Find research datasets worth reusing

opencc-by-sa-3.0Oct 2022View details →

The global water resources and use model WaterGAP v2.2e: streamflow calibration and evaluation data basis

The data collection covers the streamflow data used for calibrating and validating the global water use and availability model WaterGAP v2.2e. The collection is a result of a data selection, assessment and merge effort from three data sources (GRDC, GSIM, ADHI) for in total 1509 stations. The stations are co-registered to the DDM30 (Döll & Lehner 2002) according their best hydrological fit. Please see the readme.md for details. Version 1.1 contains now the shapefiles as a zip instead single files which makes downlad more convenient.

opencc-by-4.0Oct 2022View details →

Supporting Information - Fabbri et al. 2022 - Evaluation of sugar feedstocks for bio-based chemicals: A consequential, regionalized life cycle assessment

The supporting information of the journal article "Evaluation of sugar feedstocks for bio-based chemicals: A consequential, regionalized life cycle assessment" from Fabbri et al. (2022) includes one file with the following content: S1 Details of consequential modelling: feedstock     S1.1 Identification type of changes (demand or supply)     S1.2 Identification of constrains in the market     S1.3 Identification of product substitutions     S1.4 Identification of affected production technology     S1.5 Identification of marginal crop and marginal supplier S2 Details of consequential modelling: by-products S3 Model parameters and unit processes     S3.1 Sugar beet     S3.2 Sugar cane     S3.3 Wheat     S3.4 Maize     S3.5 Wood     S3.6 Residual woodchips and sawdust S4 Review of land use change accounting methods     S4.1 Direct land use change (dLUC)     S4.2. Indirect land use change (iLUC) S5 Additional results     S5.1 Influence of spatial differentiation in LCIA     S5.2 Influence of indirect land use change (iLUC) S6 References

Evaluating noninvasive methods for estimating cestode prevalence in a wild carnivore population

This repository holds the datasets and R code files needed to run the models in: Brandell et al., 2022. Evaluating noninvasive methods for estimating cestode prevalence in a wild carnivore population. PLOS ONE. Excel files have associated KEYs for each data column; CSVs are analyzed with their associated R code.

opencc-by-4.0Jul 2022View details →

Sea Level Datasets for HIDRA2 Training and Evaluation

HIDRA2 is a state-of-the-art deep neural model for sea-level prediction based on past sea level observations and future tidal and atmospheric forecasts. Training and evaluation was performed on the published sea level datasets.

Supplementary Figures: Effectiveness Evaluation of a UVC-Photoinactivator against Selected ESKAPE-E Pathogens

Figure S1. Front view of the upper platform of the Sanitech UVC shoe sole decontaminator equipment. Figure S2. Arrangement of ATCC bacterial strains in the microplate for exposure to the UVC system and control group (no treatment). Abbreviations: Sa, S. aureus (ATCC 6538); Pa, P. aeruginosa (ATCC 15442); Se, S. enterica (ATCC 10708); Ec, E. coli (ATCC 25922); Ab, A. baumannii (ATCC 19606); Ef, E. faecalis (ATCC 29212); PBS, sterile phosphate buffered saline; L, Luria Bertani broth.

Child 3D anthropometry evaluation datasets

Background: An efficacy evaluation of the AutoAnthro system to measure child (0–59 months) anthropometry in USA found three-dimensional imaging performed as well as gold-standard manual measurements for biological plausibility and precision. Objectives: Conduct an effectiveness evaluation of the accuracy of the AutoAnthro system to measure 0–59 months child anthropometry in population-based surveys and surveillance systems in households in Guatemala and Kenya, and in hospitals in China. Methods: The evaluation was done using health or nutrition surveillance system platforms among 600 children 0–59 months (Guatemala, Kenya) and 300 children 0-23 months (China). Field team anthropometrists and their assistants collected from each child manual and scan anthropometric measurements including length/height, mid-upper arm circumference (MUAC), and head circumference (HC, China only). An anthropometry expert and assistant later collected both manual and scan anthropometric measurements on the same child. The expert manual measurements were considered the standard compared to field team scans. Results: Overall, in Guatemala, Kenya and China, respectively, for inter-rater accuracy, average bias for length/height was -0.3 cm, -1.9 cm, -6.2 cm; for MUAC was 0.9 cm, 1.2 cm, -0.8 cm; for HC was 2.4 cm; the inter-technical error of measurement (TEM) for length/height was 2.8 cm, 3.4 cm, 5.5 cm; for MUAC was 1.1 cm, 1.5 cm, 1.0 cm; for HC was 2.8 cm. For intra-rater precision, absolute mean difference and intra-TEM were 0.1 cm for all countries for all manual measurements. For scan, overall, absolute mean difference ranged for length/height 0.4-0.6 cm; MUAC 0.1-0.1 cm; HC was 0.4 cm. For intra-TEM, length/height was 0.5 cm in Guatemala and China, 0.7 cm in Kenya, and other measurements were <0.3 cm. Conclusions: Understanding the factors that cause the many poor scan results and how to correct them will be needed prior to using this instrument in routine population-based survey and surveillance systems.

