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236 results for “Modeling Methods”
Stimuli from: AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures
<p>Stimuli generated using a Diffusion Model from the paper "AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures".</p> <p>Please cite as:<br><span>Ciupinska, K.; Marchesi, S.; Abbo, G. A.; Belpaeme, T. and Wykowska, A. (2024). <strong>AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures</strong>. In <em>Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: AWAI</em>; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 1436-1443. DOI: 10.5220/0012596400003636</span></p>
Data for: Information accessibility, accounting manipulation, and sustainable development of digital enterprises: Based on double moderating effect model and panel PSM-DID method
<p>A theoretical mechanism was analyzed from the micro perspective of the enterprise to explore how information accessibility moderates the effect of accounting manipulation on the sustainable development of digital enterprises. Using data from 1200 listing digital enterprises in China and the DEA-Malmquist index method, the efficiency value of digital enterprises in 2007–2021 was estimated to represent the index of sustainable development of digital enterprises. The accounting manipulation was detected using the panel PSM-DID method based on the Administrative Measures for the Recognition of High-tech Enterprise's policy. The information accessibility value was estimated based on the MDA method. Empirical studies were conducted using text analysis, the panel PSM-DID method, and the double moderating effect model. The results showed that: (1) Accounting manipulation had a negative impact on the sustainable development of "true" digital enterprises and the "fake" digital enterprises; (2) Information accessibility directly and positively enhanced the technological progress and scale efficiency of digital enterprises, and its moderating effect was heterogeneous, with a significant moderating effect on the "true" digital enterprises and a negative effect on the "fake" ones.</p>
Data from: Exploring the multi-level impacts of a youth-led comprehensive sexuality education model in Madagascar using human-centered design methods
<p>Comprehensive sexuality education (CSE) is recognized as a critical tool for addressing sexuality and reproductive health challenges among adolescents. However, little is known about the broader impacts of CSE on populations beyond adolescents, such as schools, families, and communities. This study explores multi-level impacts of an innovative CSE program in Madagascar, which employs young adult CSE educators to teach a three-year curriculum in government middle schools across the country. The two-phased study embraced a participatory approach and qualitative Human-centered Design (HCD) methods. In phase 1, 90 school principals and administrators representing 45 schools participated in HCD workshops, which were held in six regional cities. Phase 2 took place one year later, which included 50 principals from partner schools, and focused on expanding and validating findings from phase 1. From the perspective of school principals and administrators, the results indicate several areas in which CSE programming is having spill-over effects, beyond direct adolescent student sexuality knowledge and behaviors. In the case of this youth-led model in Madagascar, the program has impacted the lives of students (e.g., increased academic motivation and confidence), their parents (e.g., strengthened family relationships and increased parental involvement in schools), their<br>schools (e.g., increased perceived value of schools and teacher effectiveness), their communities (e.g., increased community connections), and impacted broader structural issues (e.g., improved equity and access to resources such as menstrual pads). While not all impacts of the CSE program were perceived as positive (e.g., students start experimenting with sex and love), the findings uncovered opportunities for targeting investments and refining CSE programming to maximize positive impacts at family, school, and community levels.</p>
Data for: A new threshold selection method for species distribution models with presence-only data: extracting the mutation point of the P/E curve by threshold regression
<p>Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real-world applications and model assessments. Prevalent threshold selection methods for presence-only data require unproven pseudo-absence data or subjective researchers' decisions. This study proposes a new method, Boyce-Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo-absence data. We summarize that the mutation point is a typical shape feature of the predicted-to-expected (P/E) curve after reviewing relevant articles. Analysis based on source-sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point.</p> <p>To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods.</p>
