Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

StageIV-IRC – A High-resolution Dataset of Extreme Orographic Quantitative Precipitation Estimates (QPE) Constrained to Water Budget Closure for Historical Floods in the Appalachian Mountains

<h2>Quantitative Flood Estimation (QFE) in complex terrain remains a grand challenge in operational hydrology due to the lack of accurate high-resolution Quantitative Precipitation Estimates (QPE) at spatial and temporal resolutions needed to capture the variability of orographic precipitation, and where radar-based QPE are available there are significant biases due to the geometry and constraints of radar operations.  Here, we present a high-resolution (i.e. 250m, 5minute-hourly) QPE dataset for the most extreme (flood-producing) events from 2008 to 2024 for 26 gauged basins (in total 215 events) in the Appalachian mountains constrained to meet basin-scale water budget closure through inverse rainfall-runoff modeling to correct the Next Generation Weather Radar (NEXRAD) Stage IV analysis (4km resolution, hourly) using a fully-distributed uncalibrated hydrological model that leverages recent advances in hydrologic modeling in mountainous regions (e.g. improved river routing and initial soil moisture estimation) (Liao and Barros, 2024a and 2024b). The corrected Stage IV analysis is referred to as StageIV-IRC (Inverse Rainfall Correction).  Previously, a subset of this dataset informed the construction of a generalized QPE error model (Liao and Barros, 2023), supporting the development of water budget closure constrained QPE and providing physics insights into orographic QPE uncertainties for various radar-based products at high resolution in complex terrain. The unique advantage of the StageIV-IRC QPE is that it achieves water budget closure at the storm-flood event scale within observational uncertainty of streamflow observations, that is the golden standard in hydrological modeling.  The QPE dataset is publicly available at:  <a href="https://doi.org/10.5281/zenodo.14028867">https://doi.org/10.5281/zenodo.14028867</a></h2>

opencc-by-4.0Oct 2023View details →

Dataset of processed Sentinel-2 images for chlorophyll-a estimation in high-altitude lakes in the Sierra Nevada, Spain

This dataset contains Sentinel 2 satellite images clipped to 5 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. The images were processed with the following atmospheric correction algorithms:<ul><li><a href="https://c2rcc.org/">C2RCC</a> (<a href="https://ui.adsabs.harvard.edu/abs/2016ESASP.740E..54B/abstract">Brockmann et al. 2016</a>)</li><li><a href="https://github.com/MarcYin/SIAC">SIAC</a> (<a href=" https://doi.org/10.5194/gmd-15-7933-2022">Yin et al. 2022)</a></li><li><a href="https://github.com/acolite/acolite/releases/tag/20221114.0">ACOLITE</a> (<a href="https://doi.org/10.1016/j.rse.2018.07.015">Vanhellemont & Ruddick, 2018</a>)</li><li><a href="https://grass.osgeo.org/grass83/manuals/i.atcorr.html">6SV</a> (<a href="https://doi.org/10.1109/36.581987">Vermote et al. 2006</a>)</li></ul>Included Lakes and and their IDs:<ul><li>Laguna de la Caldera (ID = P-2)</li><li>Laguna-embalse de las Yeguas (ID = D-6)</li><li>Laguna de Río Seco (ID = P-8)</li><li>Laguna Larga (ID = G-7)</li><li>Laguna de la Mosca (ID = G-11)</li></ul>

Data Sets: Estimating scalar turbulent fluxes with slow-response sensors in the stable atmospheric boundary layer

Date of data analysis: Statistical analyses conducted throughout the 2023 year  Information about funding sources that supported the collection of the data:The research was supported by the Cooperative Institute for Modeling the Earth System at Princeton University under Award NA18OAR4320123 from the National Oceanic and Atmospheric Administration, and by the US National Science Foundation under award number AGS 2128345. Also, it was supported by the National Defense Science and Engineering Graduate Fellowship from the U.S. Department of Defense and Army Research Office. Similarly, the National Science Foundation provided support to complete the PHOXMELT field studies (Grant PLR- 1417914) to collect the data. Also, the study was supported by the U.S. National Science Foundation (NSF-AGS-2028633) and the Department of Energy (DE-SC0022072).The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration.This dataset contains the observational data for the two field experiments (Barrow and Wendell) in .nc file format.

