cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4231/nqg5-5v79
title: North Central Region Household Data. NCR-Stat: Baseline Survey 2024

description: &lt;p&gt;The NCR-Stat: Baseline Survey 2024 is a collaborative initiative developed by the North Central Regional Center for Rural Development, the Southern Rural Development Center, and Auburn University. The survey explores essential topics related to household, business, and community well-being. It aims to learn more about the conditions, issues, and challenges faced by rural and urban communities and their residents in the North Central Region.&lt;/p&gt;

&lt;p&gt;Conducted as a 20-minute online survey through the Qualtrics platform, it covers a broad spectrum of topics, including household demographics, income, workforce participation, entrepreneurship, caregiving, housing, broadband access, migration and staying behavior, civic engagement, community belonging, health, food security, well-being, and environmental concerns. The survey was designed to maximize participation across a diverse range of household types, geographic locations, genders, ages, and racial and ethnic groups, ensuring comprehensive and balanced representation.&lt;/p&gt;

&lt;p data-end="2180" data-start="1232"&gt;Building on the 2022 survey, the NCR-Stat: Baseline Survey 2024 features both adapted and expanded questions, introducing new topics like climate change perceptions and the recreation economy. Data collection took place from August to November 2024, primarily through the Qualtrics platform, with additional responses gathered via computer-assisted telephone interviewing (CATI) in select states to reach offline populations. High data quality was maintained through attention-check questions and mandatory responses, with all collected data fully coded and de-identified to safeguard participant confidentiality.&lt;/p&gt;
description: The dataset covers essential topics related to household, business, and community well-being. It aims to learn more about the conditions, issues, and challenges faced by rural and urban communities and their residents in the North Central Region.
DOI: 10.4231/sznq-gs46
title: Northeast Region Household Data. NER-Stat: Baseline Survey 2024

description: &lt;p&gt;The NER-Stat: Baseline Survey 2024 is a collaborative initiative developed by the North Central Regional Center for Rural Development, the Southern Rural Development Center, and Auburn University. The survey explores essential topics related to household, business, and community well-being. It aims to learn more about the conditions, issues, and challenges faced by rural and urban communities and their residents in the Northeast Region.&lt;/p&gt;

&lt;p&gt;Conducted as a 20-minute online survey through the Qualtrics platform, it covers a broad spectrum of topics, including household demographics, income, workforce participation, entrepreneurship, caregiving, housing, broadband access, migration and staying behavior, civic engagement, community belonging, health, food security, well-being, and environmental concerns. The survey was designed to maximize participation across a diverse range of household types, geographic locations, genders, ages, and racial and ethnic groups, ensuring comprehensive and balanced representation.&lt;/p&gt;

&lt;p data-end="2180" data-start="1232"&gt;Building on the 2022 survey, the NER-Stat: Baseline Survey 2024 features both adapted and expanded questions, introducing new topics like climate change perceptions and the recreation economy. Data collection took place from August to November 2024 through the Qualtrics platform. High data quality was maintained through attention-check questions and mandatory responses, with all collected data fully coded and de-identified to safeguard participant confidentiality.&lt;/p&gt;
description: The survey covers essential topics related to household, business, and community well-being. It aims to learn more about the conditions, issues, and challenges faced by rural and urban communities and their residents in the Northeast Region.
DOI: 10.5281/zenodo.17969957
title: Data and Code: Nest defense within and among Neotropical birds

description: These data and code support the paper "Nest defense within and among Neotropical birds", currently in review.
DOI: 10.4231/an8s-1819
title: Southern Region Household Data. SR-Stat: Baseline Survey 2024

description: &lt;p&gt;The SR-Stat: Baseline Survey 2024 is a collaborative initiative developed by the North Central Regional Center for Rural Development, the Southern Rural Development Center, and Auburn University. The survey explores essential topics related to household, business, and community well-being. It aims to learn more about the conditions, issues, and challenges faced by rural and urban communities and their residents in the Southern Region.&lt;/p&gt;

&lt;p&gt;Conducted as a 20-minute online survey through the Qualtrics platform, it covers a broad spectrum of topics, including household demographics, income, workforce participation, entrepreneurship, caregiving, housing, broadband access, migration and staying behavior, civic engagement, community belonging, health, food security, well-being, and environmental concerns. The survey was designed to maximize participation across a diverse range of household types, geographic locations, genders, ages, and racial and ethnic groups, ensuring comprehensive and balanced representation.&lt;/p&gt;

