cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34945/f5mg68
title: ASIA Impairment Scale, level of injury, intraoperative time series mean arterial pressure and heart rate after spinal cord injury in patients in a multi-site retrospective TRACK-SCI cohort: site 2 of 2

description: STUDY PURPOSE: The main goal for the collection of this data was to assess whether intraoperative hemodynamics (arterial pressure and heart rate) is associated to patient neurological improvement at hospital discharge in spinal cord injury (SCI). The data were collected in two level I trauma centers in Northern California (Santa Clara Valley Medical Center and Zuckerberg San Francisco General Hospital). This dataset represents the data from one of the centers, while the data collected in the other center is available elsewhere (see provenance). Both datasets have been harmonized, meaning variables and data dictionary align. DATA COLLECTED: The dataset includes mean arterial pressure (MAP) and heart rate (HR) at 5 minute intervals during SCI associated surgery for n = 87 patients, as well as summaries across surgery time (average, standard deviation, kurtosis and skewness). The data also include demographics (age and gender), length metrics (surgery duration and days from injury to discharge), and injury characteristics and outcome metrics (AIS grade at admission, AIS grade at hospital discharge, dichotomized neurological level of injury: cervical vs. non-cervical). DATA USAGE NOTES:
DOI: 10.6073/pasta/eec055c0416a916b37c5d8079f32acc7
title: End of Year Biomass in Marshes of the Virginia Coast Reserve 1999-2017

description: Background: Brinson et al. (1995) developed a model representing the change that occurs in ecosystem state (or habitat type) along the shorezone, from the forest -&gt; high marsh -&gt; low marsh -&gt; mud flat, in response to the increased inundation caused by rising sea-level. They suggested that a seaward shift in ecosystem state is largely dependent on local slope and sediment supply. The states are associated with the dominant vegetation found within each. The most seaward (lowest in elevation) state is the mud flat. It is frequently inundated by tide and typically supports algal species. The next landward state is the mineral low marsh; it is dominated by Spartina alterniflora and is typically flooded at high tide. Sediments here may be largely mineral in origin. The next landward state is the high marsh; it may be dominated by S. patens, Distichlis spicata, and Juncus roemerianus. It is occasionally inundated by high tides and the soil is usually organic. The transition zone between the high marsh and the forest is typically dominated by Iva frutescens, Baccharis hamifolia, and Juniperus virginiana. It is only inundated during severe storm surges. The forest may be dominated by either pines or hardwoods and is again flooded with sea water only by storm surges. Goals: The goal of this long-term project is to evaluate how sea-level rise affects marsh evolution and ecosystem state change. Seventeen sites along Virginia's eastern shore have been selected to study marsh evolution on both the mainland and the barrier islands. These sites will be available for long-term seasonal to annual observations. Some sites will also be available for experimentation and short-term studies. The initial project is to establish initial site characteristics. Note, in 2017 a freezer failure caused decomposition of all unprocessed samples. For that reason, most of the data from that year does not distinguish live from dead and many of the species are unidentified. Affected samples are identified by a comment noting the freezer failure.
DOI: 10.6073/pasta/1d560fbd372dec7da8da848e214675ef
title: NOAA-NOS-NGS t-sheet Vector Shorelines for the Eastern Shore of VA and southern MD, 1847-1978

description: The primary purpose of this dataset is to provide VCRLTER researchers and students with a convenient and comprehensive set of historical NOS t-sheet shorelines spanning the full Virginia Eastern Shore in a single GIS data layer.Â From NOAA-NOS-NGS source metadata: "These shoreline data represent a vector conversion of a set of NOS raster shoreline manuscripts identified by t-sheet or tp-sheet numbers. These vector data were created by contractors for NOS who vectorized georeferenced raster shoreline manuscripts using Environmental Systems Research Institute, Inc. (ESRI)(r), ArcInfo's(r) ArcScan(r) software to create individual ArcInfo coverages. The individual coverages were ultimately edgematched within a surveyed project area and appended together. The NOAA NESDIS Environmental Data Rescue Program (EDRP) funded this project. The NOAA National Ocean Service, Coastal Services Center, developed the procedures used in this project and was responsible for project oversight. The project intent was to rescue valuable historical data and make it accessible and useful to the coastal mapping community. This process involved the conversion of original analog products to digital mapping products. This file is a further conversion of that product from a raster to a vector product that may be useful for Electronic Charting and Display Information Systems (ECDIS) and geographic information systems (GIS)." Original NOAA-NOS-NGS data were organized by project, with each project containing a single shapefile containing the historical shoreline features from multiple T-sheets based on surveys from roughly the same time period.Â There were 43 projects containing information from 208 T-sheets and TP-sheets that were found to cover the Eastern Shore of VA and southern MD and ranging in time from 1847 to 1978 (plus one set of shorelines from 2009 for the new Chincoteague bridge and the immediate surrounding area).Â VCRLTER staff combined these 43 shapefiles into a single shapefile with an added "PROJID" attribute to identify the source project. This shoreline dataset compliments and overlaps other VCRLTER shoreline datasets for the Virginia barrier islands that contain historical shorelines derived from a combination of sources, including: a subset of the included NOS t-sheets (digitized by VCRLTER researchers prior to availability in digital format from NOAA-NOS-NGS); NOAA coastal change maps; photointerpretation of aerial photos (from USGS, USACE, VITA-VGIN-VBMP, and others), and satellite imagery (from ETM+ Landsat 7 and IKONOS); and GPS surveys.
DOI: 10.5281/zenodo.5708832
title: Characterizing disease-associated human proteins without available protein structures or homologues

