Find research datasets worth reusing

opencc-by-4.0Sep 2021View details →

WHUS2-CD+ dataset for sentinel-2 cloud detection validation

WHUS2-CD+ is a cloud validation detection dataset for Sentinel-2A images. WHUS2-CD+ contains 36 manually labeled cloud masks at 10m resolution and corresponding Sentinel-2A images evenly distributed over China mainland. If you use this dataset for your research, please cite us accordingly: #Reference:  [1] J. Li, Z. Wu, Z. Hu, C. Jian, S. Luo, L. Mou, X. Zhu, and M. Molinier, "A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features," in IEEE Transactions on Geoscience and Remote Sensing, 2021. <a href="https://doi.org/10.1109/TGRS.2021.3069641">https://doi.org/10.1109/TGRS.2021.3069641</a>. [2] Z. Wu, J. Li, Y. Wang, Z. Hu and M. Molinier, "Self-Attentive Generative Adversarial Network for Cloud Detection in High Resolution Remote Sensing Images," in IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 10, pp. 1792-1796, Oct. 2020. <a href="https://doi.org/10.1109/LGRS.2019.2955071">https://doi.org/10.1109/LGRS.2019.2955071</a>. The training and testing list is (The challenging senes are marked in bold): <table> <tbody> <tr> <td>Training set</td> </tr> <tr> <td>S2A_MSIL1C_20190714T043711_N0208_R033_T46TFN_20190714T073938</td> </tr> <tr> <td>S2A_MSIL1C_20191219T040151_N0208_R004_T47SQU_20191219T055033</td> </tr> <tr> <td>S2A_MSIL1C_20190630T045701_N0207_R119_T45SWC_20190630T080543</td> </tr> <tr> <td>S2A_MSIL1C_20191215T042151_N0208_R090_T46RGV_20191215T065406</td> </tr> <tr> <td>S2A_MSIL1C_20180930T044701_N0206_R076_T45SXR_20180930T074413</td> </tr> <tr> <td>S2A_MSIL1C_20200317T024541_N0209_R132_T51TWM_20200317T053350</td> </tr> <tr> <td>S2A_MSIL1C_20180816T053641_N0206_R005_T44TKK_20180816T093424</td> </tr> <tr> <td>S2A_MSIL1C_20191023T040821_N0208_R047_T47TQF_20191023T074550</td> </tr> <tr> <td>S2A_MSIL1C_20180824T031541_N0206_R118_T50TKL_20180824T061636</td> </tr> <tr> <td>S2A_MSIL1C_20191118T025011_N0208_R132_T50RMN_20191118T071843</td> </tr> <tr> <td>S2A_MSIL1C_20190916T023551_N0208_R089_T50RQS_20190916T042547</td> </tr> <tr> <td>S2A_MSIL1C_20190819T031541_N0208_R118_T49SFU_20190819T065332</td> </tr> <tr> <td>S2A_MSIL1C_20190815T051651_N0208_R062_T44TPN_20190815T090034</td> </tr> <tr> <td>S2A_MSIL1C_20200410T022551_N0209_R046_T51TXG_20200410T042047</td> </tr> <tr> <td>S2A_MSIL1C_20191002T025551_N0208_R032_T50TQQ_20191002T054113</td> </tr> <tr> <td>S2A_MSIL1C_20180429T032541_N0206_R018_T49SCV_20180429T062304</td> </tr> <tr> <td>S2A_MSIL1C_20200506T024551_N0209_R132_T51UWS_20200506T043639</td> </tr> <tr> <td>S2A_MSIL1C_20200325T034531_N0209_R104_T47RQL_20200325T065315</td> </tr> <tr> <td>S2A_MSIL1C_20190928T031541_N0208_R118_T49RBJ_20190928T061248</td> </tr> <tr> <td>S2A_MSIL1C_20180827T032541_N0206_R018_T48RYV_20180827T062627</td> </tr> <tr> <td>S2A_MSIL1C_20200222T030731_N0209_R075_T49QEE_20200222T060244</td> </tr> <tr> <td>S2A_MSIL1C_20180722T030541_N0206_R075_T49RFP_20180722T060550</td> </tr> <tr> <td>S2A_MSIL1C_20180729T025551_N0206_R032_T49RGL_20180729T055945</td> </tr> <tr> <td>S2A_MSIL1C_20200506T024551_N0209_R132_T50SPE_20200506T052918</td> </tr> <tr> <td>Testing set</td> </tr> <tr> <td>S2A_MSIL1C_20180930T030541_N0206_R075_T49QDD_20180930T060706</td> </tr> <tr> <td>S2A_MSIL1C_20191105T023901_N0208_R089_T51STR_20191105T054744</td> </tr> <tr> <td>S2A_MSIL1C_20190812T032541_N0208_R018_T48RXU_20190812T070322</td> </tr> <tr> <td>S2A_MSIL1C_20190602T021611_N0207_R003_T52TES_20190602T042019</td> </tr> <tr> <td>S2A_MSIL1C_20190328T033701_N0207_R061_T49TCF_20190328T071457</td> </tr> <tr> <td>S2A_MSIL1C_20191001T050701_N0208_R019_T45TXN_20191002T142939</td> </tr> <tr> <td>S2A_MSIL1C_20200416T042701_N0209_R133_T46SFE_20200416T074050</td> </tr> <tr> <td>S2A_MSIL1C_20200528T050701_N0209_R019_T44SPC_20200528T082127</td> </tr> <tr> <td>S2A_MSIL1C_20210207T023851_N0209_R089_T52UCU_20210207T040210</td> </tr> <tr> <td>S2A_MSIL1C_20210126T052111_N0209_R062_T44SNE_20210126T063836</td> </tr> <tr> <td>S2A_MSIL1C_20210102T054231_N0209_R005_T43SFB_20210102T065941</td> </tr> <tr> <td>S2A_MSIL1C_20201206T041141_N0209_R047_T47SMV_20201206T053320</td> </tr> </tbody> </table>

