cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/889w8yxbr8
title: Segment Three Data of Persian Gulf Industrial Water Transfer to Yazd

description: This dataset contains the monthly planned and actual progress data for Segment Three of the Persian Gulf Water Transfer to Southeast Industries project, covering the period from April 2018 (Farvardin 1397) to December 2020 (Azar 1399). Segment Three involves the transfer of industrial water from the Sarcheshmeh Mine area to the Chadormalu Steel Complex in Ardakan, Yazd. The project execution is divided into three construction sites, and the dataset records key metrics for each site, including monthly plans, actual monthly progress, cumulative planned progress, cumulative actual progress, and deviations between planned and actual cumulative progress. This data is crucial for monitoring, evaluating, and managing the progress of this significant industrial water supply initiative aimed at supporting mining and industrial activities in the region.
DOI: 10.17632/889w8yxbr8.1
title: Segment Three Data of Persian Gulf Industrial Water Transfer to Yazd

description: This dataset contains the monthly planned and actual progress data for Segment Three of the Persian Gulf Water Transfer to Southeast Industries project, covering the period from April 2018 (Farvardin 1397) to December 2020 (Azar 1399). Segment Three involves the transfer of industrial water from the Sarcheshmeh Mine area to the Chadormalu Steel Complex in Ardakan, Yazd. The project execution is divided into three construction sites, and the dataset records key metrics for each site, including monthly plans, actual monthly progress, cumulative planned progress, cumulative actual progress, and deviations between planned and actual cumulative progress. This data is crucial for monitoring, evaluating, and managing the progress of this significant industrial water supply initiative aimed at supporting mining and industrial activities in the region.
DOI: 10.17632/6r2fzbb722
title: Dataset of "Poroelastic effects on the nucleation process of dynamic fault rupture during fluid injection"

description: The dataset used for the publication titled "Poroelastic effects on the nucleation process of dynamic fault rupture during fluid injection".
DOI: 10.17632/6r2fzbb722.1
title: Dataset of "Poroelastic effects on the nucleation process of dynamic fault rupture during fluid injection"

description: The dataset used for the publication titled "Poroelastic effects on the nucleation process of dynamic fault rupture during fluid injection".
DOI: 10.17632/v7twn9f3kp
title: Sp_dt_indices_yield_2025_OU_dataset

description: The data contains tuber yield data for 14 sweetpotato accessions phenotyped under irrigated (control) and drought conditions. The data was generated from field experiments carried out to evaluate the effect of drought and irrigation on the tuber yield of the fourteen sweetpotato accessions. The field experiment was laid in a randomised complete block design in three replicates per treatment. Irrigation was withdrawn at 103 days after planting to simulate drought for 6 weeks while the control was continuously irrigated. The data proffers information on the effect of climate change (drought) on the root -crop sweetpotato 
by stating the yield of each accession under conditions of drought and irrigation and helps to identify the drought-tolerant accessions of sweet potato.
DOI: 10.17632/v7twn9f3kp.1
title: Sp_dt_indices_yield_2025_OU_dataset

description: The data contains tuber yield data for 14 sweetpotato accessions phenotyped under irrigated (control) and drought conditions. The data was generated from field experiments carried out to evaluate the effect of drought and irrigation on the tuber yield of the fourteen sweetpotato accessions. The field experiment was laid in a randomised complete block design in three replicates per treatment. Irrigation was withdrawn at 103 days after planting to simulate drought for 6 weeks while the control was continuously irrigated. The data proffers information on the effect of climate change (drought) on the root -crop sweetpotato 
by stating the yield of each accession under conditions of drought and irrigation and helps to identify the drought-tolerant accessions of sweet potato.
DOI: 10.17632/cx6y3fxt5s
title: Expression of estrus in dairy cows: phenotyping, genetic variability, and association with productive and reproductive performance