description: AlphaFold and RoseTTAFold models of domains of disease associated human proteins without structures/known homologues. Tables containing the model quality, model region, sequence alignment statistics, matched FunFam, associated GO terms for the FunFam if mutation is near a predicted functional site (conserved residue/ligand binding site/protein-protein interface)
DOI: 10.6084/m9.figshare.17035931
title: example

description: This is an example data set for the Benisse(<b>B</b>CR <b>e</b>mbedding graphical <b>n</b>etwork i<b>nf</b>ormed by <b>s</b>cRNA-<b>s</b>eq). Place all files under /Benisse/example.
DOI: 10.6084/m9.figshare.17035931.v1
title: example

description: This is an example data set for the Benisse(<b>B</b>CR <b>e</b>mbedding graphical <b>n</b>etwork i<b>nf</b>ormed by <b>s</b>cRNA-<b>s</b>eq). Place all files under /Benisse/example.
DOI: 10.6084/m9.figshare.16699343
title: Stratified Stochastic Variational Inference for High-Dimensional Network Factor Model

description: There has been considerable recent interest in Bayesian modeling of high-dimensional networks via latent space approaches. When the number of nodes increases, estimation based on Markov chain Monte Carlo can be extremely slow and show poor mixing, thereby motivating research on alternative algorithms that scale well in high-dimensional settings. In this article, we focus on the latent factor model, a widely used approach for latent space modeling of network data. We develop scalable algorithms to conduct approximate Bayesian inference via stochastic optimization. Leveraging sparse representations of network data, the proposed algorithms show massive computational and storage benefits, and allow to conduct inference in settings with thousands of nodes. An R package with an efficient c++ implementation of the proposed algorithms is provided.
DOI: 10.6084/m9.figshare.16699343.v2
title: Stratified Stochastic Variational Inference for High-Dimensional Network Factor Model

description: There has been considerable recent interest in Bayesian modeling of high-dimensional networks via latent space approaches. When the number of nodes increases, estimation based on Markov chain Monte Carlo can be extremely slow and show poor mixing, thereby motivating research on alternative algorithms that scale well in high-dimensional settings. In this article, we focus on the latent factor model, a widely used approach for latent space modeling of network data. We develop scalable algorithms to conduct approximate Bayesian inference via stochastic optimization. Leveraging sparse representations of network data, the proposed algorithms show massive computational and storage benefits, and allow to conduct inference in settings with thousands of nodes. An R package with an efficient c++ implementation of the proposed algorithms is provided.
DOI: 10.6084/m9.figshare.16699343.v1
title: Stratified stochastic variational inference for high-dimensional network factor model

description: There has been considerable recent interest in Bayesian modeling of high-dimensional networks via latent space approaches. When the number of nodes increases, estimation based on Markov Chain Monte Carlo can be extremely slow and show poor mixing, thereby motivating research on alternative algorithms that scale well in high-dimensional settings. In this article, we focus on the latent factor model, a widely used approach for latent space modeling of network data. We develop scalable algorithms to conduct approximate Bayesian inference via stochastic optimization. Leveraging sparse representations of network data, the proposed algorithms show massive computational and storage benefits, and allow to conduct inference in settings with thousands of nodes. An r package with an efficient c++ implementation of the proposed algorithms is provided.
DOI: 10.6084/m9.figshare.17035951
title: Interval Censored Recursive Forests