&lt;p data-end="2180" data-start="1232"&gt;Building on the 2022 survey, the SR-Stat: Baseline Survey 2024 features both adapted and expanded questions, introducing new topics like climate change perceptions and the recreation economy. Data collection took place from August to November 2024 through the Qualtrics platform. High data quality was maintained through attention-check questions and mandatory responses, with all collected data fully coded and de-identified to safeguard participant confidentiality.&lt;/p&gt;
description: The dataset covers essential topics related to household, business, and community well-being. It aims to learn more about the conditions, issues, and challenges faced by rural and urban communities and their residents in the Southern Region.
DOI: 10.5067/aqua/modis/l4m/carbon/2022.0
title: Aqua MODIS Level-4 Global Mapped Carbon Data, version 2022.0
DOI: 10.4231/gv7j-6z90
title: The structural compactness of a tropical cyclone seed affects its persistence

description: &lt;p&gt;Tropical cyclones (TCs) are often generated from pre-existing &#39;seed&#39; vortices. A seed with a higher persistence might have a higher chance to undergo TC genesis, albeit understanding the factors influencing seed persistence remains a challenge. This study proposes that the role of planetary Rossby wave drag is a key dynamical factor affecting seed vortex persistence. A novel parameter, termed &quot;vortex structural compactness&quot; (Cv), is introduced to characterize the weakening of the vortex&#39;s maximum wind speed (Vmax) by planetary Rossby wave drag, represented as the ratio of Vmax to the Rhines speed at the radius of maximum wind (Rmax). The relationship between Cv and the vortex weakening rate is investigated through barotropic &Icirc;&sup2;-plane experiments. The initial Cv of vortices is varied systematically by altering their initial Vmax and Rmax in idealized wind profile models. Real-world developing TC seed data are sampled from reanalysis datasets, providing diverse Cv values for each seed. The barotropic experiments reveal a strong dependence of vortex early-stage weakening rate on the initial Cv, particularly for initially compact (Cv&gt;1) members. A comprehensive TC seed dataset is compiled using TempestExtremes to identify qualified tropical disturbances. The correlation between TC seeds&#39; lifetime average Cv and their persistence is examined, and large-scale low-level vorticity budget analysis is employed to investigate the relationship between TC seeds&#39; Cv and maximum vorticity tendency.&lt;/p&gt;
description: MATLAB scripts and data for figures in the paper “The structural compactness of a tropical cyclone seed affects its persistence.”
DOI: 10.4231/dnbt-5g17
title: Understanding disease-associated metabolic changes in human colon epithelial cells using iColonEpithelium metabolic reconstruction

description: &lt;p&gt;Background: The human colon epithelium is crucial for metabolic interactions with gut microbiota, which mediates influence of dietary inputs on host metabolism. Colonic epithelial cells have distinct metabolic properties essential for maintaining homeostasis, whose disturbances are associated with inflammatory bowel disease (IBD). However, metabolism of colon epithelium and its interactions with the gut microbiota at the cellular level remains poorly understood.&lt;/p&gt;

&lt;p&gt;Method: We analyzed transcriptome data of 188 healthy human colon epithelium samples from the GEO database to build the draft reconstruction of colonic epithelial cells based on the generic human metabolic reconstruction, Recon3D. It was further refined with metabolic reactions and genes specific to colon epithelium and its specificity was tested with metabolic tasks essential to colonic epithelial cells. We used colon carcinoma (Caco-2) cell line for validating findings related to short chain fatty acids and amino acids.&lt;/p&gt;

&lt;p&gt;Results: The iColonEpithelium reconstruction consisted of 6551 reactions, 1820 metabolites and 1961 genes. It can achieve essential colonic epithelial cell metabolic functions, like beta-oxidation of butyrate and acetate and tryptophan metabolism. We used it to predict metabolic changes in two case studies: ulcerative colitis (UC) and Crohn&rsquo;s diseases (CD). &lt;/p&gt;

&lt;p&gt;Conclusion and application: We present the first manually curated metabolic reconstruction of human colonic epithelial cells, namely iColonEpithelium, which captures the metabolic signatures of the cell. Ongoing integration of iColonEpithelium with gut microbiome models and multi-omics data will provide deeper insights into the role of colonic epithelial cells in the host-microbiome interactions. It will contribute to predicting dietary effects on host-microbiome metabolism and the metabolic alterations associated with various gut-related disorders.&lt;/p&gt;
description: Codes for the construction of iColomEpithelium and analysis of case studies
DOI: 10.4231/mjk3-f255
title: Datasets for analyzing basin contribution to Apollo samples

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 basin-forming period (&gt;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. They will not be exactly the same due to the way random number seeding is handled.&lt;/p&gt;