Dataset on rapid sensory methods for cured hams and associations with consumer values in the Schwartz model
<p>A data set comprising data from N=127 consumers. Consumers answered the Schwartz Portrait Values questionnaire with 21 items. Later the same consumers rated blind and informed liking for eight types of cured hams, performed projective mapping based on tasting and on front-and back of pacg images, CATA on usages and values associated with the hams. </p> <p>Data collection procedures are described in attached pdf document, whereas all data is available as an excel file, including the metadata. </p>
Data from: "Cross-realm transferability of species distribution models – species characteristics and prevalence matter more than modelling methods applied"
<h2>Abstract</h2> <p>This data contains occurrence observations (presence-absence) of 11 aquatic macrophytes from Bothnian Sea and Lake Puruvesi, and environmental covariates used to build species distribution models (SDMs) in paper "Cross-realm transferability of species distribution models – species characteristics and prevalence matter more than modelling methods applied" in Ecological Modelling.</p> <p>In addition to data files, also R code for fitting the SDMs is supplied, as is the R code to replicate the analysis conducted in the paper. The data is stored in rdata format (point data), without coordinate information due to data policy restrictions. The species in the data are <em>Isoëtes lacustris, Isoëtes echinospora, Ranunculus reptans, Ranunculus schmalhausenii, Potamogeton berchtoldii, Potamogeton perfoliatus, Potamogeton gramineus, Myriophyllum alterniflorum, Equisetum fluviatile, Eleocharis acicularis and Elodea canadensis. </em>The environmental covariates are bottom water salinity, turbidity, sandy substrate occurrence, colored dissolved organic matter (CDOM), surface fetch, sampling depth, total nitrogen and total phosphorus, and distance to closest <em>Phragmites australis </em>reed bed. </p> <h2>Objective of the study </h2> <p>The modelling objective of the paper was species distribution model (SDM) transferability assesment. Transferability was assessed using models built in marine areas in projecting the distributions of the target species in Lake Puruvesi, Saimaa, Eastern Finland. Macrophyte mapping data from Lake Puruvesi was used as independent test data, against which transferability of the models was assessed.</p> <h2>Location</h2> <p>The species data was collected from two geographic areas: Bothnian Bay (Baltic Sea) and Lake Puruvesi (Eastern Finland). The marine observations from Bothnian Bay were split into three overlapping areas (areas 1-3), to test the effect of input data gradient length to SDM transferability. The largest marine sampling area (Area 3) ranged from 62.95, 65.91 latitude and 19.14, 27.99 longitude. Area 2 ranged from 63.95, 65.91 latitude and 21.53, 27.99 longitude. Area 1 ranged from 64.91, 65.91 latitude and 23.82, 27.99 longitude. The Hummonselkä subbasin of Lake Puruvesi, where the macrophyte test data was collected, is located at 61.89, 62.05 latitude and 29.58, 29.78 longitude.</p> <h2>Species data </h2> <p>The species observations were collected using diving transects placed in the floor of the sea or lake, and species observations were recorded in 2 m22 grid cells separated by 10 meters along the transect or 1 meters depth, depending which criteria was met first. The species data was collected in 2010 - 2020 from marine area, and 2017 from Lake Puruvesi. All macrophytes in 2 x 1 m frames were identified to species level by the diver, and the data contained information on species presence or absence in each grid cell. The diving transects were conducted using systematic survey protocol used in the underwater inventories of the Finnish Underwater Biodiversity Survey Program (VELMU) (Frosblom & Virtanen et al. 2024). The locations of the diving transects were not randomly distributed, but were placed using expert judgement. As all our study species are macroscopic and relatively easily identifiable in the field (with the exception of possibility of mixing <em>I. echinospora</em> and <em>I. lacustris</em>), we consider the absences in our observation data to indicate true absences. That said, as the observation area is rather small (2 m2), it is possible that a species may be found in the site of investigation (e.g. a small lagoon) but be located outside the vegetation sampling grid.</p> <h2>Environmental data</h2> <h3>Bottom water salinity</h3> <p>The seasonal mean bottom water salinity was modeled using a generalized additive model with mean salinity as response, with log-link and gamma distribution for the errors. This was necessary to keep the resulting predictions positive. Bottom depth, CDOM, river influence and spatial location were used as predictors. Data from 448 locations were used and each location had a minimum of three observations. The model was validated using 30 % of the data left outside of the model fitting. The explained deviance of the model was 0.94 and the correlation between raw data and predicted values was 0.95 with few outliers.