cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.bqxd9n
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Placamen chloroticum, Venus chlorotica, Venus sidneyensis)",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059566-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14217190
title: Berita UNPAM
DOI: 10.15468/dl.b2zba3
title: Occurrence Download

description: A dataset containing 955 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to 75.0",
 "DecimalLatitude is less than or equal to 80.0",
 "DecimalLongitude is greater than or equal to -100.0",
 "DecimalLongitude is less than or equal to -95.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 955 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059565-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4231/71r5-ej83
title: Data Deposit - Efficacy of baits and lures for weasel detection

description: &lt;p&gt;Researchers across the midwestern and eastern United States compared different combinations of baits and lures to determine which would be most effective at attracting weasels (&lt;em&gt;Mustela erminea&lt;/em&gt;, &lt;em&gt;Mustela nivalis&lt;/em&gt;, &lt;em&gt;Neogale frenata&lt;/em&gt;) to camera traps. Data were collected from 56 sites in 2022 and 66 sites in 2023 located in 14 states. Data were collected from January to June. Each site contained a cluster of 4 cameras baited with 2 different baits or lures. Two of the 4 cameras also had an extra lure (ground mouse bait in 2022 and salmon oil in 2023) added to the original 2 attractants. Sites were separated into northern and southern regions, as &lt;em&gt;Mustela erminea&lt;/em&gt; is only located in the Northern region.&lt;/p&gt;

&lt;p&gt;The WeaselBait_LogisticRegression_Data dataset was used to conduct logistic regression models to determine if the type of bait/lure present at a camera affected its ability to detect the presence of different weasel species.&lt;/p&gt;

&lt;p&gt;The WeaselBait_SurvivalAnalysis_Data dataset was used to conduct survival analysis to determine how quickly cameras with different bait/lure types observed different weasel species.&lt;/p&gt;
description: Weasel presence data collected using camera traps set in 66 sites (located in 14 states in eastern USA) during 2022-2023. Each site contained a cluster of 4 cameras baited with 2 different baits or lures, 2 cameras also had an extra lure applied.
DOI: 10.4231/cy5f-mr37
title: Data for elasto-capillary fluid-structure interaction with compound droplets

description: &lt;p&gt;&lt;strong&gt;Overview&lt;/strong&gt;: This dataset includes simulation data for elasto-capillary fluid-structure interaction (FSI) problems involving three immiscible fluids. Our computational model employs a phase-field method for the governing equations of the fluids and a large deformation neo-Hookean model for the solid. Our focus is on micro- and nano-scale problems, where capillary forces at fluid-fluid interfaces significantly deform the solid.&lt;br /&gt;
&lt;br /&gt;
&lt;strong&gt;Context and Objective: &lt;/strong&gt;The primary objective of this dataset is to demonstrate the effectiveness of our computational model, algorithm, and numerical formulation in simulating elasto-capillary FSI problems. Our simulations can help us better understand the dynamic interactions between compound droplets and deformable solids in many applications, such as pharmaceutics, biology, atmospheric chemistry, and food industry.&lt;br /&gt;
&lt;br /&gt;
&lt;strong&gt;Data Collection: &lt;/strong&gt;The data in this repository were collected from numerical simulations of multi-component FSI model. Detailed information about the model can be found in the published manuscript [1]. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;1. S. R. Bhopalam, J. Bueno and H. Gomez, Elasto-capillary fluid&ndash;structure interaction with compound droplets, &lt;em&gt;Computer Methods in Applied Mechanics and Engineering&lt;/em&gt;, 2022, 400, 115507. (&lt;a href="https://doi.org/10.1016/j.cma.2022.115507" rel="nofollow noopener noreferrer" target="_blank"&gt;https://doi.org/10.1016/j.cma.2022.115507&lt;/a&gt;)&lt;/p&gt;
description: Computational modeling of elasto-capillarity in computational mechanics: a study of fluid-structure interaction phenomena driven by capillary forces at fluid-fluid interfaces.
DOI: 10.4231/hghv-6c15
title: Database of case studies of earthquake-induced cracking of earth dams (1905 to 2023)

description: &lt;p&gt;This dataset, in Excel format, contains high-quality information gathered by Jimenez-Pirajan et al. (2024) from case studies on earthquake-induced cracking of earth dams. It includes 385 dams and 21 earthquakes from 2000 to 2023. The database by Pells and Fell (2002), which contains 305 case studies, is also included after being modified for consistency with the new data. The information in the dataset is categorized into three groups: Dam Information, Earthquake Information, and Damage Information. Each category is divided into different sub-categories that include parameters that were collected or calculated.&lt;/p&gt;

