> </p> <p><strong>Data Analysis</strong></p> <p>We performed a qualitative study through semi-structured interviews and observations. We use the open, axial and selective coding approach from grounded theory [1] to create our codebook, which informed the results and discussion of our study. Two independent coders held agreement sessions to discuss the codes, consolidate the codes and calculate the inter-rater reliability using the Cohen Kappa's coefficient for measuring observer agreement for categorical data [2]. </p> <p> </p> <p><strong>Artifact Descriptions</strong></p> <p>Our replication package contains three artifacts:</p> <p>1. Codebook.csv: The codebook contains rows for the list of codes used, including the code name and the description of the codes. The codes are the final set of themes derived during the thematic analysis of the interview responses. For example, 'Gaps in communication' means when interview participants describe miscommunications due to team members making assumptions about a project/process or having unclear expectations for a project.</p> <p>2. kappa-scores.csv: This contains the associated kappa values for each round of inter-rater agreement sessions. For each agreement session, the Cohen Kappa's coefficient was calculated from the number of agreements and disagreements of codes within one or two interview transcripts. The Kappa values represent the level of agreement ranging from 0 to 1, where > 0.6 represents substantial agreement. </p> <p>3. Interview-questions.csv: This contains the interview questions used in the semi-structured interviews. Some of the interview questions varied depending on the interviewee’s role, experience and the flow of the interviews.</p> <p><strong> </strong></p> <p><strong>Usefulness</strong></p> <p>We recognize that the value and usefulness of our replication package are yet-to-be-determined. In the interest of transparency of open science, we published our artifacts. We hope that these artifacts are useful to either replicate our findings or to further analyze them to produce other enlightening results.</p> <p><strong> </strong></p> <p><strong>References</strong></p> <p>1. Rashina Hoda, James Noble, and Stuart Marshall. "Grounded theory for geeks". In: Proceedings of the 18th conference on pattern languages of programs. 2011, pp. 1–17.</p> <p>2. J Richard Landis and Gary G Koch. "The measurement of observer agreement for categorical data". In: biometrics (1977), pp. 159–174.</p> <p><strong> </strong></p> <p> </p>
Dataset - AHRC survey of digital/software requirements survey 2021
<p>This is the data collected from the SSI survey of digital/software requirements run for the AHRC in 2021.</p> <p>Personally Identifiable Information (names, email addresses) have been removed, as have other information to minimise the chance of deductive disclosure (job role, institution).</p>
Raw data for the article "Unwrap Them First: Operando Potential-induced Activation Is Required when Using PVP-Capped Ag Nanocubes as Catalysts of CO₂ Electroreduction"
<p>Raw data for the article "Unwrap Them First: Operando Potential-induced Activation Is Required when Using PVP-Capped Ag Nanocubes as Catalysts of CO₂ Electroreduction'', published in Chimia 2021 75:163, doi: <a href="http://doi.org/10.2533/chimia.2021.163">10.2533/chimia.2021.163</a></p> <p>Folder names describe the type of data content.</p>
Evenness response to aridity gradients: Data required for Smith et al. 2022 Oecologia
These are the required data to do all analyses in Smith et al. 2022 in Oecologia using the assembled database across gradients. Included are the richness, evenness, and site level abiotic data. Paper abstract: We sought to understand the role that water availability (expressed as an aridity index) plays in determining regional and global patterns of richness and evenness, and in turn how these water availability-diversity relationships may result in different richness-evenness relationships at regional and global scales. We examined relationships between water availability, richness and evenness for eight grassy biomes spanning broad water availability gradients on five continents. Our study found that relationships between richness and water availability switched from positive for drier (South Africa, Tibet and USA) vs. negative for wetter (India) biomes, though were not significant for the remaining biomes. In contrast, only the India biome showed a significant relationship between water availability and evenness, which was negative. Globally, the richness-water availability relationship was hump-shaped, however, not significant for evenness. At the regional scale, a positive richness-evenness relationship was found for grassy biomes in India and Inner Mongolia, China. In contrast, this relationship was weakly concave-up globally. These results suggest that different, independent factors are determining patterns of species richness and evenness in grassy biomes, resulting in differing richness-evenness relationships at regional and global scales. As a consequence, richness and evenness may respond very differently across spatial gradients to anthropogenic changes, such as climate change.