</p> <h3>Turbidity</h3> <p>Maps of turbidity (in FNU, Formazin Nephelometric Unit) were generated from Sentinel-2 Multi-Spectral Imager (MSI) observations using the Case-2 Regional Coast Colour (C2RCC) bio-optical inversion model, containing separate atmospheric correction and water quality parts. Before computing C2RCC, the original 10-meter input data was downsampled to 60 meters. The output variable of the C2RCC processor correlative to turbidity is the backscattering of total suspended sediments at 443 nm, which was further calibrated into turbidity (FNU) values using SYKE's empirical equations for coastal waters and clear lakes (for a similar approach, see Attila et al. (2013) and Sagerman, Hansen, and Wikström (2020)). Monthly observations of turbidity were aggregated into median composites to reduce the effects of cloud cover and other disturbances. Due to low solar elevation and ice cover in winter, the turbidity distribution maps are generated only for the summer months (May to September). The current processing covers years 2017 to 2021. An average raster layer was created from monthly observations as input for SDM building.</p> <h3>Probability of sandy substrate </h3> <p>Random forest model was used to classify sandy bottoms from Sentinel 2 MSI satellite images in shallow water areas. Identifying sandy substrate is based on the higher reflectance compared to other substrates. The model was trained and validated using diver recorded field observations in the Baltic area, and diver recorded and echo sounding observations in the freshwater area. For full coverage including areas beyond the shallow water, the satellite image classification was combined with boosted regression tree modelling result in the Baltic, and echo sounding based product in the freshwater region. The resulting layers were probabilities of sandy substrate with 10-meter cell resolution.</p> <h3>CDOM</h3> <p>We used different methods to estimate the CDOM levels in Bothnian Bay and Lake Puruvesi, based on biogeochemical model data and satellite images. For Lake Puruvesi, we applied the Finnish Environment Institute's (Syke) in-house CDOM algorithm to the Sentinel-2 MSI images processed by the C2RCC bio-optical processor (Brockmann et al. 2016). The observations in 10 m resolution were aggregated as monthly averages for each month of the summer season (May to October) from 2017 to 2021. For Bothnian Bay, we used Syke's in-house Sentinel-2 MSI CDOM layers (resolution: 60 m) aggregated as seasonal averages (1 Jul to 7 Sep). CDOM values are given as absorption coefficient of CDOM at 400 nm [m⁻¹].</p> <h3>Surface fetch</h3> <p>A surface fetch raster was produced to the Puruvesi and Bothnian Bay. The analysis required a feature layer of shorelines from Puruvesi and Baltic Sea. First, we created polyline from north to south spanning over the whole area of interest with a gap of 20 meters which is also the resolution of the output raster. These lines were then cut each time they hit the shoreline and the part of the line that was overlapping land was removed. The distance of the remaining lines was then calculated and a point with the distance value was created every 20 meters. Each time the line was cut when hitting an island for example and starting again from the other side of the island, the distance calculation started from 0. This created a point dataset with a distance value in each point. We repeated the procedure for 15 times for different compass directions with 22.5 degree intervals and calculated average fetch for each point location on 20 meters grid from these 15 point layers.</p> <h3>Depth</h3> <p>Depth was measured by a diver using a dive computer while surveying each vegetation grid cell, and measured depth was used when projecting model results to Puruvesi (transferability performance). In addition, a depth model for the freshwater region was created from Sentinel 2 MSI satellite image using the logarithmic band ratio model of blue and red band. The model was calibrated using diver recorded field observations and validated against echo sounding measurements. For more complete coverage and to include deep areas, echo soundings from multiple sources were combined with the satellite derived bathymetry. The cell resolution of the resulting depth layer was 10 meters.</p> <h3>Total nitrogen and phosphorus</h3> <p>Mean total nitrogen and phosphorus layers for marine area were produced using ArcGIS "splines with barriers" tool for the EEZ of Finland with 20 meters spatial resolution (Virtanen et al. 2018). Summer (July - September) nutrient measurements from 0 to 10 meters depth between 2010 and 2020, obtained from the VESLA database, were used as input data for the interpolation.