&lt;p&gt;These datasets are expected to work with the Python scripts provided here.&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 basin-forming period, as well as inputs to produce similar data using the model.
DOI: 10.5067/aqua/modis/l4m/gsm/2022.0
title: Aqua MODIS Level-4 Global Mapped Garver-Siegel-Maritorena Model (GSM) Data, version 2022.0
DOI: 10.4231/qqt9-gh24
title: Genome-wide, Organ-delimited gene regulatory networks (OD-GRN) provide high accuracy in candidate Transcription Factor (TF) selection across diverse processes

description: &lt;p&gt;Organ-specific gene expression datasets that include hundreds to thousands of experiments allow reconstruction of organ-level gene regulatory networks. However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower and seed in &lt;em&gt;Arabidopsis thaliana&lt;/em&gt;. A gene regulatory network (GRN) inference approach was then used to determine: &lt;em&gt;i.&lt;/em&gt; influential transcription factors (TFs) in each organ and, &lt;em&gt;ii.&lt;/em&gt; the most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, with relevance to food and biofuel production. Of the top twenty candidate TFs, eight are known regulators of seed oil content, including WRI1, LEC1, and FUS3. Importantly, we validated five previously unknown TFs MybS2, TGA4, SPL12, AGL18 and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MyB2 and show that it induces PAP family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to discover novel regulators of any trait-of-interest. &lt;/p&gt;

&lt;p&gt;A web tool for querying and visualizing the OD-GRNs is available here: https://www.purdue.edu/hla/sites/varalalab/od-grns/ . The underlying gene expression datasets shared here can be used to create custom subsets, or as input for alternate GRN inference or other ML applications. &lt;/p&gt;
description: Organ-specific gene expression datasets that include hundreds to thousands of experiments allow reconstruction of gene regulatory networks and discovery of transcriptional regulators various pathways and processes.
DOI: 10.4231/bbs3-3542
title: Col I/II and HA scaffolds for osteoarthritic applications data sets

description: &lt;p&gt;Scaffolds were evaluated for chondrogenic and chondroprotective responses. The following properties were characterized: cartilage matrix formation and degradation; cartilage, inflammatory, and degradative enzyme gene expression; cytokine and enzyme production. &lt;/p&gt;
description: In this work, blended collagen I, collagen II, and hyaluronic acid hydrogels with encapsulated mesenchymal stromal cells were evaluated for cartilage repair applications within an inflammatory, osteoarthritic environment.
DOI: 10.4231/aknb-0623
title: A Cyber-physical Testbed for Smart and Resilient Space Habitats - Companion Data

description: &lt;p&gt;&lt;span sans="" segoe="" style="color: rgb(66, 66, 66); font-family: "&gt;The datasets in this repository support the research conducted on the Human-centered Autonomous Resilient Space Habitat (HARSH), a cyber-physical testbed developed by the Resilient Extra-Terrestrial Habitats Institute (RETHi). HARSH enables researchers to test technologies for resilient space habitats by simulating various conditions and disruptions. The data package includes three distinct datasets: one representing the nominal case, another detailing a single damage scenario involving a structural protective layer fault, and the third capturing a cascading damage scenario with faults in both the structural protective layer and the thermal management system. These datasets are used in the paper to examine the dynamics of complex systems, aimed towards research in digital twin design, fault detection, decision-making, and autonomous operations, ultimately contributing to the development of sustainable and resilient space habitats.&lt;/span&gt;&lt;/p&gt;
description: The datasets in this repository support the paper on HARSH, a cyber-physical testbed for SmartHabs research. HARSH enables testing of lunar, Martian, and other mission scenarios through physical and virtual systems, focusing on resilience, fault detection
DOI: 10.4231/ja2n-dj86
title: MRM profiiling dataset - Milk and mammary gland Scd1 global knockout (GKO) and WT C57BL/6 mice

description: &lt;table id="infotbl"&gt;
 &lt;tbody&gt;
 &lt;tr&gt;
 &lt;td&gt;
 &lt;p&gt;This dataset was acuquired to study the effect of &lt;em&gt;Scd1&lt;/em&gt; deficiency on the mammary gland and milk lipid profile during lactation, a period characterized by intense mammary lipogenic activity, and its subsequent effect on neonatal weight.&lt;/p&gt;

