cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e155002
title: Investigating Why Academically Successful Community College Students Leave College without a Degree

description: These files contain instructions for accessing data and scripts to replicate the analyses for "Investigating Why Academically Successful Community College Students Leave College without a Degree"<br><br>The abstract for the paper is found below:<br><br>Even though a postsecondary degree can offer economic, social, and civic benefits, many community college students leave without earning a degree—including some who have performed well academically and made substantial progress toward graduation. To better understand the factors contributing to early exit, we surveyed a number of former students in a large community college system. We improve the generalizability of the survey responses through multilevel regression with poststratification (MRP), which we use to reweight the responses to better represent the population in our original survey frame. We find that tuition and fees, living expenses, and no longer being eligible for financial aid are the factors contributing to early exit for the largest share of students. We also find variation in both financial and non-financial factors across subgroups, suggesting that targeted supports may be useful in helping students persist or return to college and complete their degree.
description: Previously successful community college students who left prior to earning a degree.
description: web-based survey;
DOI: 10.3886/e155002v1
title: Investigating Why Academically Successful Community College Students Leave College without a Degree

description: These files contain instructions for accessing data and scripts to replicate the analyses for "Investigating Why Academically Successful Community College Students Leave College without a Degree"<br><br>The abstract for the paper is found below:<br><br>Even though a postsecondary degree can offer economic, social, and civic benefits, many community college students leave without earning a degree—including some who have performed well academically and made substantial progress toward graduation. To better understand the factors contributing to early exit, we surveyed a number of former students in a large community college system. We improve the generalizability of the survey responses through multilevel regression with poststratification (MRP), which we use to reweight the responses to better represent the population in our original survey frame. We find that tuition and fees, living expenses, and no longer being eligible for financial aid are the factors contributing to early exit for the largest share of students. We also find variation in both financial and non-financial factors across subgroups, suggesting that targeted supports may be useful in helping students persist or return to college and complete their degree.
description: Previously successful community college students who left prior to earning a degree.
description: web-based survey;
DOI: 10.7267/pc289r68h
title: Dataset associated with Long-Term Effects of Fuel Reduction Treatments on Surface Fuel Loading in the Blue Mountains of Oregon

description: Dataset contains a 2015 partial re-measurement of the Blue Mountains Fire and Fire Surrogate study. 8 plots were measured from each of the 16 units, selected
randomly from all plots in each unit. Species are recorded using USDA plant codes, "UNKN" is unknown species. For fine fuels, 3 Brown's transects were collected 
per plot. The length varied based on fuel size class. The "hr1/hr10/hr100" columns include the count of fuel particles within each size class along the given 
transect length. 1K hour fuels were collected along 3 20-m transects at each plot. All trees within a circular 1/10th acre plot were measured.
DOI: 10.34740/kaggle/dsv/2827379
title: US counties COVID 19 dataset
DOI: 10.6073/pasta/e2a696a11bd57eddc25e5eb4657fe59a
title: SBC LTER: REEF: Net primary production, growth and standing crop of Macrocystis pyrifera in Southern California

description: The giant kelp Macrocystis pyrifera forms subtidal forests on shallow reefs in temperate regions of the world. It is one of the fastest-growing multicellular autotrophs on Earth and its high productivity supports diverse marine food webs. In 2008, we published a method for estimating biomass and net primary production (NPP) of giant kelp along with five years of data, to provide a more integrated measure of NPP than those yielded by previous methods. Our method combines monthly field measurements of standing crop and loss rates with a model of kelp biomass dynamics to estimate instantaneous mass-specific growth rates and NPP for each season of each year. We have since improved our approach to account for several previously unresolved sources of biomass loss. These improvements have led to a near doubling of our prior estimates of growth and NPP. At our site with the most persistent stand of giant kelp, NPP averages ~5.2 kg dry mass m-2 y-1 and results from the rapid growth (~3.5% per day) of a relatively small standing biomass (~ 0.4 kg dry mass m-2 on average) that turns over ~ 12 times annually. Here we provide revised estimates of seasonal biomass, growth and NPP for the five years covered by our previous publication (2002-2006), along with an additional decade of data (2007-2017). We also present updated relationships for predicting giant kelp biomass and NPP from much more easily obtained measurements of frond density. These data can be used to understand the mechanisms that drive variation in giant kelp NPP at a wide range of temporal scales.
DOI: 10.6073/pasta/dc0668f21c9c8d73d28727d4c5371fe9
title: SBC LTER: Reef: Kelp Forest Community Dynamics: Cover of sessile organisms, Uniform Point Contact