</p> <p>Nitrogen and phosphorus measurements in Puruvesi between 2010 and 2020 was gathered from the VESLA database. Data from July to September was selected to represent the growing season. A mean value of NTOT and PTOT was then calculated for each location. Spline with Barriers (SwB) tool was used to interpolate the values (Arcmap 10.7.1). The tool uses a feature layer as barrier to create the raster representing only the area of interest. For the barrier and the extent of the interpolated raster we used a shapefile representing Lake Puruvesi shoreline. The resolution was set to 5x5 meters. SwB tool created an "extent box" around the area of interest which was removed with Extract by Mask tool using the shoreline feature layer. After the interpolation we noticed that either one of the locations was situated on land or the polygon used as barrier was "leaking". SwB doesn´t interpolate areas that doesn't have locations with values or aren´t connected to the main body of water. To fix this, the raster was extended outwards based on the values of nearby cells and after that the raster was masked again to remove any cells on land. The phosphorus interpolation provided negative values in southern parts on Enanlahti in Kontiolahti and Muholanlahti. These negative values were caused by considerably larger phosphorus values in Enanlahti Lamminniemi (9m) Enanlahti Lamminniemi (4m) locations when compared with the nearby Puruvesi Enanlahti location. The interpolation apparently continued to decrease the values according to the trend set by the difference between these locations and caused it to reach negative values. The southern parts of the bay, about 750 meters, was removed and new values were calculated based on the surrounding cells with Focal Statistics tool. The interpolations were validated by removing 20 % of the locations and reproducing the interpolation. The removed locations and their values were then compared to the interpolated raster. R∗2∗2 value from phosphorus interpolation model was 0.91 after removing two outliers and R22 value from nitrogen interpolation model was 0.715 after removing one outlier.</p> <h3>Distance to closest reed </h3> <p>The aquatic vegetation (<em>Phragmites australis</em> reeds) presence/absence maps were also generated from Sentinel-2 MSI data. The processing included extracting one month of data (July 2019) from green and near-infra-red bands from Sentinel-2 Global Mosaic (S2GM) service and transforming those to normalized-difference vegetation indices (NDVIs). After that, Bayesian statistics were used to predict the posterior probability of vegetation occurrence when distance from shore and NDVI were used as predictor variables. The posterior variable was thresholded and the resulting vegetation presence areas were sieved so that both too small vegetation areas (fewer than 5 pixels) or areas that were not directly attached to shoreline were removed. The resulting map has 10 m pixel size and tentatively represents the locations of reed belts or other shoreline-attached vegetation. This EO-based layer could also be referred to as helophytes or helophytic macrophytes, as it denotes a specific zone of vegetation with emergent aquatic plants containing leaf-green, particularly those that grow densely and have horizontally oriented leaves. In some lakes, this layer can represent, for example, thick stands of <em>Equisetum fluviatile</em>, although in most cases, it is associated with common reed belts. The approach is described in more detail in Koponen et al. (2022).</p> <h2>Data partitioning </h2> <p>Data was partitioned with 70/30 splitting into training and test (interpolation accuracy) data. The splitting was repeated 100 times for each species by randomly selecting 70 % of observations which were used to build each of the SDMs (GLM, GAM, BRT and BART). The partitioning was repeated for each of the three input data areas and 11 species. The input data indexes for replicating the split are supplied in the data files. </p> <h2>R code </h2> <p>Code files contain scripts for fitting the SDM models described in the paper using the data. Also code for calculating calidation statistics (modelling results) and code for statistical analyses for making inferences in the paper, are supplied. </p> <h2>Additional info</h2> <p>More details on modelling protocol may be found in the original paper in Ecological Modelling, and in the Supplementary Information file of the original paper, which follows the model reporting template "ODMAP" by Zurell et al. (2020).</p> <h2>References </h2> <p><span>Attila, J., et al. 2013. MERIS Case II water processor comparison on coastal sites of the northern Baltic Sea. - Remote Sensing of Environment 128: 138-149.</span></p> <p><span>Brockmann, C., et al. 2016. Evolution of the C2RCC neural network for Sentinel 2 and 3 for the retrieval of ocean colour products in normal and extreme optically complex waters. - In: Living Planet Symposium. p. 54.