Data for: Evaluating Golden-winged Warbler use of alder and aspen communities managed with shearing in the western Great Lakes

Best management practices are often written by researchers to guide land managers and landowners in the creation of habitat for wildlife species of interest. These documents are based on research evaluating the habitat needs of a species, but also describe tools and strategies managers can implement to create or restore desired conditions. Shrub and sapling shearing is a management practice often used to improve habitat for early-successional species, yet little monitoring or research has focused on wildlife response to shearing. The goal of this research was to formally evaluate the effect of shrub and sapling shearing as a best management strategy for Golden-winged Warbler (Vermivora chrysoptera) conservation at a regional scale. Specifically, we surveyed for male Golden-winged Warblers during the breeding season in sheared sites and untreated reference sites across portions of the western Great Lakes to assess the effects of 1) management status (i.e., sheared aspen or alder vs un-treated sites), and 2) the patch-level vegetation characteristics on male abundance. We found that male Golden-winged Warbler abundance was twice as high in sheared sites than in mature reference sites and peaked when sapling cover was ~40%. Male abundance was also negatively associated with percent cover of forbs and non-vegetated ground. These findings highlight the importance of patch-level heterogeneity when implementing shearing treatments for Golden-winged Warblers, and demonstrate the potential need for pre-treatment site assessments to help focus conservation efforts for this species. Ultimately, our results support the use of a site-specific, nuanced approach to shearing implementation to maximize cost efficiency and desired species outcomes.

Data for: Evaluating the impact of anatomical partitioning on summary topologies obtained with Bayesian phylogenetic analyses of morphological data

Morphological data are a fundamental source of evidence to reconstruct the Tree of Life, and Bayesian phylogenetic methods are increasingly being used for this task. Bayesian phylogenetic analyses require the use of evolutionary models, which have been intensively studied in the past few years, with significant improvements to our knowledge. Notwithstanding, a systematic evaluation of the performance of partitioned models for morphological data has never been performed. Here we evaluate the influence of partitioned models, defined by anatomical criteria, on the precision and accuracy of summary tree topologies considering the effects of model misspecification. We simulated datasets using partitioning schemes, trees, and other properties obtained from two empirical datasets, and conducted Bayesian phylogenetic analyses. Additionally, we reanalysed 32 empirical datasets for different groups of vertebrates, applying unpartitioned and partitioned models, and, as a focused study case, we reanalysed a dataset including living and fossil armadillos, testing alternative partitioning hypotheses based on functional and ontogenetic modules. We found that, in general, partitioning by anatomy has little influence on summary topologies analysed under alternative partitioning schemes with a varying number of partitions. Nevertheless, models with unlinked branch lengths, which account for heterotachy across partitions, improve topological precision at the cost of reducing accuracy. In some instances, more complex partitioning schemes, led to topological changes, as tested for armadillos, mostly associated with models with unlinked branch lengths. We compare our results with other empirical evaluations of morphological data and those from empirical and simulation studies of partitioning of molecular data, considering the adequacy of anatomical partitioning relative to alternative methods of partitioning morphological datasets.

Adaptive, randomized, non-inferiority trial to evaluate the efficacy of monoclonal antibodies in outpatients with mild or moderate COVID-19

The dataset is based on the results of a trial called MANTICO (Clinical trial number NCT05205759). The study is a non-inferiority randomised controlled trial comparing the clinical efficacy of bamlanivimab/etesevimab, casirivimab/imdevimab, and sotrovimab in outpatients aged 50 or older with early COVID-19. The primary outcome was COVID-19 progression (hospitalisation, need of supplemental oxygen therapy, or death through day 14).

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>

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.