Non-methane volatile organic compound emissions over China estimated using TROPOMI HCHO retrievals

We used the Regional multi-Air Pollutant Assimilation System (RAPAS) with the EnKF algorithm to optimize daily NMVOC emissions in China by assimilating TROPOMI HCHO retrievals.  airqual.qc.csv includes assimilated and verified surface NO2 observations.HCHO.tar.gz includes assimilated TROPOMI HCHO retrievals.posterior_emission_27km.nc and  posterior_emission_mg_27km.nc includes inferred daily posterior anthropogenic and biogenic NMVOC emissions respectively for August 2022.

opencc-zeroNov 2023View details →

Data from: Non-invasive age estimation based on fecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins

Age is necessary information for the study of life history of wild animals. A general method to estimate the age of odontocetes is counting dental growth layer groups (GLGs). However, this method is highly invasive as it requires the capture and handling of individuals to collect their teeth. Recently, the development of DNA-based age estimation methods has been actively studied as an alternative to such invasive methods, of which many have used biopsy samples. However, if DNA-based age estimation can be developed from fecal samples, age estimation can be performed without touching or disrupting individuals, thus establishing an entirely non-invasive method. We developed an age estimation model using the methylation rate of two gene regions, GRIA2 and CDKN2A, measured through methylation-sensitive high-resolution melting (MS-HRM) from fecal samples of wild Indo-Pacific bottlenose dolphins (Tursiops aduncus). The age of individuals was known through conducting longitudinal individual identification surveys underwater. Methylation rates were quantified from 36 samples. Both gene regions showed a significant correlation between age and methylation rate. The age estimation model was constructed based on the methylation rates of both genes which achieved sufficient accuracy (after LOOCV: MAE = 5.08, R2 = 0.34) for the ecological studies of the Indo-Pacific bottlenose dolphins, with a lifespan of 40-50 years. This is the first study to report the use of non-invasive fecal samples to estimate the age of marine mammals.

Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"

This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories.

Data from: Methylation-based markers for the estimation of age in African Cheetah, Acinonyx jubatus

This is a dataset for methylation analyses in cheetah done by EpiTYPER mass array.

opencc-by-4.0Apr 2022View details →

Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets

This archive contains the empirical data analysed in the paper 'Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets' by Touzalin et al. (https://doi.org/10.24072/pci.ecology.100416).  The dataset is provided as a .Rdata file ('TLoss_GMdata.Rdata'), and full description of the content is provided in the file 'Readme_TLdata.csv'. All additional details are available in the main text (https://doi.org/10.24072/pci.ecology.100416) or in the supporting information (https://doi.org/10.5281/zenodo.10204538).

Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses

This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses".  Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:<ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated. Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.

opencc-zeroNov 2023View details →

Age estimation of captive Asian elephants (Elephas maximus) based on DNA methylation: An exploratory analysis using methylation-sensitive high-resolution melting (MS-HRM)

Age is an important parameter for bettering the understanding of biodemographic trends-development, survival, reproduction and environmental effects-critical for conservation. However, current age estimation methods are challenging to apply to many species, and no standardised technique has been adopted yet. This study examined the potential use of methylation-sensitive high-resolution melting (MS-HRM), a labour, time, and cost-effective method to estimate chronological age from DNA methylation in Asian elephants (Elephas maximus). The objective of this study was to investigate the accuracy and validation of MS-HRM use for age determination in long-lived species, such as Asian elephants. The average lifespan of Asian elephants is between 50-70 years but some have been known to survive for more than 80 years. DNA was extracted from 53 blood samples of captive Asian elephants across 11 zoos in Japan, with known ages ranging from a few months to 65 years. Methylation rates of two candidate age-related epigenetic genes, RALYL and TET2, were significantly correlated with chronological age. Finally, we established a linear, unisex age estimation model with a mean absolute error (MAE) of 7.36 years. This exploratory study suggests an avenue to further explore MS-HRM as an alternative method to estimate the chronological age of Asian elephants.