</span></p> <p><span>Forsblom, L., et al. 2024. Finnish inventory data of underwater marine biodiversity<span> </span>-Scientic data</span></p> <p><span>Koponen, S., et al. 2022. Blue Carbon Habitats: – a comprehensive mapping of Nordic salt marshes for estimating Blue Carbon storage potential. - Nordisk Ministerråd.</span></p> <p><span>Sagerman, J., et al. 2020. Effects of boat traffic and mooring infrastructure on aquatic vegetation: A systematic review and meta-analysis. - Ambio 49: 517-530.</span></p> <p><span>Zurell, D., et al. 2020. A standard protocol for reporting species distribution models. - Ecography 43: 1261-1277.</span></p> <p></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Human face-off: a new method for mapping evolutionary rates on three-dimensional digital models
<p>Modern phylogenetic comparative methods allow estimating evolutionary rates of phenotypic change, how these rates differ across clades, and assessing whether the rate remained constant over time. Unfortunately, currently available phylogenetic comparative tools express the rate in terms of a scalar dimension, hence they do not allow us to determine rate variations among different parts of a single, complex phenotype, or charting of realized rate variation directly onto the phenotype. Herein, we present a new method which allows the mapping of evolutionary rate variation directly on three-dimensional phenotypes, informing on the direction and magnitude of trait change automatically.</p> <p>This new method, implemented by the function rate.map embedded in the R package 'RRphylo', is based on phylogenetic ridge regression rate estimates. Since the latter represent ridge regression slopes, they possess sign and magnitude. In 'RRphylo', different rates are calculated for different districts of the phenotype, which can then be visualized directly onto the phenotype itself. We present the application of rate.map to the evolution of facial skeleton in Hominoidea (the clade including living and fossil apes), the primate clade inclusive of Homo and the greater apes. We found that the highly derived, unique shape of the face in modern humans evolved through rapid phenotypic changes affecting the nasal bones, the brow ridge and the maxillary region. The canine fossa, a facial feature unique to Homo sapiens, did not belong to a region of rapid phenotypic change, and could be seen as the by-product of midface evolution as suggested by previous studies.</p>
Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data
<p>Supporting simulation code and data for the manuscript "Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data"</p>
Data availability: Random encounter model is a reliable method for estimating population density of multiple species using camera traps
<p>Data of the paper entitled "Random encounter model is a reliable method for estimating population density of multiple species using camera traps" published on Remote Sensing in Ecology and Conservation</p>
A fast, precise, in-vivo method for micron-level 3D models of corals using dental scanners
<p>1. Several sampling and measurement strategies have been developed to assess biological forms in three dimensions (3D), including corals. However, the effectiveness (in speed and precision) of current 3D methods in scanning and model construction are challenging at small scales (μm – mm).</p> <p>2. In this paper, a practical 3D scanning and model construction tool using an intra-oral dental scanner was assessed to measure the surface area and volume of coral juveniles across multiple species. Intra-oral scanners using confocal imaging are fast, precise to the μm scale, and safe to use with live tissue, thereby eliminating the need to harm or kill the animals. The trial was conducted at the National Sea Simulator at the Australian Institute of Marine Science.</p> <p>3. High-quality 3D scans of individual coral juveniles were successfully generated and integrated automatically into high-resolution (μm) mesh from point clouds. The attained average scanning efficiency was < 2 min./individual, without a significant difference in speed given coral complexity or between live colonies or dead coral skeleton.</p> <p>4. Overall, this fast and precise system could become a promising tool for marine environmental surveys and restoration initiatives. This tool also removes the need to sacrifice animals for measurement analysis, thereby increasing conservation and animal welfare.</p>
Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets
<p>Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets</p> <p>The impact of simulated rainfall on the soil surface roughness of different soil types with various initial surface states and the differences between their spectral characteristics were studied under laboratory conditions. The soil samples were collected from a horizon of fields near Poznań, western Poland. The physical and physicochemical properties of each soil sample were determined. Then, the part of the soil materials, consisting of natural aggregates, were used to form three soil surface roughness. </p> <p>An explanation of the table column names in the “soils properties.csv” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“sand” - Sand content in the soil sample in %.</p> </li> <li> <p>“silt” – Silt content in the soil sample in %.</p> </li> <li> <p>“clay” – Clay content in the soil sample in %.</p> </li> <li> <p>pHH2O” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“pHKCl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “rainfall doses.csv” file:</p> <p> </p> <ul> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p>“rainfall dose” - One-time amount of rainfall dose expressed in millimeters.</p> </li> <li> <p>“accumulated rainfall” – Summation of rainfall after each successive dose expressed in millimeters.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “soil measurements” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p> “reflectance” - The amount of radiation reflected from the soil surface under the influence of successive rainfalls and expressed in nanometres. </p> </li> <li> <p>“roughness state” - The size of the roughness: R1 is the lowest soil roughness state, R2 represents medium soil roughness, and R3 represents the greatest roughness.</p> </li> <li> <p>“T3D” - Tortuosity index is a surface roughness index. It was calculated from DEM (Digital Elevation Model). It expresses the ratio between the true surface of DEM and its flat horizontal area.</p> </li> <li> <p>“HSD” - Height Standard Deviation is the second surface roughness index. It was calculated from DEM and expressed in millimeters. </p> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><br> </p> <p> </p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
A High-Resolution Stochastic Modeling Method for Elastic Parameters Based on FDMA
<p>Data used in article ‘A High-Resolution Stochastic Modeling Method for Elastic Parameters Based on FDMA’. Including logging data, seismic P-wave velocity data and data of figures in this article.</p>
Additonal material for the dissertation "An Accelerated Solution Method for Two-Stage Stochastic Models in Disaster Management": Data, MATLAB codes and results
<p>File "DataImport" contains a "ReadMe" file, raw data for all case studies in Excel and the MATLAB code "ImportData.m" importing Excel data into MATLAB</p> <p>File "LShaped" contains a "ReadMe" file, all data in the form of matrices and the MATLAB code "LShaped_MultiCut.m" solving all case studies via the standard or accelerated L-shaped method using a multi-cut approach</p> <p>File "Results" contains a "ReadMe" file, results of all case studies and computation time required by Gurobi, der standard L-shaped method and accelerated L-shaped method</p>
The dataset of the manuscript "GPU-HADVPPM4HIP V1.0: higher model accuracy on China's domestically GPU-like accelerator using heterogeneous compute interface for portability (HIP) technology to accelerate the piecewise parabolic method (PPM) in an air quality model (CAMx V6.10)"
<p><strong>bcfile.zip:</strong> the clean boundary condition files.</p> <p><strong>CAMxv6x_cpp.zip: </strong>the source code of CAMx-HIP version which coupled with HIP-HADVPPM scheme.</p> <p><strong>data.zip:</strong> final data tables used to plot figures.</p> <p><strong>emisfile.zip: </strong>the emission files.</p> <p><strong>icfile.zip:</strong> the clean initial condition files.</p> <p><strong>tuvfile.zip </strong>and <strong>o3mapfile.zip:</strong> the photolysis files.</p> <p><strong>outputfile.zip:</strong> the computation results outputted by CAMx model for Fortran version on the Intel Xeon E5-2682 v4 CPU, CUDA version on the NVIDIA K40m and V100 clusters, and HIP version on the China' s domestically heterogeneous cluster A.</p> <p><strong>wrfcamx.zip:</strong> the meteorological files.</p> <p><strong>offline_test_cuda.zip: </strong>the advection module code written in CUDA C language</p> <p><strong>offline_test_fortran.zip:</strong> the advection module code written in Fortran language</p> <p><strong>offline_test_hip.zip: </strong>the advection module code written in HIP C language</p>
Dataset: Methods for computing the maximum performance of computational models of fMRI responses.
<p>Accompanying data for manuscript: Methods for computing the maximum performance of computational models of fMRI responses. written by Agustin Lage-Castellanos, Giancarlo Valente, Elia Formisano, Federico De Martino, submitted for publication in Plos Computational Biology, July 2018.</p> <p>This dataset provide the Betas for subcortical and a subset of the cortical voxels for three subjects in matlab format.</p> <p>The field bTest refers to the Beta coefficients for every voxel in the test data. The fields beta1 and beta2 refer to the split-half partitions of the bTest coefficients. The field varBparam refers to the parametric variances of the Beta coefficients and the field varBBootstrap refers to the variances of the Betas computed with bootstrap. </p>