Benchmarks for the Urban-PLUMBER model evaluation project Phase 1 (AU-Preston)

AU-Preston benchmarks for the Urban-PLUMBER projectThese timeseries data are associated with the study:<blockquote>Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results</blockquote>doi: <a href="https://doi.org/10.1002/qj.4589">https://doi.org/10.1002/qj.4589</a>These are empirical models of various complexity used as benchmarks to evaluate land surface models in the project. The benchmarking process follows the methods in the PLUMBER project (Best et al., 2015).BenchmarksPhysically based<ol><li>Manabe_1T: A simple 'slab and bucket' model (Fig. 1a) based on physical principles (i.e. conservation of energy, mass and momentum). The impervious (built) fraction is simulated using a one-tile slab scheme (Best, 2005). For the pervious fraction a simple representation allows precipitation to fill a store which overflows when full, and otherwise freely evaporate (Manabe, 1969). At each timestep, the impervious and pervious tile outputs are calculated and aggregated with a weighted mean.</li></ol>Empirical (out-of-sample)<ol><li>REG1-SWdown: Linear regression with one variable (SWdown, e.g. Fig. 1b) is used separately to predict SWup, LWup, Qh, Qle, and Qtau. At night predicted values are constant where SWdown = 0.</li><li>REG2-SWdown-Tair: Two-variable (SWdown and Tair) linear regression (e.g. Fig. 5c) provides some information at night and more generally for variables closely dependent on temperature (e.g. Lwup and Qh).</li><li>KM3-SWdown-Tair-RH: Following PLUMBER's conceptual arguments, three predictor variables (SWdown, Tair and RH data) are split into three (low, medium and high) to create 3^3=27 groups, for which independent regressions are trained. K-means clustering is used to determine the training data groups (e.g. Fig. 1d). To use this piecewise regression benchmark, at each time step the input data's proximity to one of the 27 cluster centroids is determined to select the regression to apply. This benchmark equates to PLUMBER's EMP3KM27 (Best et al., 2015), which based on common metrics outperformed all their participating land surface models when predicting sensible and latent heat fluxes across 20 sites.</li></ol>Empirical (in-sample)<ol><li>KM3-IS-SWdown-Tair-RH: This follows the KM3-SWdown-Tair-RH method, but trained with in-sample data only (i.e. AU-Preston). This should outperform an out-of-sample model because of the reuse of the forcing data, but performance is expected to degrade if applied to dissimilar conditions (i.e. another site).</li><li>KM4-IS-SWdown-Tair-RH-Wind: The k-means approach is applied again, but with an additional variable (wind speed), which increases the clusters to 81 (3^4) given the above rationale. Wind speed provides information that helps to predict turbulent heat and momentum fluxes.</li></ol>AuthorsEmperical benchmarks were developed by Mathew Lipson: <a href="https://orcid.org/0000-0001-5322-1796">https://orcid.org/0000-0001-5322-1796</a>The physical benchmark was developed by Martin Best: <a href="https://orcid.org/0000-0003-4468-876X">https://orcid.org/0000-0003-4468-876X</a>Co-authors for the associated manuscript are:Mathew Lipson, Sue Grimmond, Martin Best, Gab Abramowitz, Andrew Coutts, Nigel Tapper, Jong-Jin Baik, Meiring Beyers, Lewis Blunn, Souhail Boussetta, Elie Bou-Zeid, Martin G. De Kauwe, Cécile de Munck, Matthias Demuzere, Simone Fatichi, Krzysztof Fortuniak, Beom-Soon Han, Maggie Hendry, Yukihiro Kikegawa, Hiroaki Kondo, Doo-Il Lee, Sang-Hyun Lee, Aude Lemonsu, Tiago Machado, Gabriele Manoli, Alberto Martilli, Valéry Masson, Joe McNorton, Naika Meili, David Meyer, Kerry A. Nice, Keith W. Oleson, Seung-Bu Park, Michael Roth, Robert Schoetter, Andres Simon, Gert-Jan Steeneveld, Ting Sun, Yuya Takane, Marcus Thatcher, Aristofanis Tsiringakis, Mikhail Varentsov, Chenghao Wang, Zhi-Hua WangReferencesObservational dataLipson, M., Grimmond, S., Best, M., Chow, W., Christen, A., Chrysoulakis, N., Coutts, A., Crawford, B., Earl, S., Evans, J., Fortuniak, K., Heusinkveld, B. G., Hong, J.-W., Hong, J., Järvi, L., Jo, S., Kim, Y.-H., Kotthaus, S., Lee, K., Masson, V., McFadden, J. P., Michels, O., Pawlak, W., Roth, M., Sugawara, H., Tapper, N., Velasco, E., and Ward, H. C.: Data for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project, <a href="https://doi.org/10.5281/zenodo.7104984">https://doi.org/10.5281/zenodo.7104984</a>, 2022.Benchmark referencesBest, M. J., Abramowitz, G., Johnson, H. R., Pitman, A. J., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P. A., Dong, J., Ek, M., Guo, Z., Haverd, V., Hurk, B. J. J. van den, Nearing, G. S., Pak, B., Peters-Lidard, C., Santanello, J. A., Stevens, L., and Vuichard, N.: The Plumbing of Land Surface Models: Benchmarking Model Performance, Journal of Hydrometeorology, 16, 1425–1442, <a href="https://doi.org/10.1175/JHM-D-14-0158.1">https://doi.org/10.1175/JHM-D-14-0158.1</a>, 2015.Best, M. J.: Representing urban areas within operational numerical weather prediction models, Boundary-Layer Meteorol, 114, 91–109, <a href="https://doi.org/10.1007/s10546-004-4834-5">https://doi.org/10.1007/s10546-004-4834-5</a>, 2005.Manabe, S.: CLIMATE AND THE OCEAN CIRCULATION: I. THE ATMOSPHERIC CIRCULATION AND THE HYDROLOGY OF THE EARTH'S SURFACE, Monthly Weather Review, 97, 739–774, <a href="https://doi.org/10.1175/1520-0493(1969)097">https://doi.org/10.1175/1520-0493(1969)097</a><0739:CATOC>2.3.CO;2, 1969.