Result data related to "Tröndle et al (2020) -- Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe"
<p>The dataset contains aggregated result data of our study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 1.2 (2020-09-28)</p> <p>* Add location name to scenario results.<br> * Add scenario results in CSV format, next to already existing NetCDF format.<br> * Remove capacity factors from scenario results.</p> <p>Version 1.1 (2020-07-17)</p> <p>* Remove macOS resource forks cluttering the zip file.</p> <p> </p>
User requirements of Big Earth Data - Survey 2019
<p>The survey was conducted between November 2018 and May 2019 with the aim to find out how users working with large volumes of environmental data interact with data, what challenges they face and how they would like to use cloud-based data services in the future.</p> <p>The term Big Earth Data in this context refers to digital information about Earth, including observations, imagery, derived higher-level products, forecasts and analyses produced by computer models.</p> <p>The survey was conducted in collaboration with the European Centre for Medium-Range Weather Forecasts (ECMWF) and as part of a PhD thesis on "Big Data technologies for environmental and climate data" at University of Marburg, Germany.</p> <p>The results are published in form of two articles:</p> <ul> <li>Wagemann, J., Siemen, S., Seeger, B. and J. Bendix (2021): Users of open Big Earth data - An analysis of the current state. Computers and Geosciences 2021. <a href="https://doi.org/10.1016/j.cageo.2021.104916">doi:10.1016/j.cageo.2021.104916</a></li> <li>Wagemann, J. Siemen, S., Seeger, B. and J. Bendix (2021): A user perspective on future cloud-based services for Big Earth data. International Journal of Digital Earth 2021. doi: <a href="http://doi.org/10.1080/17538947.2021.1982031">10.1080/17538947.2021.1982031</a></li> </ul> <p> </p>
Collected recommendations and requirements for FAIR-enabling services
<p>Within FAIRsFAIR task 2.4, we carried out a structured literature review to extract requirements, recommendations and other desiderata for FAIR-enabling services. This document contains the full list of excerpts, structured and annotated. This work has been used as input for the basic framework on FAIRness of services developed by FAIRsFAIR task 2.4 (see https://doi.org/10.5281/zenodo.4292599).</p>
Dataset - Survey results - Applying Model-based Requirements Engineering in Three Large European Collaborative Projects
<p>This dataset and its associated report contain the results of an online survey on using a model-based requirements engineering approach in three European projects. </p>
Dataset: Results of the CRAFT-OA requirement survey for OJS installation and update toolkit
<p>This dataset is the result of a CRAFT-OA survey that collected requirements for an installation and update toolkit which aims at facilitating a state-of-the-art implementation and operation of the journal software OJS. </p>
Time required for typing numbers
<p>The goal of this test is to see how much time it is needed for one keystroke of a number on the keyboard. By random principle the program displays a number made of three whole digits and one or two decimals separated by comma. Upon starting the test program displays the first number. When the user finishes pressing all number keys (and a comma that separates whole from decimal digits), user has to press Enter so the next number can be displayed. The process is repeated for 5 different numbers.</p> <p>In addition, the number of errors per ten typed numbers entered for each user is counted. The Damerau–Levenshtein algorithm is used to calculate the number of errors. The Damerau–Levenshtein distance between two words is the minimum number of operations (consisting of insertions, deletions or substitutions of a single character, or transposition two adjacent characters) required to change one word into the other. Since this number is usually too small, we counted the number of errors per 10 typed numbers.</p>
ACTIVAGE User needs_requirements and services
<ul> <li>AUC: List of Activage Use cases</li> <li>RUC: List of Reference Ucs</li> <li>Needs: list of DS needs</li> <li>DSReq_list: List of All DS Requirements</li> <li>SLEawRq_list: List of Smart Living Environment for Ageing Well Requirements</li> <li>SLEaw-DS req map: Mapping DSs requirements to ACTIVAGE SLEaw requirements</li> <li>Initial DSs Service list: list provided by DS in Jun 2018 with services, partial description</li> <li>DSs SUBservice list: Decomposition of DS Services in atomic components</li> <li>DSReq_Cl_descr: Desciption of attributes (columns) of DS requirments</li> <li>SLEawReq_Cl_descr: Desciption of attributes (columns) of SLEaw requirments</li> <li>Legenda: This sheet. Include description of sheets and change proposals</li> </ul>
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