Code + simulated + publically accessable data for "Evaluating health facility access using Bayesian spatial models and location analysis methods"
<p># README</p> <p>These files contain r data objects and R files that represent the key details of the paper, "Evaluating health facility access using Bayesian spatial models and location analysis methods".</p> <p>The following datasources are available for simulation of some of the ideas in the paper.</p> <p>- dat_grid_sim: simulated data of the grid and grid cells<br> - dat_ohca_cv_sim: simulated data containing the cross validated test/training sets of OHCA data<br> - dat_ohca_sim: simulated OHCA event data<br> - dat_aed_sim: simulated AED location data<br> - dat_bldg_sim: simulated building location data<br> - dat_municipality_sim: simulated municipality information<br> - table_1: Table 1 information containing key demographic data</p> <p>These data were produced using the code in 01-create-sim-data.R, and one of the statistical models is demonstrated in 02-demo-inla-model.R</p> <p>In terms of the paper itself, the functions and code used in the manuscript are located in:</p> <p>* 01_tidy.Rmd - analysis code used to tidy up the data</p> <p>* 02_fit_fixed_all_cv.Rmd - analysis code used to place AEDs</p> <p>* 02_model.Rmd - analysis code used to fit the model in INLA</p> <p>* 03_manuscript.Rmd - Full code and text used to create the paper</p> <p>* 04_supp_materials.Rmd - full code and text used to create the supplementary materials</p> <p>The following files are a part of an R package "swatial" that was developed along with the paper. These files are:</p> <p>* DESCRIPTION</p> <p>* NAMESPACE</p> <p>* LICENSE</p> <p>* LICENSE.md</p> <p>* decay.R</p> <p>* spherical-distance.R</p> <p>* test-figure-data-matches.R</p> <p>* test-table-data-matches.R</p> <p>* testthat.R</p> <p>* tidy-inla.R</p> <p>* tidy-posterior-coefs.R</p> <p>* tidy-predictions.R</p> <p>* utils-pipe.R</p> <p>* All files that end in .Rd are documentation files for the functions.</p> <p>## Regarding data sources</p> <p>Census information for Ticino was transcribed from the Annual Statistical Report of Canton Ticino from years 2010 to 2015. This data was taken from their publicly accessible annual reports - for example: (https://www3.ti.ch/DFE/DR/USTAT/allegati/volume/ast_2015.pdf). The raw data was extracted from these annual reports, and placed into the file: "swiss_census_popn_2010_2015.xlsx". These data are put into analysis ready format in the file “01_tidy.Rmd”</p> <p>Housing and other relevant geospatial data can be accessed via http://map.housing-stat.ch/ and https://data.geo.admin.ch/. The maps of buildings from the REA (Register of Buildings and Dwellings) can be found here: https://map.geo.admin.ch/?zoom=11&bgLayer=ch.swisstopo.pixelkarte-grau&lang=en&topic=ech&layers=ch.bfs.gebaeude_wohnungs_register,ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill,ch.bfs.volkszaehlung-gebaeudestatistik_gebaeude,ch.bfs.volkszaehlung-gebaeudestatistik_wohnungen,ch.swisstopo.swissbuildings3d_1.metadata,ch.swisstopo.swissbuildings3d_2.metadata&E=2717616.28&N=1096597.25&catalogNodes=687,696&layers_timestamp=,,2016,2016,,&layers_visibility=true,false,false,false,false,false&layers_opacity=1,1,1,1,1,0.75</p> <p>For further enquiries on this data, contact the Swiss federal Office of Statistics at the details listed here: https://www.bfs.admin.ch/bfs/en/home/services/contact.html</p> <p>The shapefiles of the Comuni can be accessed here: https://www4.ti.ch/dfe/de/ucr/documentazione/download-file/?noMobile=1</p> <p>Data from the people living in the Municipalities in Ticino can be downloaded here: https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=dati.home&tema=33&id2=61&id3=65&c1=01&c2=02&c3=02</p> <p>## Future work</p> <p>In the future, these functions from the paper may be generalised and put into their own package. If that happens, this repository will be updated with a link to updated functions.</p>
Benchmark Dataset for Structure Refinement Methods of Protein Complex Models
<p>This is the dataset used in our work entitled " Benchmarking of Structure Refinement Methods for Protein Complex Model " by Jacob Verburgt and Daisuke Kihara, which is under review.</p> <p>ZDOCK Derived Benchmark Dataset:</p> <p>The primary benchmark set used in was directly derived from the <a href="https://zlab.umassmed.edu/benchmark/">ZDOCK Benchmark set</a>. The ZDOCK set contains four structures per target: An unbound ligand, an unbound receptor, a bound ligand, and a bound receptor. The benchmark set is available in such a way where the coordinates of the bound subunits are oriented identical to their complex structure, and the unbound subunits are superimposed onto their respective bound subunits. Our dataset creates the optimially oriented "unbound" complexes by combining the superimposed and unbound subunits, along with removal of waters, ligands, and other non-protein atoms. These unbound complexes are saved in the dataset in the form "XXXX_c_u.pdb", where XXXX is the PDB ID.