Dataset II related to the publication: In silico Evaluation of the Thr58-associated Conserved Water with KRAS Switch-II Pocket Binders

Desmond trajectories of simulations conducted with TIP3P water model related to the publication: Leini R, Pantsar T: In Silico Evaluation of the Thr58-Associated Conserved Water with 2 KRAS Switch-II Pocket Binders. J. Chem. Inf. Model. [accepted] https://doi.org/10.1021/acs.jcim.2c01479 <ul> <li>Individual .zip files contain the Desmond trajectories and -out.cms -files.</li> </ul> Related datasets: 10.5281/zenodo.7656467 and 10.5281/zenodo.7342311

Hypothalamic interaction with reward-related regions during subjective evaluation of foods

The reward system implemented in the midbrain, ventral striatum, orbitofrontal cortex, and ventromedial prefrontal cortex evaluates and compares various types of rewards given to the organisms. It has been suggested that autonomic factors influence reward-related processing via the hypothalamus, but how the hypothalamus modulates the reward system remains elusive. In this functional magnetic resonance imaging study, the hypothalamus was parcellated into individual hypothalamic nuclei performing different autonomic functions using boundary mapping parcellation analyses. The effective interaction during subjective evaluation of foods in a reward task was then investigated between the human hypothalamic nuclei and the reward-related regions. We found significant brain activity decrease in the paraventricular nucleus (PVH) and lateral nucleus in the hypothalamus in food evaluation compared with monetary evaluation. A psychophysiological interaction analysis revealed dual interactions between the PVH and (1) midbrain region and (2) ventromedial prefrontal cortex, with the former correlated with the stronger tendency of participants toward food-seeking. A dynamic causal modeling analysis further revealed unidirectional interactions from the PVH to the midbrain and ventromedial prefrontal cortex. These results suggest that the PVH in the human hypothalamus interacts with the reward-related regions in the cerebral cortex via multiple pathways (i.e., the midbrain pathway and ventromedial prefrontal pathway) to evaluate rewards for subsequent decision-making.