description: We propose interval censored recursive forests (ICRF), an iterative tree ensemble method for interval censored survival data. This nonparametric regression estimator addresses the splitting bias problem of existing tree-based methods and iteratively updates survival estimates in a self-consistent manner. Consistent splitting rules are developed for interval censored data, convergence is monitored using out-of-bag samples, and kernel-smoothing is applied. The ICRF is uniformly consistent and displays high prediction accuracy in both simulations and applications to avalanche and national mortality data. An R package icrf is available on CRAN. Supplementary files for this article are available online.
DOI: 10.6084/m9.figshare.17035951.v1
title: Interval Censored Recursive Forests

description: We propose interval censored recursive forests (ICRF), an iterative tree ensemble method for interval censored survival data. This nonparametric regression estimator addresses the splitting bias problem of existing tree-based methods and iteratively updates survival estimates in a self-consistent manner. Consistent splitting rules are developed for interval censored data, convergence is monitored using out-of-bag samples, and kernel-smoothing is applied. The ICRF is uniformly consistent and displays high prediction accuracy in both simulations and applications to avalanche and national mortality data. An R package icrf is available on CRAN. Supplementary files for this article are available online.
DOI: 10.3886/e150081v1-101900
title: Vaccine hunters and jostlers

description: Desperate times call for desperate measures. But it remains unknown how private citizens’ extreme behavior to self-protect interacts with public health efforts. We conducted a survey experiment shortly after a limited supply of COVID-19 vaccines were made available to prioritized groups, and examined how salience of extreme actions to gain access to vaccines affect general vaccine preferences. Learning about people who jump the line (jostlers) or people who go through great lengths to secure left-over vaccine doses (hunters) is off-putting and may have a meaningful, negative effect on people’s vaccine preferences. Most people, however, predict the opposite -- that news about extreme behavior would help the vaccination effort. If public health authorities share such beliefs, they run the risk of implementing information policies that backfire.<br><br><br><br><br>
DOI: 10.5066/p9qxke7y
title: Maui Island Modeling Scenarios for the Management of Axis Deer 1959-2014

description: Our objectives were to examine the population history of axis deer on Maui, estimate observed population growth, and then use species-specific demographic parameters in a VORTEX population viability analysis to examine removal scenarios that would most effectively reduce the population. Only nine deer were introduced in 1959, but recent estimates of less than 10,000 deer suggest population growth rates (r) ranging between 0.147 and 0.160 although at least 11,200 have been removed by hunters and resource managers. In the VORTEX simulations, we evaluated an initial population size of 6,000 females and 4,000 males, reflecting the probable 3F:2M sex ratio on Maui because of male biased hunting. Scenarios were modeled over a 10-year period with removal rates of 10%, 20%, and 30% of each annual population estimate, considering both growth and removals. A removal rate of 10% the annual population estimate (1,000 deer in the first year), and an evenly distributed effort that would remove an approximate ratio of 3F:2M resulted in a positive growth rate of 0.103 ± 0.001. A 20% removal rate resulted in only a slight negative growth, while a 30% removal rate dropped the estimate to 2,759 ± 15 deer in 10 years. By increasing the ratio of females removed to 4F:1M in the 30% removals scenario, the rate of decline nearly doubled and resulted in a mean population of 1,086 ± 15 deer. Our results indicate that effectively reducing an axis deer population would require an annual removal of approximately 20–30% of the estimated population and maintaining a ratio of 4F:1M would result in the steepest population decline.
DOI: 10.6073/pasta/5d7de48756b61397d58da39c8f277f69
title: Monitoring O. mykiss Life Stages on the Stanislaus River