&lt;p&gt;With the dataset, there are two files: one detailing the description of each of the columns of the database, and the other providing organized references that are cited for each of the case studies. The information presented in the database can be used by designers to improve the seismic resilience of embankments and earth dams, as well as by researchers to improve our knowledge of the seismic response of compacted materials and to develop new numerical models or calibrate or verify existing ones.&lt;/p&gt;
description: Excel file with case studies of earthquake-induced cracking on earth dams from 1905 to 2023. It has the data from Pells and Fell (2002), that has been modified, and from Jimenez-Pirajan et al. (2024). It has information on the dam, earthquake, and damage
DOI: 10.4231/vv24-q615
title: Tracking the distribution and risk of tar spot of corn in Indiana from 2015 to 2022 - County Data from 2015 to 2022

description: &lt;p&gt;The focus of this survey was to document the presence of tar spot, independent of genotypic or management interactions, to establish the current intensity and distribution throughout Indiana and provide data for future risk prediction efforts.&lt;/p&gt;

&lt;p&gt;In each county, two or more farmers&rsquo; fields were scouted for tar spot and percent field incidence and average leaf severity were documented and county weather data was collected. &lt;/p&gt;

&lt;p&gt;As of the 2022 survey, the presence of tar spot has been confirmed in 86 counties in Indiana. Fields in northern Indiana have had the highest severity and the highest risk for the disease in the future. Pockets of tar spot outbreaks indicate that once the disease is found locally, favorable environmental conditions of moderate temperatures and fluctuating periods of seasonal moisture may increase the risk of a severe epidemic in a field in future years.&lt;/p&gt;

&lt;p&gt;Since the survey tar spot has now been confirmed in all 92 Indiana counties. &lt;/p&gt;

&lt;p&gt; &lt;/p&gt;

&lt;p&gt; &lt;/p&gt;
description: County data of tar spot distribution from 2015 to 2022 and weather data that includes precipitation, precipitation deviation from normal, temperature, temperature deviation from normal, and relative humidity for each county from 2019 to 2022.
DOI: 10.4231/vhb3-en13
title: Simulations, data, and scripts for Correlations for the interphase drag in the two-fluid model of gas-liquid flows through packed-bed reactors

description: &lt;p&gt;Two sets of simulation files are zipped into individual archives and named as &quot;Teflon_packing.zip&quot; and &quot;Glass_packing.zip&quot; corresponding to each type of packed-bed reactor packing material (Teflon or glass) of NASA&#39;s Packed Bed Reactor Experiment (PBRE). These zipped archives contain the simulation case files for all PBRE conditions considered by NASA. For example, the for each packing material, the NASA considered different flow conditions (specifically, they varied the gas and liquid mass flow rates, parameterized by the inlet gas and liquid superficial velocities, v&lt;sub&gt;gs&lt;/sub&gt; and v&lt;sub&gt;ls&lt;/sub&gt;) thus the archives &quot;Teflon_packing.zip&quot; and &quot;Glass_packing.zip&quot; each contain the simulation case files for many simulations, at different inlet gas and liquid flow rates. The different inlet gas and liquid flow are translated into inlet superficial velocities for each simulations. Thus, each simulations&#39; case files, are distinguished by the subfolder names within these archives (for example, the case files in the subfolder &quot;simulationB3_vls0p001644_vgs0p0003528_all_data&quot; correspond to the inlet conditions of v&lt;sub&gt;ls&lt;/sub&gt; = 0.001644 m/s v&lt;sub&gt;gs&lt;/sub&gt; = 0.0003528 m/s). These case files can be used to rerun the simulations using ANSYS Fluent 2022R1. Some selected output data files, which allows the user to identify the simulation that this data corresponds to in the manuscript, are also included.&lt;/p&gt;