description: Dairy cow estrus is marked by acute changes in behavior, such as increased activity and decreased rumination. These changes can be measured on the farm using wearable sensors and other monitoring systems. The main goals of this study were to quantify the expression of estrus in lactating dairy cows, evaluate its genetic variability, and assess its genetic correlation with milk production and fertility responses. Data consisted of 46,048,112 bi-hourly activity and rumination records collected using automated monitoring devices in 10,335 lactating dairy cows during 2 years on a large commercial farm. We developed an algorithm that quantifies changes in activity and rumination to infer the duration and intensity of estrus. First, we evaluated the association between estrous traits and pregnancy success using alternative logistic regression models, adjusting by lactation, year-season, and sex-sorted or conventional semen. The statistical analysis showed that the addition of a detected estrus (binary variable, yes/no) contributes significantly to explaining pregnancy success, relative to insemination in the absence of a detected estrus. Similar results were found for estrous duration and intensity. Second, we assessed the heritability of estrous traits using a sliding 21-day window between 21-100 DIM. We also evaluated the heritability of number of estrus between 11-70 DIM (voluntary waiting period). The statistical models included herd-season and lactation as fixed effects and cow as a random effect. Heritability estimates for presence/absence of estrus were around 0.20±0.02 in the period 21-50 DIM and 0.09±0.03 in the period 50-100 DIM. Similar results were observed for the duration and intensity of estrus. The number of estrus during the voluntary waiting period (11-70 DIM) showed a heritability estimate of 0.23±0.02. Finally, we evaluated the relationship between estrous traits and productive performance, including average milk production in the period 11-70 DIM and also 305-day mature equivalent milk production. Estrous traits were all negatively correlated with productive traits, ranging from -0.14±0.06 to -0.37±0.05. Overall, the duration and intensity of estrus can be measured in a digital, standardized, and scalable framework, and these traits are significantly associated with pregnancy success. Notably, estrous behavior traits are heritable, which implies that breeding for improved expression of estrus has the potential to improve on-farm detection and increase insemination and pregnancy rates. Additionally, estrous traits are genetically unfavorably correlated with milk production.
DOI: 10.17632/cx6y3fxt5s.1
title: Expression of estrus in dairy cows: phenotyping, genetic variability, and association with productive and reproductive performance

description: Dairy cow estrus is marked by acute changes in behavior, such as increased activity and decreased rumination. These changes can be measured on the farm using wearable sensors and other monitoring systems. The main goals of this study were to quantify the expression of estrus in lactating dairy cows, evaluate its genetic variability, and assess its genetic correlation with milk production and fertility responses. Data consisted of 46,048,112 bi-hourly activity and rumination records collected using automated monitoring devices in 10,335 lactating dairy cows during 2 years on a large commercial farm. We developed an algorithm that quantifies changes in activity and rumination to infer the duration and intensity of estrus. First, we evaluated the association between estrous traits and pregnancy success using alternative logistic regression models, adjusting by lactation, year-season, and sex-sorted or conventional semen. The statistical analysis showed that the addition of a detected estrus (binary variable, yes/no) contributes significantly to explaining pregnancy success, relative to insemination in the absence of a detected estrus. Similar results were found for estrous duration and intensity. Second, we assessed the heritability of estrous traits using a sliding 21-day window between 21-100 DIM. We also evaluated the heritability of number of estrus between 11-70 DIM (voluntary waiting period). The statistical models included herd-season and lactation as fixed effects and cow as a random effect. Heritability estimates for presence/absence of estrus were around 0.20±0.02 in the period 21-50 DIM and 0.09±0.03 in the period 50-100 DIM. Similar results were observed for the duration and intensity of estrus. The number of estrus during the voluntary waiting period (11-70 DIM) showed a heritability estimate of 0.23±0.02. Finally, we evaluated the relationship between estrous traits and productive performance, including average milk production in the period 11-70 DIM and also 305-day mature equivalent milk production. Estrous traits were all negatively correlated with productive traits, ranging from -0.14±0.06 to -0.37±0.05. Overall, the duration and intensity of estrus can be measured in a digital, standardized, and scalable framework, and these traits are significantly associated with pregnancy success. Notably, estrous behavior traits are heritable, which implies that breeding for improved expression of estrus has the potential to improve on-farm detection and increase insemination and pregnancy rates. Additionally, estrous traits are genetically unfavorably correlated with milk production.
DOI: 10.82522/n1cq-bw62
title: HERSS-MAIZE 2024; High-rEsolution dRone dataset for Site-Specific weed wanagement in Maize (San Piero - Pisa, Tuscany, Italy)