description: This study is designed to partly address steelhead conservation measures outlined in the U.S. Bureau of Reclamation's Proposed Action for the Long-Term Operation of the CVP. This effort is funded under the Central Valley Project Improvement Act (CVPIA) authority Provision 3046 (g)(4), which states the primary purpose of this effort shall be to support the Secretary's efforts in fulfilling the requirements of this title through improved scientific understanding concerning, but not limited to, measures needed to restore anadromous fisheries to optimum and sustainable levels in accordance with the restored carrying capacities of Central Valley rivers, streams, and riparian habitats. Conceptual and quantitative work described in this report encompass multiple O. mykiss life-stages and transitions among life stages. The geographic scope of the report extends from Goodwin Dam to the confluence of the Stanislaus and San Joaquin Rivers. The goal of this study is to develop a framework for monitoring O. mykiss life stages, and transitions among life stages, to quantify how water project operations and environmental variation influence life history expression, abundance, and population productivity. Here we provide results from activities during the base year of the Project. Task 1 Steelhead Monitoring Plan: Define methods and objectives for long-term O. mykiss monitoring in the Stanislaus River drainage by developing a Steelhead Monitoring Plan. Task 2 Enumerate the O. mykiss population size in the Stanislaus River. Task 3 Age and Growth Study: Estimate key population demographics (e.g., age structure, sex ratio). Task 4 Monitoring Instream and Outmigration Survival, and Outmigration Rate: Quantify the survival and transition probabilities of key life stages (e.g., instream rearing, outmigration survival). This completed dataset for the 2021 portion of the Project includes several components: (1) Electrofishing and seining: In 2021 electrofishing, seining, hook and line sampling and PIT tagging of captured fish occurred opportunistically by CFS staff as well as by volunteer anglers. (2) PIT tagging: PIT tag detection records are included in the dataset after being filtered for false detections. (3) Species Counts and Physical Attributes: The species and the number caught are included in the dataset. For some O. mykiss individuals caught, the weight and fork length measurements are included. Whether or not a genetic sample (either fin clip or scale) was taken is indicated as well. (4) Supplementary Data: Data reporting field staff, weather, water temperature, electrofishing settings, and collection method used are included for greater context. Data is recorded in a combination of paper to computer files and electronic only files. Annual reports summarize the survey findings.
DOI: 10.5517/ccdc.csd.cc28b8jf
title: CCDC 2095244: Experimental Crystal Structure Determination

description: Related Article: Richard Y. Kong, Maria Batuecas, Mark R. Crimmin|2021|Chemical Science|12|14845|doi:10.1039/D1SC04940B
DOI: 10.5517/ccdc.csd.cc28b8kg
title: CCDC 2095245: Experimental Crystal Structure Determination

description: Related Article: Richard Y. Kong, Maria Batuecas, Mark R. Crimmin|2021|Chemical Science|12|14845|doi:10.1039/D1SC04940B
DOI: 10.5517/ccdc.csd.cc28b8lh
title: CCDC 2095246: Experimental Crystal Structure Determination

description: Related Article: Richard Y. Kong, Maria Batuecas, Mark R. Crimmin|2021|Chemical Science|12|14845|doi:10.1039/D1SC04940B
DOI: 10.5517/ccdc.csd.cc28b8mj
title: CCDC 2095247: Experimental Crystal Structure Determination

description: Related Article: Richard Y. Kong, Maria Batuecas, Mark R. Crimmin|2021|Chemical Science|12|14845|doi:10.1039/D1SC04940B
DOI: 10.5517/ccdc.csd.cc28b8nk
title: CCDC 2095248: Experimental Crystal Structure Determination

description: Related Article: Richard Y. Kong, Maria Batuecas, Mark R. Crimmin|2021|Chemical Science|12|14845|doi:10.1039/D1SC04940B
DOI: 10.5517/ccdc.csd.cc28b8pl
title: CCDC 2095249: Experimental Crystal Structure Determination

description: Related Article: Richard Y. Kong, Maria Batuecas, Mark R. Crimmin|2021|Chemical Science|12|14845|doi:10.1039/D1SC04940B
DOI: 10.5517/ccdc.csd.cc28b8qm
title: CCDC 2095250: Experimental Crystal Structure Determination

description: Related Article: Richard Y. Kong, Maria Batuecas, Mark R. Crimmin|2021|Chemical Science|12|14845|doi:10.1039/D1SC04940B
DOI: 10.5517/ccdc.csd.cc28b8rn
title: CCDC 2095251: Experimental Crystal Structure Determination

description: Related Article: Richard Y. Kong, Maria Batuecas, Mark R. Crimmin|2021|Chemical Science|12|14845|doi:10.1039/D1SC04940B
DOI: 10.5517/ccdc.csd.cc28b8sp
title: CCDC 2095252: Experimental Crystal Structure Determination

description: Related Article: Richard Y. Kong, Maria Batuecas, Mark R. Crimmin|2021|Chemical Science|12|14845|doi:10.1039/D1SC04940B
DOI: 10.5517/ccdc.csd.cc28b8tq
title: CCDC 2095253: Experimental Crystal Structure Determination

description: Related Article: Richard Y. Kong, Maria Batuecas, Mark R. Crimmin|2021|Chemical Science|12|14845|doi:10.1039/D1SC04940B
DOI: 10.5517/ccdc.csd.cc28b8vr
title: CCDC 2095254: Experimental Crystal Structure Determination