opencc-by-4.0Dec 2023View details →

Deliverable 2.3: Biodiversity impact estimates and documentation for indicators on multi-dimensional biodiversity aspects ready for use in WP3

To quantify human-driven impacts on biodiversity, we used advanced modeling techniques paired with a few key variables such as habitat quality, vegetation structure, climate, and topography to develop an innovative predictive model that estimates both 1) current biodiversity patterns and distributions and 2) a baseline model of biodiversity. By comparing both of these models, we are able to identify regions with significant human-driven reductions in species richness, endemism, and species composition across both of our focus regions, South America and Africa. The files provided in this repository (1 km2 resolution) include: 1- Species richness (number of species). 2- Endemism (species rarity). For this metric, we used the corrected weight endemism index, which is the inverse of a species’ range size and effectively assigns higher scores to species with more restricted distributions. For this index, we used species ranges found within our study area (of South America and Africa). 3- Species composition of vertebrates, invertebrates, and plants (which species occur in a given location). This metric, also known as beta diversity, summarizes which sets of species occur at each location. For this, we used the Sorensen index metric, as it does not depend on species absence data. These results provide a improve in previous deliverable 2.2.

opencc-zeroJan 2024View details →

Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models

Knowledge of individual age can help both in-situ and ex-situ conservation programs to design more efficient and suitable management plans for targeted wildlife species. DNA methylation is one of the epigenetic aging markers that has emerged as a promising tool that can estimate age with high accuracy using only a tiny amount of biological material, which can be collected in a minimally invasive way. Here, we sequenced five targeted genetic regions and used 8–23 selected CpG sites to build age estimation models with machine learning methods with about only $3–7 per sample, using blood samples of seven Felidae species—ranging from small to big, and domestic to endangered species: domestic cats (Felis catus, 139 samples), Tsushima leopard cats (Prionailurus bengalensis euptilurus, 84 samples), and five Panthera species (96 samples). The models built achieved satisfactory accuracy—the mean absolute error of the best models was 1.966, 1.348, and 1.552 years in domestic cats, Tsushima leopard cats, and Panthera spp., respectively. Our models in domestic cats and Tsushima leopard cats were applicable to individuals regardless of health conditions, indicating the high applicability of our models to samples collected from diverse situations, e.g., rescued individuals in the context of conservation. We also showed the possibility of developing universal age estimation models for the five Panthera spp. using two of the five genetic regions, suggesting an even lower cost to use our models for future applications.

openmit-licenseSep 2023View details →

Fast Radio Map Estimation and Automated Radio Network Design Using Deep Learning App and Dataset

In this research, we present the Deep Learning architecture encoder/fully-connected for estimate optimal desings WLANs in indoor scenarios. This architecture was implemented for WLAN structures consisting of 1, 2, 3, 4, and 5 access points, with the capability to perform the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>, but in a fast manner. <a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#general-dataset-structure">General Dataset Structure</a> A major initial difficulty for starting the research was the lack of data, in this case, indoor scenario floor plans, users posisitons and optimal designs for training the architecture. Therefore, it was necessary to create an appropriate database that would facilitate the respective trainings. The dataset was created in base the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>. This implementation was carried out in the MATLAB software <a href="https://github.com/johanflorez98/WLANs-optimal-designs-methodology">WLANs-optimal-designs-methodology</a>. Thus, this research provides a dataset that can be used for training multiple Deep Learning architectures and can facilitate future investigations into similar problems. We show a dataset composed by optimal designs for the 5GHz band WiFi in indoor scenarios: it has 102 indoor constructions plans and around 20 users distributions per plan floor as image and APs positions per case as coordinates. These distributions are random and several WLAN's structures: 1 to 5 access points. The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels. The pictures have a depth of 8 bits and size of 256pixels X 256pixels equivalents to indoor constructions of 20 X 20 m2. These ones make reference to offices's spaces at general or classroom. <a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#obtained-models">Obtained Models</a> To evaluate the obtained models, the dataset consisting of floor plans 911 to 102 can be used, with available user distributions for each case of APs configurations as a test set, or other new images can be used. To manipulate the codes better, click here in the <a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset">repository</a>.