opencc-by-4.0Sep 2021View details →

Extended data table 1: Taqman array card results showing all individual target hits with Ct values and whether validated by conventional microbiology and/or microbial sequencing.

Data from study, protocol published at 10.5281/zenodo.5081880 Extended Data Table 1: TAC results showing all individual target hits with Ct values and whether validated by conventional microbiology and/or microbial sequencing. (BAL: bronchoalveolar lavage.) *Not included in validation numbers as duplicate at sub-species or genus level detection, **MecA was not included in validation numbers. TAC hits which did not pass the internal quality control standards required for reporting are indicated by (not reported). Samples which did not undergo sequencing indicated by ND. (✓) indicates low confidence hits from sequencing.

opencc-by-4.0Sep 2021View details →

Dataset used for validation of the Danish 20-item Spiritual Needs Questionnaire.

This material comprises the datasets to support the validation works of the Danish 20-item Spiritual Needs Questionnaire (DA-SpNQ-20). There is data on the questionnaire instruments: Spiritual Needs Questionnaire (danish translation of questionnaire will be published in Zenodo later) and the World Health Organization Well Being Index (WHO-5). All data has been fully anonymized and thus stripped of age, gender and religious/spiritual denominations. The sample is a convenience sample of (healthy) adult Danes with a large body of university students in the sample. Mean age of sample is 43,9 (sd: 16,3) and 73% were women. Information on the SpNQ is available at www.spiritualneeds.net Files included: (1) dataset of the full sample. (2) dataset of a randomly selected half (sample A) that was used for the exploratory factor analysis. (3) dataset of a randomly selected half (sample B) that was used for the confirmatory factor analysis.

opencc-zeroOct 2021View details →

Data from: Validating marine Devonian biogeography: a study in bioregionalization

The Devonian record presents an opportunity to test and validate an existing marine bioregionalisation. This study is the first to use comparative biogeography and phylogenetic data to test Devonian bioregionalisation. Proposed in the 1960's the Old World, Eastern Americas, and Malvinokaffric realms have been the functional standard within marine Devonian Biogeography. Data from 32 published phylogenies of Devonian marine taxa and a database of c.800 occurrences were analysed using phylogenetic software to test for area monophyly. The taxic occurrences within the current database were then tested against total fauna Devonian occurrences with the Palaeobiology Database to indicate differences in sampling. Results indicate that the current Devonian bioregionalisation is not representative of natural areas and requires revision. The result highlights areas that are most robust from which the study makes recommendations to improve the process and diagnosis of Devonian biogeographic areas. We found that legacy issues within palaeontology are evident within the results and their interpretation. The validation of bioregionalisation and process is critical to the advancement of biogeography and palaeontology. The sensitivity of bioregionalisation shows biotic and geographical relationships, how life and earth evolved together, and how geographic bias is evident in scientific process.