 &lt;p&gt;In this study, we utilized wild-type (WT; n=7) and &lt;em&gt;Scd1&lt;/em&gt;-/- (&lt;em&gt;Scd1&lt;/em&gt; global knockout (GKO)) (n=6) mice and bred them twice. Thus, all studies were conducted on second parity dams, so all growth data for neonates, milk, and mammary are from second parity dams. Mice were initially paired at ~7 weeks of age, permitting a complete reproductive cycle and allowing dams to nurse their pups. After weaning, mice were re-paired to generate a second litter. To ensure that all pups were of the same genotype, GKO females were mated with WT males, while WT females were bred with GKO males, resulting in heterozygous offspring. Pregnancy was closely monitored, and litters were standardized to 5 pups per dam on postnatal day 2, with surplus pups cross-fostered to other mice within the colony. All mice nursed their 5 pups until day 10 of lactation, except one GKO female, which had three pups whose weight was within the range of pups from other GKO dams.&lt;/p&gt;
 &lt;/td&gt;
 &lt;/tr&gt;
 &lt;/tbody&gt;
&lt;/table&gt;
description: The dataset comprises mass spectrometry raw files (.d), a table with the experimental group and sample type related to each name file, and the list of ion transitions screened for in the samples.
DOI: 10.4231/yp65-j966
title: Altered yellow perch stock-recruit relationships in Saginaw Bay, Lake Huron - Data

description: &lt;p&gt;In Saginaw Bay, a large productive embayment of Lake Huron, yellow perch (&lt;em&gt;Perca flavescens&lt;/em&gt;) experienced a multi-decadal decline in adult abundance despite increasing reproductive success (age-0 relative abundance in fall), suggesting a shift in recruitment dynamics. To assess the relative influence of ecosystem changes in altering yellow perch stock-recruit relationships, we compared candidate models to evaluate potential shifts in the age at which year-class strength (YCS) is set and the importance of environmental and biotic factors in explaining yellow perch reproduction and recruitment success (1970-2022), assuming either stationary or nonstationary dynamics. Results indicate that the age when YCS is set shifted from age-0 to age-1 in the early-2000s. Incorporating nonstationary breaks in models best explained annual variation in yellow perch reproductive (~79% variation explained) and recruitment success (~48%). Increased reproductive success was attributed to reduced competition and predation following the 2003-2004 collapse of alewife (&lt;em&gt;Alosa pseudoharengus&lt;/em&gt;). However, increases in walleye (&lt;em&gt;Sander vitreus&lt;/em&gt;) abundance (since late-1980s), coinciding with alewife declines, may have intensified predation pressure on age-1 yellow perch, leading to declines in annual recruitment success. &lt;/p&gt;
description: Yellow perch abundance has continued to decline in Saginaw Bay despite strong annual reproductive success. Stock-recruitment modeling revealed a shift in the age at which year-class strength is set, coinciding with alewife collapse and walleye expansion.
DOI: 10.4231/0e48-t344
title: FIA tree species code changes through time

description: &lt;p&gt;This repository contains data and R code that evaluates changes in species code (SPCD) within USDA Forest Service Forest Inventory and Analysis (FIA) Program tree remeasurements. The FIA Program conducts the national forest inventory of the United States, with a spatially representative sample of over 140,000 forested plots across the conterminous U.S. (CONUS), Alaska, Hawaii, and the U.S. Territories and Affiliated Islands. In this project, we linked remeasurements of individual trees across CONUS FIA plots to determine how often tree species identification (SPCD) changed, how rates of SPCD changes varied across the country, and how decisions to rectify these differences can lead to differences in estimates of species range centroids, plot-level species richness, and both tree-level and population-level estimates of aboveground carbon. &lt;/p&gt;

&lt;p&gt;The data file included in this repository contains a table in CSV format that includes &gt;3 million unique trees that have been remeasured in FIA plots. The table includes columns of tree control number (CN) for the most recent measurement of live trees and their previous CNs through past measurements. These CNs can be used to link to additional tree-level data from the FIA database. This table also includes columns of tree SPCD at each remeasurement, columns flagging whether the SPCD changed between remeasurements, and what each tree&#39;s first and current recorded SPCDs were.&lt;/p&gt;

&lt;p&gt;R code included in this repository shows how this data file was created. It includes instructions for downloading the SQLite FIA database from the FIA DataMart (https://apps.fs.usda.gov/fia/datamart/datamart.html), how to use the &#39;odbc&#39; and &#39;RSQLite&#39; packages to perform SQL queries on the database, and steps for constructing the trees-through-time data table using the &#39;dplyr&#39; and &#39;data.table&#39; packages. &lt;/p&gt;
description: This repository contains data and R code that evaluates changes in species code (SPCD) within USDA Forest Service Forest Inventory and Analysis (FIA) Program tree remeasurements.