description: Related Article: Richard Y. Kong, Maria Batuecas, Mark R. Crimmin|2021|Chemical Science|12|14845|doi:10.1039/D1SC04940B
DOI: 10.5517/ccdc.csd.cc28c0g4
title: CCDC 2095955: Experimental Crystal Structure Determination

description: Related Article: Dragana Stevanović, Jovana Bugarinović, Marko Pešić, Anka Todosijević, Goran A. Bogdanović, Ivan Damljanović|2021|RSC Advances|11|36208|doi:10.1039/D1RA07619A
DOI: 10.5517/ccdc.csd.cc28cqr4
title: CCDC 2096646: Experimental Crystal Structure Determination

description: Related Article: Michael Wiesinger, Christian Knüpfer, Holger Elsen, Jonathan Mai, Jens Langer, Sjoerd Harder|2021|ChemCatChem|13|4567|doi:10.1002/cctc.202101071
DOI: 10.5517/ccdc.csd.cc28csbs
title: CCDC 2096695: Experimental Crystal Structure Determination

description: Related Article: Michael Wiesinger, Christian Knüpfer, Holger Elsen, Jonathan Mai, Jens Langer, Sjoerd Harder|2021|ChemCatChem|13|4567|doi:10.1002/cctc.202101071
DOI: 10.5517/ccdc.csd.cc28csct
title: CCDC 2096696: Experimental Crystal Structure Determination

description: Related Article: Michael Wiesinger, Christian Knüpfer, Holger Elsen, Jonathan Mai, Jens Langer, Sjoerd Harder|2021|ChemCatChem|13|4567|doi:10.1002/cctc.202101071
DOI: 10.6084/m9.figshare.14195141
title: Robin tree-nesting data

description: We found and monitored American Robin (Turdus migratorius) tree nests in 2019 at a commercial tree farm in Champaign County, Illinois, U.S.A. <br><br>We located nests visually then returned to check nest contents (minimum every 2 days). We recorded whether the nest had eggs and chicks (Y/N/unknown) and whether the chicks successfully fledged (Y/N/unknown). We recorded the date the nest was first found, last checked while still active, and last checked overall. We coded nest fate as 0=successful and 1=unsuccessful to run calculations of daily survival rate in R program RMark. Finally, we calculated initiation date based on the known laying period (1 egg per day for clutch of 3-4), known incubation period (11 days) and known nestling period (~12 days) for the population. <br><br>All nest monitoring was approved by the University of Illinois at Urbana-Champaign's Institutional Animal Care and Use Committee.
DOI: 10.6084/m9.figshare.17019836
title: Dataset of automatically extracted sizes and morphologies of AuNPs from literature-mined SEM/TEM images

description: This is a dataset of automatically extracted sizes and morphologies of Gold nanoparticles, which have been obtained from the analysis of 4365 literature-mined SEM/TEM images. The dataset contains 4365 records, each of which contains extracted size/morphology information and metadata corresponding to a single microscopy image.
DOI: 10.6084/m9.figshare.14195123
title: Robin ground nest data

description: We found and monitored American Robin (Turdus migratorius) ground nests from 2018-2020 at a commercial tree farm in Champaign County, Illinois, U.S.A. <br><br>We located nests visually then returned to check nest contents (minimum every 2 days). We recorded whether the nest had eggs and chicks (Y/N/unknown) and whether the chicks successfully fledged (Y/N/unknown). We recorded the date the nest was first found, last checked while still active, and last checked overall. We coded nest fate as 0=successful and 1=unsuccessful to run calculations of daily survival rate in R program RMark. Finally, we calculated initiation date based on the known laying period (1 egg per day for clutch of 3-4), known incubation period (11 days) and known nestling period (~12 days) for the population. <br><br>All nest monitoring was approved by the University of Illinois at Urbana-Champaign's Institutional Animal Care and Use Committee.
DOI: 10.15151/esrf-es-625913735
title: SAARLAND BAG: ATOMIC MECHANISMS OF ACTION OF MEMBRANE PROTEINS AND BIOSYNTHETIC ENZYMES

description: Projects are focussed primarily within the framework of the DFG-funded excellence network program on "Ca2+signals: Molecular Mechanisms und Integrative Functions" at Saarland University and a new joint multidisciplinary initiative of the University with the Helmholtz Institute of Pharmaceutical Research Saarland on natural bioactive compounds including their interactions with their target proteins. These projects involve the structure determination of membrane proteins and soluble proteins.