opencc-by-4.0Oct 2021View details →

Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders: Synthetic dataset for validation of trace gas retrieval algorithms

This data set is described in detail in a paper submitted to AMTD: Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders - Part I: Synthetic dataset for validation of trace gas retrieval algorithms by Claudia Emde, Huan Yu, Arve Kylling, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and Bernhard Mayer   The subdirectory boxcloud includes synthetic reflectances for clearsky, 1D cloud and box cloud. The subdirectory les_cloud includes synthetic reflectances for the LES cloud scenario for low earth orbit (leo) and geostationary orbit (geo). All data are provided in netcdf format.

opencc-zeroOct 2021View details →

Development and validation of the Health Belief Model questionnaire to promote smoking cessation for nasopharyngeal cancer prevention: a cross-sectional study

Objective: Nasopharyngeal cancer (NPC) risk factors caused by lifestyle choices are substantial yet avoidable. Using the Health Belief Model as a conceptual framework, this study develops and validates a questionnaire to predict smokers' intentions to quit in Sarawak, Malaysia (HBM). Design: A cross sectional study. Setting: Urban and suburban areas in Sarawak, Malaysia. Participants: Following a thorough literature study, the preliminary items for the instrument were created. Prior to beginning the content validity by a panel of 10 experts, the instrument was translated into the Malay language utilising the forward-backwards approach. 10 smokers carried out face validity in both quantitative and qualitative ways. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to assess the instrument's concept validity. Phase 1 of the EFA involved 100 smokers, whereas phase 2 of the CFA involved 171 smokers. Cronbach's alpha coefficients were used to measure internal consistency and assess reliability. Results: The factor loading of each item remained within the acceptable threshold in the exploratory stage. The final revised CFA yielded the following model fit indices that suitably fitted the seven-factor model: Chi Square: 641.705; df= 500; P< 0.001; CFI = 0.953; TLI: 0.948; RMSEA= 0.041. With the exception of one pairwise construct, satisfactory convergent validity and divergent validity were demonstrated. Phases 1 and 2 both have Cronbach's alpha values over 0.7, suggesting acceptable internal reliability. Conclusions: The study confirmed the validity and reliability of the instrument in predicting smokers' tendency to adopt healthy behaviour of smoking cessation to lower risk of developing cancer. The instrument consists of 34 items, dividing into two sections: 6 HBM components and health behavioural intention. The instrument is regarded as innovation and can be applicable in other smoking-related malignancies in different susceptible populations and geographical locations.

opencc-by-4.0Dec 2018View details →

``HiPen'': a new dataset for validating (S)QM/MM free energy simulations

Calculating free energy differences between levels of theory (i.e., \(\Delta A^{low \to high}\)) is integral to performing indirect (S)QM/MM free energy simulations. However, connecting levels of theory via free energy simulations has proved difficult due to (1) bond/angle degrees of freedom, (2) dihedral degrees of freedom, and (3) solvent arrangement differences between levels of theory, largely due to partial charge differences between levels of theory. In order to improve calculation of (S)QM/MM free energy simulations, the free energy simulation community should begin to compare methods based on convergence success relative to overall computational time and resource requirements. We have begun to compile such a dataset by calculating \(\Delta A^{MM \to SCC-DFTB}\) in gas phase for 22 drug-like molecules, as seen in our recent publication, Kearns, et al. 2018, Molecules, Submitted, and we hope that future practitioners will do the same. With this work we hope to provide a standard for comparison for future FES methodologies; additionally, in the near future we hope to continue to add to this dataset including results in more complicated environments such as in solution and in enzyme. All data can be found in our publication and in the accompanying Supporting Information; raw data (such as simulation trajectories and raw data files) can be made available upon request. The purpose of this dataset publication is to make available all starting coordinates, topologies, parameter sets, and input files necessary to replicating the results published in our work.