</p> <p>From the complete ZDOCK Benchmark of 230 targets, 18 targets were removed due to containing multiple ligand chains, which is incompatible with the standard ligand to receptor model used within CAPRI. The ZDOCK PDB ID’s of these targets are 1AKJ", "1BJ1", "1DE4", "1EER", "1EXB", "1EZU", "1GP2", "1I9R", "1JMO", "1K74", "1N2C", "1QFW", "2HMI", "3EO1", "3HMX", "4FQI", "4GXU", and "9QFW".</p> <p>There are an additional 8 targets where the superimpostion of the ligand and receptor structures onto the complex led to entanglement of the chains and were subsequently removed from the dataset. The ZDOCK PDB IDs for these targets are "1BGX", "1H1V", "1IRA", "1R8S", "1Y64", "2OT3", "3AAD", "4GAM".</p> <p>Note:</p> <p>In the work, we also used CAPRI scoring model dataset derived from CAPRI rounds 38-45. This dataset is unable to be distributed directly by us due to CAPRI guidelines, but can be derived from "Scoring round" models from the <a href="https://www.ebi.ac.uk/pdbe/complex-pred/capri/">CAPRI Website </a>.</p> <p>The targets considered were T122-T125, T131-T133, and T136, as these were targets which contained globular protein ligands and receptors. Please contact us directly if you have any further questions on this dataset.</p>
A high-resolution finite element method (FEM) human head model for non-invasive brain stimulation
<p>High-resolution finite element method (FEM) model of a human head for non-invasive brain stimulation modeling using SimNIBS or other compatible software. The original head model (Ernie) was downloaded from the tutorial dataset of <a href="http://simnibs.org">www.simnibs.org</a> and further refined in grey matter and white matter regions.</p> <p>This supplementary dataset is released as part of the NeMo-TMS toolbox (<a href="https://github.com/OpitzLab/NeMo-TMS">https://github.com/OpitzLab/NeMo-TMS</a>). Please refer to the corresponding article for more information:</p> <p>Shirinpour, S., Hananeia, N., Rosado, J., Galanis, C., Vlachos, A., Jedlicka, P., Queisser, G., & Opitz, A. (2020). Multi-scale Modeling Toolbox for Single Neuron and Subcellular Activity under (repetitive) Transcranial Magnetic Stimulation. <em>BioRxiv</em>, 2020.09.23.310219. <a href="https://doi.org/10.1101/2020.09.23.310219">https://doi.org/10.1101/2020.09.23.310219</a></p>
Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids
<p>Animal-attached devices can be used on cryptic species to measure their movement and behaviour, enabling unprecedented insights into fundamental aspects of animal ecology and behaviour. However, direct observations of subjects are often still necessary to translate biologging data accurately into meaningful behaviours. As many elusive species cannot easily be observed in the wild, captive or domestic surrogates are typically used to calibrate data from devices. However, the utility of this approach remains equivocal. </p> <p>Here, we assess the validity of using captive conspecifics, and phylogenetically-similar domesticated counterparts (surrogate species) for calibrating behaviour classification. Tri-axial accelerometers and tri-axial magnetometers were used with behavioural observations to build random forest models to predict the behaviours. We applied these methods using captive Alpine ibex (Capra ibex) and a domestic counterpart, pygmy goats (Capra aegagrus hircus), to predict the behaviour including terrain slope for locomotion behaviours of captive Alpine ibex. </p> <p>Behavioural classification of captive Alpine ibex and domestic pygmy goats was highly accurate (> 98%). Model performance was reduced when using data split per individual, i.e., classifying behaviour of individuals not used to train models (mean ± sd = 56.1 ± 11%). Behavioural classifications using domestic counterparts, i.e., pygmy goat observations to predict ibex behaviour, however, were not sufficient to predict all behaviours of a phylogenetically similar species accurately (> 55%).</p> <p>We demonstrate methods to refine the use of random forest models to classify behaviours of both captive and free-living animal species. We suggest there are two main reasons for reduced accuracy when using a domestic counterpart to predict the behaviour of a wild species in captivity; domestication leading to morphological differences and the terrain of the environment in which the animals were observed. We also identify limitations when behaviour is predicted in individuals that are not used to train models. Our results demonstrate that biologging device calibration needs to be conducted using: (i) with similar conspecifics, and (ii) in an area where they can perform behaviours on terrain that reflects that of species in the wild.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.