Dataset III related to the publication: In silico Evaluation of the Thr58-associated Conserved Water with KRAS Switch-II Pocket Binders

Desmond trajectories of simulations conducted with TIP4P water model related to the publication: Leini R, Pantsar T: In Silico Evaluation of the Thr58-Associated Conserved Water with 2 KRAS Switch-II Pocket Binders. J. Chem. Inf. Model. [accepted] https://doi.org/10.1021/acs.jcim.2c01479 <ul> <li>Individual .zip files contain the Desmond trajectories and -out.cms -files.</li> </ul> Related datasets: 10.5281/zenodo.7656467 and 10.5281/zenodo.7341954

Evaluation of Ortho VITROS and Roche Elecsys S and NC immunoassays for SARS-CoV-2 serosurveillance applications

SARS-CoV-2 seroprevalence studies are instrumental in monitoring epidemic activity and require well-characterized, high-throughput assays, and appropriate testing algorithms. The U.S. Nationwide Blood Donor Seroprevalence Study performed monthly cross-sectional serological testing from July 2020 to December 2021, implementing evolving testing algorithms in response to changes in pandemic activity. With high vaccine uptake, anti-Spike (S) reactivity rates reached > 80% by May 2021, and the study pivoted from reflex Roche anti-nucleocapsid (NC) testing of Ortho S-reactive specimens to parallel Ortho S/NC testing. We evaluated the performance of the Ortho NC assay as a replacement for the Roche NC assay and compared performance of parallel S/NC testing on both platforms. Qualitative and quantitative agreement of Ortho NC with Roche NC assays was evaluated on pre-selected S/NC concordant and discordant specimens. All 190 Ortho S+/Roche NC+ specimens were reactive on the Ortho NC assay; 34% of 367 Ortho S+/Roche NC- specimens collected prior to vaccine availability and 43% of 37 Ortho S-/Roche NC+ specimens were reactive on the Ortho NC assay. Performance of parallel S/NC testing using Ortho and Roche platforms was evaluated on 200 specimens collected in 2019 and 3,903 study specimens collected in 2021. All 200 pre-COVID 2019 specimens tested negative on the four assays. Agreement of S and NC reactivity on specimens was 96.4% (3,769/3,903); most discordant results had reactivity close to the cutoffs on the alternate assays. These findings, and higher efficiency and throughput, support use of parallel S/NC testing on either Roche or Ortho platforms for large serosurveillance studies.