opencc-by-4.0Oct 2022View details →

Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America : Dataset

This dataset is linked to the paper “Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-16-1661-2023). It contains the installer of the model, its user guide, as well as all the input files (inventory, thinning, meteorology and soil horizons files for each stand used in the evaluation and calibration steps), the R scripts and associated data used to analyse the model outputs.

opencc-by-4.0Nov 2022View details →

Figs 1–7. Ammophila turkestana Kohl. 1 in Ammophila turkestana Kohl, 1906 (Hymenoptera: Sphecidae), a valid species

Figs 1–7. Ammophila turkestana Kohl. 1 – mesosoma, lateral view; 2 – mesosoma,

opencc-zeroNov 2022View details →

Data for: Whole Animal Feed FLat (WAFFL): A complete and comprehensive validation of a novel high-throughput fly experimentation system

Non-mammalian model organisms have been essential for our understanding of the mechanisms that control development, disease, and physiology, but they are underutilized in pharmacological and toxicological phenotypic screening assays due to their low throughput in comparison with cell-based screens. To increase the utility of using Drosophila melanogaster in screening, we designed the Whole Animal Feeding FLat (WAFFL), a novel, flexible, and complete system for feeding, monitoring, and assaying flies in a high-throughput format. Our 3-D printed system is compatible with inexpensive and readily available, commercial 96-well plate consumables and equipment. Experimenters can change the diet at will during the experiment and video record for behavior analysis, enabling precise dosing, measurement of feeding, and analysis of behavior in 96-well plate format.

opencc-by-4.0Nov 2022View details →

Woerthersee sediment core data for the publication "Validation of seismic hazard curves using a calibrated 14 ka lacustrine record in the Eastern Alps, Austria"

This dataset comprises sediment core data of Wörthersee, a lake in the Eastern European Alps, Austria. Together with a dataset comprising the seismic data (10.5281/zenodo.6479186), this is the basis for the publication Daxer et al. "Validation of seismic hazard curves using a calibrated 14 ka lacustrine record in the Eastern Alps, Austria". The files contain the following data: <ul> <li>Core images Long Cores.zip: Core images of the Wörthersee Kullenberg-type long cores acquired with an ITRAX core scanner</li> <li>Core images Short Cores.zip: Core images of the Wörthersee gravity short cores (hammer-coring or trigger cores of the Kullenberg system) acquired with an ITRAX core scanner; provided as .tif files</li> <li>CT data WOER18-L5-X-Dicom.zip: X-ray computed tomography data acquired with a Siemens SOMATOM Definition AS (voxel size 0.2 x 0.2 x 0.3 mm); provided in DICOM format</li> <li>MSCL data.zip: Data acquired with a Geotek Multi-sensor core logger (e.g. magnetic susceptibility and gamma density); provided as Excel spreadsheets</li> <li>XRF data.zip: X-ray fluorescence data acquired with a ITRAX core scanner; provided in .csv format</li> </ul>

opencc-by-4.0Nov 2022View details →

UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation

This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density.  Below are the description of each data file: <ul> <li>high_density_both_lane_raw_AI_speed.csv <ul> <li>Description: extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li>Data Fields: <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_AI_count_1min_bin.csv <ul> <li>Description: AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_AI_count_1min_bin.csv <ul> <li>Description: AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>low_density_inner_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>low_density_inner_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>low_density_outer_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>low_density_outer_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>

opencc-zeroNov 2022View details →

Validation of Sepsis-3 using survival analysis and clinical evaluation of quick SOFA, SIRS, and burn-specific SIRS for sepsis in burn patients with suspected infection

