Find research datasets worth reusing

opencc-by-4.0Aug 2022View details →

An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° ET over the conterminous United States, 1980–2015 (time-merged version)

This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).

opencc-by-4.0Aug 2022View details →

An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015 (time-merged version)

This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).

opencc-by-4.0Aug 2022View details →

JOSSE: A Software Development Effort Dataset Annotated with Expert Estimates

The JIRA Open-Source Software Effort (JOSSE) dataset consists of software development and maintenance tasks collected from the JIRA issue tracking system for Apache, JBoss, And Spring open-source projects. All the issues were annotated with actual effort and 19% of them were annotated with expert estimates. JOSSE is a task-based dataset with a textual attribute represented as a task description for each data point. This paper explains how the data were collected and details six data quality refinement procedures of the data points.

opencc-by-4.0May 2022View details →

Nesting Tasks Dataset for 2D-Nesting Efficiency Estimation

Nesting efficiency dataset This is the raw dataset associated with the paper “Graph Neural Networks Comparison for 2D-Nesting Efficiency Estimation”, by C.Lallier, L. Vézard, B. Pinaud and G. Blin, 2022. Consisting of 100,000 nesting tasks. Usage: The files are: tasks.gz, parts.gz, constraints.gz, and shapes.gz. They are in PICKLE file format version 5 with a gzip compression. Example to load a file : <pre><code class="language-python">import pandas as pd tasks = pd.read_pickle('tasks.gz')</code></pre>   Description: Tasks.gz file contains nestings high-level descriptors. It is composed of the following columns: <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <th scope="row">efficiency</th> <td>float</td> <td>The variable to predict (label). Given in % </td> </tr> <tr> <th scope="row">duration</th> <td>integer</td> <td>input data. The nesting algorithm convergence time. Given in s.</td> </tr> <tr> <th scope="row">sheet_width</th> <td>integer</td> <td>input data. The width of the nesting area. Given in m-4</td> </tr> <tr> <th scope="row">sheet_length</th> <td>integer</td> <td>input data. Facultative. The height of the nesting area. Given in m-4</td> </tr> <tr> <th scope="row">sheet_type</th> <td>integer</td> <td>input data. Kind of the nesting.</td> </tr> <tr> <th scope="row">tasks_index</th> <td>integer</td> <td>Generated data. Join key between tables.</td> </tr> <tr> <th scope="row">is_train, is_val, is_test</th> <td>boolean</td> <td>Generated data. Can be used as mask for the train, val and test subsets.</td> </tr> </tbody> </table> Parts.gz contains description of the parts to be nested :  <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>tasks_index</td> <td>integer</td> <td>Reference to the join key from the Task table. </td> </tr> <tr> <td>parts_id</td> <td>integer</td> <td>Generated part id.</td> </tr> <tr> <td>shape_hash</td> <td>integer</td> <td>Reference to the hash of the part's shape, join key from the Shape table.</td> </tr> </tbody> </table> Shapes.gz is the description of the shapes of the parts to be nested :  <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>shape_hash</td> <td>integer</td> <td>Generated data. Join key between tables.</td> </tr> <tr> <td>raw</td> <td>list of integers</td> <td>List of x, y tuples for each point. Unit is m-4</td> </tr> <tr> <td>sizes</td> <td>list of integers</td> <td>List of sub-shapes sizes. </td> </tr> </tbody> </table> Constraints.gz describes constraints and their parameters: <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>type</td> <td>string</td> <td>Generated constraint type.</td> </tr> <tr> <td>tasks_index</td> <td>integer</td> <td>Reference to the join key from the Task table.</td> </tr> <tr> <td>parts_1, parts_2</td> <td>list of integers</td> <td>References to the parts_id from the Parts table.  </td> </tr> <tr> <td>p1_x, p1_y and p2_x, p2_y</td> <td>list of floats</td> <td>Input data. Origin position (x, y) of the constraint on parts. For each part of the constraint.</td> </tr> <tr> <td>r1_start, r1_end, r1_flip_x</td> <td>list of floats</td> <td>Input data. Rotation (start, end, and flip_x) parameters of the constraint. Multiple ranges accepted.</td> </tr> <tr> <td>y_min, y_max</td> <td>list of floats</td> <td>Input data. Range from (y_min, y_max). Multiple ranges accepted.</td> </tr> <tr> <td> x_offset, y_offset, motif_order, x_alignment_type, y_alignment_type, proximity_type, max_distance, groups_relative_orientation, is_frozen </td> <td>float</td> <td>Input data. Other constraint parameters.</td> </tr> </tbody> </table>

Dataset in support of the study "Getting the leaves right matters for estimating temperature extremes"