&lt;p&gt;Post-processing was done using ANSYS Fluent to generate pressure drop data, which was tabulated in an Excel spreadsheet. This data was plotted using MATLAB R2019a. The archive &quot;Math_model_figs.zip&quot; contains the data, spreadsheet, and MATLAB scripts.&lt;/p&gt;

&lt;p&gt;Inferences based on these simulations and data, and a discussion of their meaning/implications, can be found in the manuscript (available as a preprint at &lt;a href="https://arxiv.org/abs/2409.10674" target="_blank"&gt;https://arxiv.org/abs/2409.10674&lt;/a&gt;).&lt;/p&gt;
description: Zip files containing simulations, data, and post-processing scripts, used to generate the figures in the manuscript "Correlations for the interphase drag in the two-fluid model of gas-liquid flows through packed-bed reactors" by Nagrani, et al.
DOI: 10.4231/sf17-yt14
title: Management context, but not pollinator dependence, shapes pollen limitation across diverse specialty crops

description: &lt;p&gt;We measured pollination services and pollen limitation over three years in five specialty crops in Indiana: apples, blueberries, watermelons, and tomatoes grown in field and high tunnel settings, allowing us to compare pollen limitation among specialty crops with diverse pollination requirements and management practices. We imposed three pollination treatments on crop flowers: i) pollinator exclusion, ii) open pollination, iii) supplemental hand pollination.&lt;/p&gt;

&lt;p&gt;We compared fruit set, weight, sugar content, and several crop-specific quality metrics between pollination treatments to quantify pollination services (difference between exclusion and open pollination) and pollen limitation (difference between open and hand pollination) for each crop. Separate .csv files are provided for data from each crop, with apple and high tunnel tomato datasets each split between two separate .csv files (fruit set in one file and all other response variables in the other).&lt;/p&gt;

&lt;p&gt;We analyzed data in R using generalized linear mixed models and linear mixed models. All analyses are included in a single .R file and grouped sequentially by response variable. &lt;/p&gt;
description: Data and code from pollination experiments quantifying pollination services and pollen limitation in Indiana apples, blueberries, watermelons, field tomatoes, and high tunnel tomatoes.
DOI: 10.4231/0jkd-a159
title: Multi-Year Study Maize Agrivoltaics Soil Moisture and Maize Information

description: &lt;p&gt;Soil moisture data is an important feature for agronomic work, and it speaks true for agrivoltaic work as well. This research specifically attempts to link spatial location of shading with changes in soil moisture. However, for a couple of years, the soil moisture data had to be approximated due to lack of/missing data. To do so, a multiple fold Bayesian Regularization Neural Network had to be employed to extrapolate the limited data we had from the latter end of the growing seasons in 2020 and 2021 to the beginning of the seasons. In this repository, one may find the original data, the volumetric water content collected from sensors used on the field. Additionally, the data used for training and extrapolation, which is climatic data downloaded from NREL&#39;s NSRDB: National Solar Radiation Database site (https://nsrdb.nrel.gov/). The coordinates were those of the Purdue&#39;s Agronomy Center for Research &amp; Education (ACRE) farm with 2 sq.km resolution for the duration of the growing period for each year at an hourly time resolution. While NSRDB gives information for the whole growing season, only the time interval which encompassed the season was used for these data. The variables include: DHI, DNI, Dew Point, Surface Albedo, Wind Speed, Relative Humidity, Temperature, Pressure, GHI, Cloud Type, Solar Zenith, and Precipitable Water. Lastly, the calculated data for the full growing season will be found.&lt;/p&gt;