opencc-by-nc-sa-4.0Sep 2022View details →

SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis

This is the public release of the Samsung Open Mean Opinion Scores (SOMOS) dataset for the evaluation of neural text-to-speech (TTS) synthesis, which consists of audio files generated with a public domain voice from trained TTS models based on bibliography, and numbers assigned to each audio as quality (naturalness) evaluations by several crowdsourced listeners. Description The SOMOS dataset contains 20,000 synthetic utterances (wavs), 100 natural utterances and 374,955 naturalness evaluations (human-assigned scores in the range 1-5). The synthetic utterances are single-speaker, generated by training several Tacotron-like acoustic models and an LPCNet vocoder on the LJ Speech voice public dataset. 2,000 text sentences were synthesized, selected from Blizzard Challenge texts of years 2007-2016, the LJ Speech corpus as well as Wikipedia and general domain data from the Internet. Naturalness evaluations were collected via crowdsourcing a listening test on Amazon Mechanical Turk in the US, GB and CA locales. The records of listening test participants (workers) are fully anonymized. Statistics on the reliability of the scores assigned by the workers are also included, generated through processing the scores and validation controls per submission page. To listen to audio samples of the dataset, please see <a href="https://innoetics.github.io/publications/somos-dataset/index.html">our Github page</a>. The dataset release comes with a carefully designed train-validation-test split (70%-15%-15%) with unseen systems, listeners and texts, which can be used for experimentation on MOS prediction. This version also contains the necessary resources to obtain the transcripts corresponding to all dataset audios. Terms of use <ul> <li>The dataset may be used for research purposes only, for non-commercial purposes only, and may be distributed with the same terms.</li> <li>Every time you produce research that has used this dataset, please cite the dataset appropriately.</li> </ul> Cite as: <pre><code>@inproceedings{maniati22_interspeech,   author={Georgia Maniati and Alexandra Vioni and Nikolaos Ellinas and Karolos Nikitaras and Konstantinos Klapsas and June Sig Sung and Gunu Jho and Aimilios Chalamandaris and Pirros Tsiakoulis},   title={{SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis}},   year=2022,   booktitle={Proc. Interspeech 2022},   pages={2388--2392},   doi={10.21437/Interspeech.2022-10922} } </code></pre> References of resources & models used Voice & synthesized texts: K. Ito and L. Johnson, “The LJ Speech Dataset,” https://keithito.com/LJ-Speech-Dataset/, 2017. Vocoder: J.-M. Valin and J. Skoglund, “LPCNet: Improving neural speech synthesis through linear prediction,” in Proc. ICASSP, 2019. R. Vipperla, S. Park, K. Choo, S. Ishtiaq, K. Min, S. Bhattacharya, A. Mehrotra, A. G. C. P. Ramos, and N. D. Lane, “Bunched lpcnet: Vocoder for low-cost neural text-to-speech systems,” in Proc. Interspeech, 2020. Acoustic models: N. Ellinas, G. Vamvoukakis, K. Markopoulos, A. Chalamandaris, G. Maniati, P. Kakoulidis, S. Raptis, J. S. Sung, H. Park, and P. Tsiakoulis, “High quality streaming speech synthesis with low, sentence-length-independent latency,” in Proc. Interspeech, 2020. Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio et al., “Tacotron: Towards End-to-End Speech Synthesis,” in Proc. Interspeech, 2017. J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan et al., “Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions,” in Proc. ICASSP, 2018. J. Shen, Y. Jia, M. Chrzanowski, Y. Zhang, I. Elias, H. Zen, and Y. Wu, “Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling,” arXiv preprint arXiv:2010.04301, 2020. M. Honnibal and M. Johnson, “An Improved Non-monotonic Transition System for Dependency Parsing,” in Proc. EMNLP, 2015. M. Dominguez, P. L. Rohrer, and J. Soler-Company, “PyToBI: A Toolkit for ToBI Labeling Under Python,” in Proc. Interspeech, 2019. Y. Zou, S. Liu, X. Yin, H. Lin, C. Wang, H. Zhang, and Z. Ma, “Fine-grained prosody modeling in neural speech synthesis using ToBI representation,” in Proc. Interspeech, 2021. K. Klapsas, N. Ellinas, J. S. Sung, H. Park, and S. Raptis, “WordLevel Style Control for Expressive, Non-attentive Speech Synthesis,” in Proc. SPECOM, 2021. T. Raitio, R. Rasipuram, and D. Castellani, “Controllable neural text-to-speech synthesis using intuitive prosodic features,” in Proc. Interspeech, 2020. Synthesized texts from the Blizzard Challenges 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2016: M. Fraser and S. King, "The Blizzard Challenge 2007," in Proc. SSW6, 2007. V. Karaiskos, S. King, R. A. Clark, and C. Mayo, "The Blizzard Challenge 2008," in Proc. Blizzard Challenge Workshop, 2008. A. W. Black, S. King, and K. Tokuda, "The Blizzard Challenge 2009," in Proc. Blizzard Challenge, 2009. S. King and V. Karaiskos, "The Blizzard Challenge 2010," 2010. S. King and V. Karaiskos, "The Blizzard Challenge 2011," 2011. S. King and V. Karaiskos, "The Blizzard Challenge 2012," 2012. S. King and V. Karaiskos, "The Blizzard Challenge 2013," 2013. S. King and V. Karaiskos, "The Blizzard Challenge 2016," 2016. Contact Alexandra Vioni - a.vioni@samsung.com <ul> <li>If you have any questions or comments about the dataset, please feel free to write to us.</li> <li>We are interested in knowing if you find our dataset useful! If you use our dataset, please email us and tell us about your research.</li> </ul>

A tunable physiomimetic stretch system evaluated with precision cut lung slices and recellularized human lung scaffolds

Datasets (excel files with raw data) to the publication Rosmark O, Ibáñez-Fonseca A, Thorsson J, Dellgren G, Hallgren O, Larsson Callerfelt A-K, Elowsson L and Westergren-Thorsson G (2022), A tunable physiomimetic stretch system evaluated with precision cut lung slices and recellularized human lung scaffolds. Front. Bioeng. Biotechnol. 10:995460. doi: 10.3389/fbioe.2022.995460

Supplementary material for a usability evaluation of a semantic search for biological datasets

We conducted a usability evaluation for a semantic dataset search with 20 biodiversity scholars in June and July 2022 in Germany. Following the TREC guidelines (https://www-nlpir.nist.gov/projects/t9i/spec.html), we setup eight user tasks and surveys with questionnaires to guide users through the evaluation. The zip file provides questionnaires, survey templates and the original results. A Jupyter notebook for data analysis is provided in our GitHub repository: <a href="https://github.com/fusion-jena/semantic-search-usability-analysis">https://github.com/fusion-jena/semantic-search-usability-analysis</a>