Dataset in support of the study “Getting the leaves right matters for estimating temperature extremes"

Limitations of Catchment Area Estimates and River Basin Boundaries from CWC and WRIS

The following files are included: [Item 1 'share_stations.txt']      list of 100 stations used in this study and their attributes, pipe(|) delimited [Item 2 'share_hydrosheds_rivers'] one shapefile of HydroSHEDS river network up to Farakka [Item 3 'share_merit_rivers']      one shapefile of MERIT river network up to Farakka [Item 4 'share_hydrosheds_reloc']  one shapefile of relocated stations, HydroSHEDS network [Item 5 'share_merit_reloc']       one shapefile of relocated stations, MERIT network [Item 6 folder 'share_bounds']     shapefiles of delineated catchment boundaries associated with each station (100 total) [Item 7 'share_station_maps.pdf']  PDF file of maps showing location of each station and associated catchment boundary (100 pages)

opencc-zeroSep 2022View details →

Data belonging to the article: Estimating pre-harvest density, adult sex ratio and fecundity of white-tailed deer using wildlife cameras

Adult sex ratio and fecundity (juveniles per female) are key population parameters in sustainable wildlife management, but inferring these requires abundance estimates of at least three age/sex classes of the population (male and female adults and juveniles). Prior to harvest, we used an array of 36 wildlife camera traps during 2 and 3 weeks in the early autumn of 2016 and 2017 respectively. We recorded white-tailed deer adult males, adult females and fawns from the pictures. Simultaneously, we collected fecal DNA (fDNA) from 92 20mx20m plots placed in 23 clusters of four plots between the camera traps. We identified individuals from fDNA samples with microsatellite markers and estimated the total sex ratio and population density using Spatial Capture Recapture (SCR). The fDNA-SCR analysis concluded equal sex ratio in the first year and female bias in the second year, and no difference in space use between sexes (fawns and adults combined). Camera information was analyzed in a Spatial Capture (SC) framework assuming an informative prior for animals' space use, either (1) as estimated by fDNA-SCR (same for all age/sex classes), (2) as assumed from the literature (space use of adult males larger than adult females and fawns), (3) by inferring adult male space use from individually-identified males from the camera pictures. These various SC approaches produced plausible inferences on fecundity, but also inferred total density to be lower than the estimate provided by fDNA-SCR in one of the study years. SC approaches where adult male and female were allowed to differ in their space use suggested the population had a female-biased adult sex ratio. In conclusion, SC approaches allowed estimating the pre-harvest population parameters of interest and provided conservative density estimates.

Weamyl radar and precipitation data related to quantitative precipitation estimation

This dataset contains radar data and precipitation data provided for research during the Weamyl project (NO Grants 2014-2021, under Project contract no. 26/2020) specifically used for quantitative precipitation estimation research. Radar data contains two radar products: <ul> <li>Reflectivity (R): a base radar product, measured in dBZ, it is the product used for estimating precipitation. R data for the lowest 2 elevation angles. </li> <li>One-hour precipitation (OHP): a derivate radar product, it is the precipitation estimation that the radar makes automatically; to be used as comparison</li> </ul> All radar data are in <a href="https://www.unidata.ucar.edu/software/netcdf/">netcdf </a>format. It is data from a WSR-98D Doppler weather radar situated in central Transylvania, provided in the form of a 557x800 grid with the cell size of ~1x1 km. Precipitation data contains 1-hour precipitation values collected from ground weather station, measured in mm; with the purpose to be used as ground truth. The precipitation data contains a csv file that contains the actual precipitation data, first value mentions the station code, the second the time and date and the third the 1-hour accumulated precipitation value, in mm. The second file provides the lat/long values for the ground weather stations, to be able to match the values of a station to a location on the grids from the radar data.

Incorporating plant access to groundwater in existing global, satellite-based evaporation estimates

This repository contains data used in the paper "Incorporating plant access to groundwater in existing global, satellite-based evaporation estimates". This repository includes the following netcdf files: 1) daily evaporation based on GLEAM-Hydro [mm/d], 2) daily evaporation based on GLEAM v3 [mm/d], 3) annual-mean groundwater-sourced evaporation (E_GW) [mm/year], and 4) temporally averaged groundwater contribution fraction (f_GW) [-].