&lt;p&gt;With V.2 out, this will include the plot, location, yield in both bu/acre and kg/ha, height in cm, the silking date and the silking-anthesis interval. These are relevant results to see how the treatment of the experimental setup impacted various crop parameters within an agrivoltaic setting.&lt;/p&gt;
description: Volumetric water content (m^3/m^3) data, maize yield, silking/anthesis, height data for agrivoltaic experimental setup. Here will also be the data which was inputed based off a k-fold Bayesian Regularization Neural Network.
DOI: 10.4231/01d8-v289
title: In-depth Interviews with Women Living in Italy to Understand Pregnancy, Birth, and Postpartum Experiences

description: &lt;p&gt;Researchers conducted 23 in-person interviews in English in May 2023. Participants included women living in or near Florence, Italy, aged 18 and over, and who had given birth in Italy. All interviews were transcribed verbatim.&lt;/p&gt;
description: The purpose of this study was to understand pregnancy and postpartum experiences, including healthcare decision-making, birth planning and execution, parental leave policies, breastfeeding, and social and cultural influences, among women in Italy.
DOI: 10.4231/d7gv-zm03
title: Data for: Qualitatively altered driven Dicke superradiance in extended systems due to infinitesimal perturbations

description: &lt;p&gt;This data is for a paper where we study aspects of the perturbed driven Dicke model. The driven Dicke model, with interesting quantum phases induced by parameterized driving, has been intensively studied in cavities, where permutation symmetry applies due to the atoms&rsquo; equal coupling to the field and identical interaction. As a result, the system, with proper initialization, can remain in a highly symmetric subset of the state space, where the photon emission of each atom constructively interferes with each other, leading to superradiance at steady state. However, because of the degeneracy of steady states for the driven Dicke model, the steady state can be qualitatively altered by an infinitesimal perturbation. In this work, we present data where we simulate superconducting qubits coupled to a 1D waveguide as the extended system and theoretically investigate four kinds of perturbations: local dephasing, individual driving phases, the separation between adjacent qubits, and individual detunings. Using an angular momentum basis, we predict the dimension of the degenerate subspace and study the transition within the subspace due to the perturbation.&lt;/p&gt;
description: Data used in the figures of (2024) titled "Qualitatively altered driven Dicke superradiance in extended systems due to infinitesimal perturbations" with short information in FILE_INFO.txt. Also contains .eps files and gnuplot routines.
DOI: 10.4231/dgrq-mc61
title: Data for Interannual Variability of Tropical Cyclone Landfalls in the Eastern North Pacific

description: &lt;p&gt;The dataset in this repository is the post-processed data needed to make the final Figures of the paper: &quot;Interannual Variability of Tropical Cyclone Landfalls in the Eastern North Pacific: Environmental Drivers and Implications.&quot;&lt;br /&gt;
The original data comes from IBTrACS (International Best Track Archive for Climate Stewardship project) and ERA5 (fifth-generation ECMWF reanalysis). &lt;/p&gt;

&lt;p&gt;The dataset presented here has undergone quality control measures, including removing storms originating from the Atlantic Ocean, selection based on intensity, and focusing on the Eastern North Pacific. It identifies the strongest landfalls for multiple landfalling storms. The dataset is trimmed spatially to include longitudes between 140&deg;W and 70&deg;W, and latitudes from -5&deg;S to 40&deg;N. The dataset covers specific periods, focusing only on high and low landfall years and the climatological mean. Additionally, the dataset includes the results of running the Storm Wind Model (Figure 2) and a 1000-step Bootstrapping for error estimations Figure 3a.&lt;/p&gt;

&lt;p&gt;&lt;br /&gt;
The Research Questions answered with this dataset are the following:&lt;/p&gt;

&lt;ul&gt;
 &lt;li&gt;What is the climatological seasonal cycle of TC landfalls in the ENP? To what extent do genesis anomalies versus steering wind anomalies control this cycle?&lt;/li&gt;
 &lt;li&gt;Where is the preferred location of landfall in the basin? Does this location depend on the yearly landfall probability?&lt;/li&gt;
 &lt;li&gt;Do the monsoonal winds, as the main source of westerlies in the basin, significantly contribute to the interannual variability of landfall in the ENP?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Resulting in the following key points:&lt;/p&gt;