opencc-by-4.0Jan 2024View details →

Supporting Dataset (Tables S1-S7, Figure S1) for "Heavy-mineral grain counting: Counting techniques, error estimation, and the number of grains to be counted"

This Dataset comprises Tables S1-S7 for the article "Heavy-mineral grain counting: Counting techniques, error estimation, and the number of grains to be counted" authored by Jan Schönig and submitted to Journal of Geophysical Research: Earth Surface. Figure S1: Error of different counting methods in comparison with theory and without considering the finite population correction Table S1: Heavy-mineral dataset from semi-automated Raman analysis Table S2: Summary of heavy-mineral composition for individual samples in percent Table S3: Computed ribbon compositions for individual samples and ribbon sizes given in numbers of counts Table S4: Relative errors at 95 % quantile for consecutive ribbon counting simulations Table S5: Relative errors at 95 % quantile for maximum distance ribbon counting simulations Table S6: Relative errors at 95 % quantile for cluster counting simulations Table S7: Numerical solution for determining the number of counts required to discriminate the content of two mineral species in an aliquot

opencc-by-4.0Sep 2023View details →

Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France

Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France.  * AGB and Volume maps are available on request. The methodology and validation of the maps are presented here: https://hal.science/hal-04249151 Please cite : David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151

opencc-zeroFeb 2024View details →

High-precision body mass estimators for small mammals: A case study in the Mesozoic

Body mass is a pivotal quantity in palaeobiology but must be estimated from an imperfect fossil record. We analyse the precision of skeletal predictors of mammalian body mass as a mean to inform the Mesozoic mammal record, including a new eutriconodont from North America. We focus on the critical small end of the size spectrum – critical because the earliest mammals were small, because small size persisted onto the stems of the major extant radiations, and because small mammals compose a large proportion of crown diversity. Linear regressions based on extant small mammals indicate a universal correlation of body mass with observed measurements, but with clear differences in precision. Postcranial predictors outperform jaw and dental metrics, with certain femoral joint dimensions providing surprisingly precise estimations. Overall, our data indicate small-mammal evolution during the Mesozoic unfolded in patterns of underappreciated complexity. Studying these dynamics is only possible when estimating body mass within a strict, highly focused phylogenetic context. The heuristic value of the estimators we provide here are not limited to the Mesozoic but are phylogenetically justified for any small-bodied mammal regardless of age.

opencc-by-4.0Mar 2024View details →

MLP parameter estimates

Multilayer perceptron (MLP) parameter estimates for the prediction of MYC, BCL2, and BCL6 gene expression in Diffuse Large B-cell Lymphoma (DLBCL)

opencc-by-4.0Mar 2024View details →

Supporting dataset for manuscript "Estimating the microarthropod diversity in cropping systems by comparing ecological indices across Europe" by Bigiotti et al. 2025

This dataset contains counts of mesofauna individuals classified as Biological Forms (BF). It represent the raw data used for all indices calculation in the manuscript entitled "Microarthropod communities' diversity for soil health assessment: comparing several ecological indices across Europe" by authors Gaia Bigiotti, Francesco Vitali, Stefano Mocali, Giovanni L’Abate, Eligio Malusà, Dawid Kozacki, Irena Bertoncelj, Morgane Ourry, Massimo Pugliese, Heinrich Maisel, Expedito Olimi, Maria Grazia Tommasini, Carlo Jacomini, and Lorenzo D’Avino.

openmit-licenseMar 2024View details →

Weekly noise estimate of the residual of the LDC2a data set

Weekly noise estimate of the residual of the LDC2a data set. The recovered Galactic binaries and massive black hole binaries are subtracted for each week.