An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015

This dataset contains the 1980–2015 monthly terrestrial water budget simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the abbreviation for "terrestrial water budget", the used parameterization, the time scale, and the suffix. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).

opencc-zeroSep 2022View details →

Can diet composition estimates using stable isotope analysis of feathers predict growth and condition in nestling mountain bluebirds (Sialia currucoides)

Insectivorous birds breeding in seasonal environments provision their dependent young during periods when prey diversity and abundance vary. Consequently, the composition and nutritional value of diets parents feed to their offspring may differ within and among broods, potentially affecting the condition of nestlings. In a population of mountain bluebirds (Sialia currucoides), we used two methods to estimate diet composition for individual nestlings: direct observation of provisioning using video recordings at 5 and 9 days post-hatch, and stable isotopes of the δ13C and δ15N in nestling feathers and prey followed by analysis with mixing models. We determined the macronutrient content (% fat and lean mass) and estimated the metabolized energy from each type of prey. We evaluated whether different methods of estimating diet composition would produce similar results, and if the types of prey nestlings ate at one or both ages affected their morphology, growth rates, or blood ketone concentration. We found that bluebirds fed their young 5 main types of prey: beetles, cicadas, grasshoppers, insect larvae, and spiders. Both observational and mixing model estimates of diet composition indicated that larvae are traded-off with grasshoppers, and that fewer larvae are provided to nestlings as the season progresses. In evaluating how diet influences individual growth and condition, estimates from direct observations had greater explanatory power than those from mixing models, indicating that diets rich in the most energy-dense prey (greatest fat content; cicadas and larvae) were associated with larger size and higher body condition, and faster rate of mass gain and growth of tarsus. Lower value prey had more limited, specific effects on nestlings, but may still be important dietary components. While isotopic methods produced estimates of diet composition that were generally informative, when applied to explain the growth and condition of nestlings they proved less useful.

Dataset for "Correlation between sonic pulse velocity and flat-jack tests for the estimation of the elastic properties of unreinforced brick masonry"

This repository contains data from the sonic test experimental campaign carried out in eight structures at various location in Croatia. The data set is structured in 3 levels of folders: - At first level, the 8 folders correspond to the 8 tested buildings. - At second level, for each building, each folder corresponds to a different test location within the building, e.g. "Data FJ1". - At third level, for each location, each folder corresponds to a different setup (i.e. distance and location of hammer and accelerometer), , e.g. "FJ1 1-2". Sonic data are presented in .txt files in three columns corresponding to the time, the hammer (emitter) and the accelerometer (receptor), respectively. Please cite the following related publication: Ortega J, Stepinac M, Lulic L, Nuñez Garcia M, Saloustros S, Aranha C, Greco F, Correlation between sonic pulse velocity and flat-jack tests for the estimation of the elastic properties of unreinforced brick masonry, under review (2022)

opencc-by-4.0Oct 2022View details →

Estimation of axial loads in tie-rods: Dataset generated from Finite Element simulations for training Artificial Neural Network

Dataset employed for training the Artificial Neural Networks (ANNs) presented in the cited journal article. The trained ANNs were used to estimate the tensile force in tie-rods installed in a historical structure (the church of the monastery of Sant Cugat close to Barcelona) from dynamic parameters obtained from vibration testing. The dataset consists of input-otput data generated using finite element (FE) simulations. A blank column has been used to separate input data from output data. More details on the nature of the data and how it was employed can be found in the following journal article, which is supplemented by this upload: Makoond N, Pelà L, Molins C. Robust estimation of axial loads sustained by tie-rods in historical structures using Artificial Neural Networks. Structural Health Monitoring. 2022;0(0). doi:<a href="https://doi.org/10.1177/14759217221123326">10.1177/14759217221123326</a> <a href="https://www.researchgate.net/publication/364098652_Robust_estimation_of_axial_loads_sustained_by_tie-rods_in_historical_structures_using_Artificial_Neural_Networks">Link to author's version of accepted manuscript</a> This work was supported by the Servei del Patrimoni Arquitectònic of the Generalitat de Catalunya through a project (managed by the City Council of Sant Cugat) aimed at monitoring the church of the Monastery of Sant Cugat (grant number C-10764). Financial support is also acknowledged from the Ministry of Science, Innovation and Universities of the Spanish Government and the ERDF (European Regional Development Fund) through the SEVERUS project (Multilevel evaluation of seismic vulnerability and risk mitigation of masonry buildings in resilient historical urban centres) (grant number RTI2018-099589-B-I00).

Estimation of reinforced urn processes under left-truncation and right-censoring

We propose a nonparametric estimator for bivariate left-truncated and right-censored (LTRC) observations that combines the Expectation-Maximization (EM) algorithm and the Reinforced Urn Process (RUP). The resulting Expectation-Reinforcement (ER) algorithm allows for the inclusion of experts' knowledge in the form of a prior distribution, thus belonging to the class of Bayesian models. This can be relevant in applications where the data is incomplete, due to biases in the sampling process, as in the case of left-truncation and right-censoring. With this new approach, the distribution of the truncation variables is also recovered, granting further insight into those biases, and playing an important role in applications like prevalent cohort studies. The estimators are tested numerically using artificial and empirical datasets and compared with other methodologies such as copula models and the Kaplan-Meier estimator.