&lt;ul&gt;
 &lt;li&gt;The Eastern North Pacific seasonal cycle has a bimodal distribution of tropical cyclone landfalls due to its orography.&lt;/li&gt;
 &lt;li&gt;During high landfall years, tropical cyclones make landfall in vulnerable Southern Mexico due to a stronger reversal of the easterlies.&lt;/li&gt;
 &lt;li&gt;Steering wind anomalies are the main driver for landfall interannual variability in the East Pacific.&lt;/li&gt;
&lt;/ul&gt;
description: This dataset contain the data and the code needed for plot the figures in the paper titled "Interannual Variability of Tropical Cyclone Landfalls in the Eastern North Pacific: Environmental Drivers and Implications"
DOI: 10.4231/z5rs-kz82
title: Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model

description: &lt;p&gt;For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market-penetration level of truck platoons. The established framework uses a robust 3D pavement model, along with the AASHTOWare Mechanistic&ndash;Empirical Pavement Design Guidelines (MEPDG) transfer functions to compute pavement distresses. However, transfer functions include high variability and lack physical significance. Therefore, as an improvement to effectively predict permanent deformation, this study utilized a conventional Burger&rsquo;s model, incorporating a nonlinear power-law dashpot, in lieu of a transfer function. Key components, including stress increments and the Jacobian, were derived for implementation in ABAQUS as a user subroutine. Model parameters were determined through asphalt concrete (AC) flow number and dynamic modulus tests. Using a nonlinear power-law dashpot, the model accurately characterized rutting under varying conditions. The Burger&rsquo;s model was both verified and validated to check the accuracy of implementation and representative of the actual behavior, respectively. Initially developed in 1D domain, the validated Burger&rsquo;s model was integrated into the robust 3D finite element (FE) pavement model to predict permanent deformation. A new load-pass approach (LPA) enabled reduction in computational domain and cost, along with implementing transient loads more efficiently. The combined integration of the LPA and the Burger&rsquo;s model into the pavement model effectively captured the rutting progression per loading cycle. Moreover, a graph neural network (GNN) was established to extend the prediction power of the framework, while strategically limiting the FE numerical matrix. The FE model data was transformed into a graph structure, converting FE model components into corresponding graph nodes and edges. The GNN-based pavement simulator (GPS) was developed to model 3D pavement responses, integrating three key components: encoder, processor, and decoder. The GPS model employed two-layer multilayer perceptrons (MLP) for the encoder and decoder, while utilizing graph network (GN) technology for the processor. Validation occurred through two case studies&mdash;OneStep and Rollout&mdash;with results compared against FE model data as ground truth. Results demonstrated that the GPS model provides an accurate and computationally efficient alternative to traditional 3D pavement FE simulations.&lt;/p&gt;
description: This study utilized a conventional Burger’s model, incorporating a nonlinear power-law dashpot. A new load-pass approach enabled a reduction in computational domain and cost. A graph neural network was established to extend the framework.
DOI: 10.4231/ctm4-vy71
title: Datasets for analyzing lunar impact bombardment during the Imbrian

description: &lt;p&gt;These datasets contain results from a modeling study using the Cratered Terrain Evolution Model (CTEM). CTEM is a Monte Carlo impact bombardment model that emplaces craters on a surface. For this study, CTEM was used to investigate the impact bombardment rate on the Moon during the Imbrian period (3-3.9 billion years ago) by examining impact melts at model locations corresponding to Apollo sites. The &quot;results_data&quot; directory contains aggregated model results from CTEM runs in the form of .csv files. The raw CTEM outputs are very large files and unable to be stored on this repository, so the important results were aggregated in the csv files. The &quot;run_environment&quot; folder contains all necessary files to reproduce similar raw outputs to this study using CTEM (which is also provided in this repository). They will not be exactly the same due to the way random number seeding is handled. The &quot;ray_generation_data&quot; directory includes raw CTEM outputs that can be used to show proximal and distal ejecta for a single crater, emphasizing the ray pattern in the distal ejecta. &lt;/p&gt;

&lt;p&gt;These datasets are expected to work with the Python scripts provided here and at https://github.com/MintonGroup/imbrian_analysis .&lt;/p&gt;
description: These datasets include results from a Monte Carlo model used to study lunar impact bombardment at the Apollo sites during the Imbrian, as well as inputs to produce similar data using the model.