Purpose: Sepsis-3 is a life-threatening organ dysfunction caused by dysregulated host responses to infection; and defined using the Sepsis-3 criteria, introduced in 2016, however, the criteria need to be validated in specific clinical fields. We investigated mortality prediction and compared the diagnostic performance of quick Sequential Organ Failure Assessment (qSOFA), systemic inflammatory response syndrome (SIRS), and burn-specific SIRS (bSIRS) in burn patients. Methods: This single-center retrospective cohort study examined burn patients in Seoul, Korea during January 2010–December 2020. Overall, 1,391 patients with suspected infection were divided into four sepsis groups using SOFA, qSOFA, SIRS, and burn-specific SIRS. Results: Hazard ratios (HRs) of all unadjusted models were statistically significant; however, the HR (0.726, p = 0.0080.001) in the SIRS ≥2 group is below 1. In the adjusted model, HRs of the SOFA ≥2 (2.426, p < 0.001), qSOFA ≥2 (7.198, p < 0.001), and SIRS ≥2 (0.575, p < 0.001) groups were significant. The diagnostic performance of dichotomized qSOFA, SIRS, and bSIRS for sepsis was defined by the Sepsis-3 criteria. The mean onset day was 4.13±2.97 according to Sepsis-3. The sensitivity of SIRS (0.989, 95% confidence interval [CI]: 0.982–0.994) was higher than that of qSOFA (0.841, 95% CI: 0.819–0.861) and bSIRS (0.803, 95% CI: 0.779–0.825). Specificities of qSOFA (0.929, 95% CI: 0.876–0.964) and bSIRS (0.922, 95% CI: 0.868–0.959) were higher than those of SIRS (0.461, 95% CI: 0.381–0.543). Conclusion: Sepsis-3 is a good alternative diagnostic tool because it reflects sepsis severity without delaying diagnosis. SIRS showed higher sensitivity than qSOFA and bSIRS and may therefore more adequately diagnose sepsis.

opencc-by-4.0Dec 2022View details →

Dataset supporting publication: "Development and Validation of Analytical Solutions for Earth Basket (Spiral) Heat Exchangers"

Dataset supporting publication: “Development and Validation of Analytical Solutions for Earth Basket (Spiral) Heat Exchangers” (publication available for download:<a href="https://zenodo.org/record/7274096#.Y2JpLnbMJPY">GEOFIT Zenodo</a>). This paper presents an analytical solution and its validation for earth basket (vertical spiral) ground heat exchangers. The model, based on the well known Finite Line Source Equation, accounts for the heat exchanger pipe diameter and seasonally varying near surface temperature. For computational efficiency the standard approach of using G-functions has been implemented as well. The analytical model is validated based on laboratory experiments and extensive CFD analysis.

opencc-by-4.0Dec 2021View details →

Scenarios used for the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION )

This dataset contains the scenarios used for one of the validation of the ARTIMATION project: Conflict Detection and Resolution (CD&R) use case. It contains the validation exercice developped at ENAC using real flown data from 2016, and modifying it to respect our constraints. Inside, one can find: -The file "exo_artimation.txt" a file containing the whole scenario, that can be played using REJEU platform. -10 individual files, extracted from "exo_artimation.txt" containing conflicting aircrafts. Those files where used in other steps of the ARTIMATION project, notably in the solution dataset, the heatmatrix, heatmap, storyboard developped (all linked in related identifiers).

opencc-by-4.0Dec 2022View details →

GSDR-I Global Sub-Daily Precipitation Indices - Validation Figures

Validation figures for gauge and gridded time series indices and supplementary statistics from GSDR-I (an observation-based dataset of global sub-daily precipitation indices)

Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds

The tables in this repository represent the data used in the figures and analyses of the paper "Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds", published in the Planetary Science Journal. The provided data was collected between 2020 and 2022. <ul> <li>AllResults.xlsx: compilation of tables 4, 5, 6, and 7 on the paper.</li> <li>WarmA1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>WarmA2.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>CryoA1.xlsx: data presented in tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth Estimation [m], Power [W]".</li> <li>CryoB1.xlsx: data presented in figures 9 and 10, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB3.xlsx: data presented in figures 6, 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC3.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Truncated Coarse Depth [m], Power [W]".</li> </ul>

Spatial Plus Cross-Validation experiments datasets and codes

This zip file includes all materials of Spatial Plus Cross-Validation experiments.  They are ordered by the first number of folder's name. In each folder, the order of running code scripts are labeled by the first number of code's name.

EUREKA WP1 validation survey results

Raw results of the EUREKA project survey on how data and knowledge are stored and shared in Horizon 2020 multi-actor projects in agriculture and forestry.

EUREKA WP1 validation survey results

Raw results of the EUREKA project survey on how data and knowledge are stored and shared in Horizon 2020 multi-actor projects in agriculture and forestry.