Estimating household preferences for coastal flood risk mitigation policies under ambiguity

Risk mitigation policies (like dike rising) are essential to address increasing coastal flood risks due to global warming. Furthermore, the optimal level of risk mitigation policy should be determined by public preferences for risk reduction. However, it is difficult to reveal public preferences for coastal flood risk reduction because projections of coastal flood risks inevitably involve uncertainty. This study aims to estimate household preference for coastal flood reduction under ambiguity and multiple projections of coastal flood risks. By coupling storm surge inundation simulations and stated preference experiments with decision models, we estimate the expected loss reduction, risk premium, and ambiguity premium for coastal flood risk mitigation policies. Results of the study show that ignoring the ambiguity premium causes significant undervaluation of coastal flood risk mitigation, and the ambiguity premium stems from households' over-concern about the worst projection, which may lead to an over-allocation of resources to prevent inundation damage caused from the worst-case flood before a disaster. The study concludes that a risk mitigation policy combining public insurance for the worst projection and pre-disaster prevention measures can be effective and efficient.

spectre: An R package to estimate spatially-explicit community composition using sparse data

An understanding of how biodiversity is distributed across space is key to much of ecology and conservation. Many predictive modelling approaches have been developed to estimate the distribution of biodiversity over various spatial scales. Community modelling techniques may offer many benefits over single-species modelling. However, techniques capable of estimating precise species makeups of communities are highly data intensive and thus often limited in their applicability. Here we present an R package, spectre, which can predict regional community composition at a fine spatial resolution using only sparsely sampled biological data. The package can predict the presence and absence of all species in an area, both known and unknown, at the sample site scale. Underlying the spectre package is a min-conflicts optimisation algorithm that predicts species' presences and absences throughout an area using estimates of α-, β-, and γ-diversity. We demonstrate the utility of the spectre package using a spatially-explicit simulated ecosystem to assess the accuracy of the package's results. spectre offers a simple-to-use tool with which to accurately predict community compositions across varying scales, facilitating further research and knowledge acquisition into this fundamental aspect of ecology.

Figure 5. Bayesian phylogeny, with species divergence age estimates reconstructed with BEAST using all the 26 in Complete mitochondrial genomes from museum specimens clarify millipede evolution in the Eastern Arc Mountains

Figure 5. Bayesian phylogeny, with species divergence age estimates reconstructed with BEAST using all the 26 mitochondrial genomes generated in this study. The dataset was supplemented with Thyropygus sp. and Abacion magnum as outgroups, derived from GenBank. GenBank accession numbers are provided in parentheses. Blue bars indicate the 95% highest probability density intervals for node ages. Age estimation for lineage divergence was based on a general arthropod mitochondrial DNA substitution rate and should be considered with caution. *Thyropygus sp. (red font) is very likely to be a misidentification; for more information, see the Discussion.

opencc-by-4.0Oct 2022View details →

data sets of "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"

data sets from "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland" Monthly means ozone profiles data sets of MCH homogenized Dobson D051 and of Brewer B040 used in the article entitled: "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland" by Eliane Maillard Barras, Alexander Haefele, René Stübi, Achille Jouberton, Herbert Schill, Irina Petropavlovskikh, Koji Miyagawa, Martin Stanek, and Lucien Froidevaux. <a href="https://doi.org/10.5194/acp-2022-344">https://doi.org/10.5194/acp-2022-344</a>

Distance estimation in the Goldfish (Carassius auratus)

Neurophysiological advances have given us exciting insights into the systems responsible for spatial mapping in mammals. However, we are still lacking information on the evolution of these systems and whether the underlying mechanisms identified are universal across phyla, or specific to the species studied. Here we address these questions by exploring whether a species that is evolutionarily distant from mammals can perform a task central to mammalian spatial mapping – distance estimation. We developed a behavioural paradigm allowing us to test whether goldfish (Carassius auratus) can estimate distance and explored the behavioural mechanisms that underpin this ability. Fish were trained to swim a set distance within a narrow tank covered with striped pattern. After changing the background pattern, we found that goldfish use the spatial frequency of their visual environment to estimate distance; doubling the spatial frequency of the background pattern resulted in a large overestimation of the swimming distance. These results provide robust evidence that goldfish can accurately estimate distance, and show that they use local optic flow to do so. These results provide a compelling basis to utilise goldfish as a model system to interrogate the evolution of the mechanisms that underpin spatial cognition, from brain to behaviour.