description: Weed management (WM) is recognised as one of the major challenges in modern agriculture, particularly within the framework of the Farm to Fork strategy, which aims to reduce pesticide use by 50% by 2030 while maintaining high crop productivity. Precision agriculture techniques, and especially Site-Specific Weed Management (SSWM), have emerged as promising approaches to achieve these goals by optimising post-emergence herbicide application according to the spatial distribution of weeds. The SSWM process is characterised by three fundamental phases. The weed detection (WD) process, the estimation of potential damage (PD), and the precision herbicide application (PHA) are supported by advanced sensing, imaging, and artificial intelligence (AI) methods.
The HERSS-MAIZE dataset was collected over a maize experimental field measuring approximately 0.8 ha, located in San Piero (Pisa, Tuscany, Italy), during the crop's critical period of susceptibility to weed competition. A total of thirteen UAV surveys were conducted using two different drone platforms—DJI MINI 3 PRO and DJI Mavic 3M—to compare data quality and assess the feasibility of SSWM under varying technological conditions. All acquired images were ortho-rectified to produce 13 high-resolution orthomosaics, representing temporal variations in vegetation cover and weed development. The dataset also incorporates flight metadata, sensor characteristics, and georeferencing information, thereby facilitating integration into Geographic Information System (GIS) environments or utilisation in machine learning and deep learning workflows for weed detection and crop monitoring.
The primary objective of the HERSS-MAIZE dataset is to provide a high-resolution, multi-temporal UAV-based resource for developing, validating, and benchmarking algorithms and methodologies for Weed Detection and Site-Specific Weed Management in maize. The incorporation of data from both professional and consumer-grade UAVs within the dataset facilitates research into scalable, cost-effective precision agriculture solutions that are aimed at reducing herbicide use and enhancing environmental sustainability. The objective of HERSS-MAIZE is to facilitate advancements in the following areas: AI-driven weed mapping, adaptive management zone delineation, and precision spraying strategies aligned with sustainable agricultural practices.
DOI: 10.13140/rg.2.2.33265.54882
title: Ordinal FFH Excel 2025
DOI: 10.15159/eds.dt.25.04
title: Sulfoxaflor modulates diet-dependent effects on bumble bee development but shows no detectable effects on adult physiology : [raw data]

description: The raw data is associated with the paper "Sulfoxaflor modulates diet-dependent effects on bumble bee development but shows no detectable effects on adult physiology" By Margret Jürison, Pilleriin Teras, Kaarel Pent, Vivika Väli, Risto Raimets, Sigmar Naudi, Angela Ploomi, Marika Mänd, Reet Karise 1. raw_data__mortality.csv This dataset contains the raw survival data used for the Kaplan-Meier survival analysis in the associated publication. Each row represents one individual bumble bee. The columns are described as follows: Treatment: The diet or exposure group Time: The survival time recorded for the individual (e.g., days). Status: Survival outcome, where 1 = dead and 0 = censored (alive at end of observation). 2. raw_data_respiratory_measurements.xlsx This file contains the CO2 measurements (in µmol/mol) recorded in the study. Each column corresponds to a single bumble bee, and each sheet represents a different treatment group. The columns are described as follows: time_s: time in seconds at which the LI-7000 (LiCor, Lincoln, NE) device logged CO2 release in bumble bees co2_...: CO2 measurements for each individual bee
DOI: 10.15468/dl.xfknrk
title: Occurrence Download

description: A dataset containing 216 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ostryopsis nobilis"
 ]
}

 The dataset includes 216 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044938-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5518/155
title: Dataset associated with 'Crystallographic Structure, Intermolecular Packing Energetics, Crystal Morphology and Surface Chemistry of Salmeterol Xinafoate (Form I)'

description: Single crystals of salmeterol xinafoate (form I), prepared from slow cooled supersaturated propan-2-ol solutions, crystallize in a triclinic P1¯ symmetry with 2 closely related independent salt pairs within the asymmetric unit, with an approximately double-unit cell volume compared with the previously published crystal structure. Synthonic analysis of the bulk intermolecular packing confirms the similarity in packing energetics between the 2 salt pairs. The strongest synthons, as expected, are dominated by coulombic interactions. Morphologic prediction reveals a plate-like morphology, dominated by the {001}, {010}, and {100} surfaces, consistent with experimentally grown crystals. Although surface chemistry of the slow-growing {001} face comprises large sterically hindering phenyl groups, although weaker coulombic interactions still prevail from the alcohol group present on the phenyl and hydroxymethyl groups. The surface chemistry of the faster growing {010} and {100} faces are dominated by the significantly stronger cation/anion interactions occurring between the carboxylate and protonated secondary ammonium ion groups. The importance of understanding the cohesive and adhesive nature of the crystal surfaces of an active pharmaceutical ingredient, with respect to their interaction with other active pharmaceutical ingredient crystals and how that may affect formulation design, is highlighted.
DOI: 10.6084/m9.figshare.30656153
title: We collected the data before and after the intervention.

description: <b>Comparing the Effects of Canagliflozin and Pioglitazone on </b><b>metabolic dysfunction-associated steatotic liver disease (MASLD)</b><b> in Patients with Type 2 Diabetes: A Prospective Open-Label Clinical Controlled Trial</b>
DOI: 10.6084/m9.figshare.30656153.v1
title: We collected the data before and after the intervention.

description: <b>Comparing the Effects of Canagliflozin and Pioglitazone on </b><b>metabolic dysfunction-associated steatotic liver disease (MASLD)</b><b> in Patients with Type 2 Diabetes: A Prospective Open-Label Clinical Controlled Trial</b>
DOI: 10.15468/dl.2jnnyd
title: Occurrence Download

description: A dataset containing 45929 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panthera"
 ]
}

 The dataset includes 45929 records from 425 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044942-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e240465
title: Dataset and Code for Strategic Integration of Developing Countries in Green Global Production Networks: The Differential Impact of Exchange Rate Policies on International Renewable Energy Investment

description: This repository contains the dataset and code scripts used in the study, "Strategic Integration of Developing Countries in Green Global Production Networks: The Differential Impact of Exchange Rate Policies on International Renewable Energy Investment." It uses panel random effects and Difference-in-Differences models to provide robust, causal evidence that exchange rate policies significantly and differentially moderate the flow of International Renewable Energy Investment into developing countries.
DOI: 10.3886/e240465v1
title: Dataset and Code for Strategic Integration of Developing Countries in Green Global Production Networks: The Differential Impact of Exchange Rate Policies on International Renewable Energy Investment

description: This repository contains the dataset and code scripts used in the study, "Strategic Integration of Developing Countries in Green Global Production Networks: The Differential Impact of Exchange Rate Policies on International Renewable Energy Investment." It uses panel random effects and Difference-in-Differences models to provide robust, causal evidence that exchange rate policies significantly and differentially moderate the flow of International Renewable Energy Investment into developing countries.
DOI: 10.5281/zenodo.17648992
title: DATA - Niche construction truffles decarbonated soils - LG Garcia-Montero - v.7
DOI: 10.5281/zenodo.17648993
title: DATA - Niche construction truffles decarbonated soils - LG Garcia-Montero - v.7
DOI: 10.5281/zenodo.11142223
title: Large-scale Forest Stand Height mapping for the northeast of U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI

description: The forest height mosaic for the northeastern parts of China and U.S are generated based on a global-to-local inversion approach proposed in (Yu et al., 2023) by making use of Spaceborne repeat-pass InSAR and spaceborne GEDI data. The sparsely but extensively distributed LiDAR samples provided by NASA’s GEDI mission are used to parametrize the semi-empirical repeat-pass InSAR scattering model(Lei et al., 2017) and to obtain forest height estimates. Compared to our previous efforts (Lei et al., 2018, Lei and Siqueira, 2022), this paper further removes the assumptions that were made given the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage inversion scheme. This approach allows a better use of local GEDI samples to achieve finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion.  This approach is further fully automated to enable a large-scale forest mapping capability. Two forest height mosaic maps were generated for the entire northeastern regions of U.S. and China with total area of 18 million hectares and 112 million hectares, respectively. The validation of the forest height estimates demonstrates much improved accuracy achieved by the proposed approach compared to the previous efforts i.e., reducing from RMSE of 3-4 m on the order of 3-6-hectare aggregated pixel size to RMSE 3-4 m on the order of 0.81-hectare pixel size. The proposed fusion approach not only addresses the sparse spatial sampling problem inherent to the GEDI mission, but also improve the accuracy of forest height estimates compared to the GEDI-interpolated maps by a factor of 20% at 30-m resolution. The extensive evaluation of forest height inversion against LVIS LiDAR data indicates an accuracy 3-4 m on the order of 0.81 hectare over smooth areas and 4-5 m over hilly areas in U.S., whereas the forest height estimates over northeastern China are best compared with small footprint LiDAR validation data even at an accuracy of even below 3.5 m with R2 mostly above 0.6. Such a forest height inversion accuracy at sub-hectare pixel size provides promising values towards the existing and future spaceborne LiDAR (JAXA’s MOLI, NASA’s GEDI, China’s TECIS) and InSAR missions (NASA-ISRO’s NISAR, JAXA’s ALOS-4 and China’s LuTan-1). This fusion prototype can work as a cost-effective solution for public users to obtain a wall-to-wall forest height mapping at large scale when only spaceborne repeat-pass InSAR data are available and freely accessible.
DOI: 10.5281/zenodo.17648121
title: Large-scale Forest Stand Height mapping for the northeast of U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI

description: The forest height mosaic for the northeastern parts of China and U.S are generated based on a global-to-local inversion approach proposed in (Yu et al., 2023) by making use of Spaceborne repeat-pass InSAR and spaceborne GEDI data. The sparsely but extensively distributed LiDAR samples provided by NASA’s GEDI mission are used to parametrize the semi-empirical repeat-pass InSAR scattering model(Lei et al., 2017) and to obtain forest height estimates. Compared to our previous efforts (Lei et al., 2018, Lei and Siqueira, 2022), this paper further removes the assumptions that were made given the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage inversion scheme. This approach allows a better use of local GEDI samples to achieve finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion.  This approach is further fully automated to enable a large-scale forest mapping capability. Two forest height mosaic maps were generated for the entire northeastern regions of U.S. and China with total area of 18 million hectares and 112 million hectares, respectively. The validation of the forest height estimates demonstrates much improved accuracy achieved by the proposed approach compared to the previous efforts i.e., reducing from RMSE of 3-4 m on the order of 3-6-hectare aggregated pixel size to RMSE 3-4 m on the order of 0.81-hectare pixel size. The proposed fusion approach not only addresses the sparse spatial sampling problem inherent to the GEDI mission, but also improve the accuracy of forest height estimates compared to the GEDI-interpolated maps by a factor of 20% at 30-m resolution. The extensive evaluation of forest height inversion against LVIS LiDAR data indicates an accuracy 3-4 m on the order of 0.81 hectare over smooth areas and 4-5 m over hilly areas in U.S., whereas the forest height estimates over northeastern China are best compared with small footprint LiDAR validation data even at an accuracy of even below 3.5 m with R2 mostly above 0.6. Such a forest height inversion accuracy at sub-hectare pixel size provides promising values towards the existing and future spaceborne LiDAR (JAXA’s MOLI, NASA’s GEDI, China’s TECIS) and InSAR missions (NASA-ISRO’s NISAR, JAXA’s ALOS-4 and China’s LuTan-1). This fusion prototype can work as a cost-effective solution for public users to obtain a wall-to-wall forest height mapping at large scale when only spaceborne repeat-pass InSAR data are available and freely accessible.