description: These data describe the cover of sessile invertebrates, understory macroalgae, and bottom substrate types as determined by a uniform point contact method. The presence of over 150 taxa of sessile invertebrates and macroalgae are recorded at 80 uniformly spaced points along permanent 40m x 2m transects. Multiple species can be recorded at any given point. Percent cover of a given species on a transect can be estimated from UPC observations as the fraction of total points at which that species was present x 100. The total percent cover of all species combined using this method can exceed 100%; however, the percent cover of any single species cannot exceed 100%. These data are part of SBC LTERs kelp forest monitoring program, which began in 2000 and was designed to track long-term patterns in species abundance and diversity of reef-associated organisms in the Santa Barbara Channel, California, USA. The sampling locations in this dataset include nine reef sites along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. Data collection began in 2000 and this dataset is updated annually. The time period of data collection varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. See Methods for more information. The two tables in this data package include: 1) The percent cover of sessile invertebrate and understory macroalage; and 2) the percent cover of bottom substrate.
DOI: 10.6073/pasta/9ef0a3d317f6553e1600a0e5af016e43
title: SBC LTER: Reef: Long-term experiment: Kelp removal: Cover of sessile organisms, Uniform Point Contact

description: These data describe the percent cover of sessile invertebrates, understory macroalgae, and bottom substrate at SBC LTER long-term kelp removal experiment sites. Percent cover is determined using a uniform point contact method that consists of noting the identity and relative vertical position of all organisms under 80 uniformly placed points located within a 1 m wide band centered on permanent 40 m transects in each sampling plot. Each species may only be recorded once per point. Using this method, the percent cover of all species combined may exceed 100%, however, the maximum percent cover possible for any single species cannot exceed 100%. The kelp removal experiment was initiated in 2008 at selected reef sites along the mainland coast of the Santa Barbara Channel. The following three giant kelp manipulations are maintained in sampling plots at each site: - Control: Macrocystis pyriferais left intact in a 2000 m2 plot. - Annual removal: Macrocystis pyriferais removed from an adjacent 2000 m2 plot once each winter and allowed to re-colonize over the course of the year. - Continuous removal: Macrocystis pyriferais removed from a 200 m2 area within the 2000 m2 plot on each sampling date. See Methods for more information.
DOI: 10.6073/pasta/19a018572f8e42624151497f7da3706f
title: SBC LTER: Kelp Removal Experiment: Hourly photon irradiance at the surface and seafloor

description: These data represent mean hourly values of photosynthetically active radiation (PAR) (in units of mol m-2 s-1) at the sea surface and the sea floor at SBC LTER kelp removal experiment sites. Sensors record instantaneous irradiance at one-minute intervals, and data are averaged hourly. Sensors are mounted on the sea floor at five sites (Arroyo Quemado, Carpinteria, Naples Reef, Isla Vista, and Mohawk Reef). A surface sensor is deployed on an unobstructed coastal rooftop at the UC Santa Barbara campus; some historical observations are available from sensors mounted above the sea surface at a subset of the five sites.
DOI: 10.6073/pasta/c85303b29d66e7deb3387215a07015be
title: Emerging fungal pathogen of an invasive grass: Implications for competition with native plant species

description: This data package includes data and code from an experiment testing the effects of a leaf spot fungal infection and competition from the invasive (to the U.S.) grass Microstegium vimineum on the performance of three native grass species: Dichanthelium clandestinum, Elymus virginicus, and Eragrostis spectabilis. The experiment was performed between June and September of 2019 in a greenhouse on the University of Florida campus in Gainesville, FL, USA. The leaf spot infection is caused by the fungal pathogen Bipolaris gigantea, which has recently emerged on populations of M. vimineum in the U.S. We tested the hypothesis that infection of B. gigantea would both directly and indirectly affect the native grass species by measuring the change in biomass of each species with and without pathogen inoculation (direct effects) and by measuring the effect of pathogen inoculation on M. vimineum competition through changes in native grass biomass across a density gradient of M. vimneum (indirect effects). The code includes statistical analyses and figures. The code was run using R (version 4.0.1).
DOI: 10.5281/zenodo.5711867
title: 1864 radio variable galaxies with classifications for activity types.

description: 1864 radio variable galaxies with classifications for activity types.
DOI: 10.5281/zenodo.5711866
title: 1864 radio variable galaxies with classifications for activity types.

description: 1864 radio variable galaxies with classifications for activity types.
DOI: 10.6073/pasta/0aa0e5446493f84c893bed0bfdcff4a7
title: Marcell Experimental Forest weekly or biweekly streamwater chemistry at the S1 catchment, 2007 - ongoing

description: This data set is a record of water chemistry for the stream draining the S1 catchment and bog at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota. Stream water samples were usually collected every one or two weeks since 2007 as part of the long-term monitoring program of the S1 catchment. Some samples were collected weekly as part of the Spruce and Peatland Responses Under Changing Environments (SPRUCE) project, with monitoring starting during 2011 prior to initiation of experimental warming in that study (during and after 2014). Samples are measured for pH, specific conductivity, anions (chloride, sulfate), cations (calcium, magnesium, potassium, sodium, aluminum, iron, strontium), silicon, nutrients (ammonium, nitrate, soluble reactive phosphorus, total nitrogen, total phosphorus), and total organic carbon. Occasionally, stable water isotopes and concentrations of ferrous and ferric iron have been measured. More values will be added as additional water samples are collected and analyzed (concentrations and isotopes), or archived water samples are analyzed (mostly for stable water isotopes). The MEF is operated and maintained by the USDA Forest Service, Northern Research Station. The SPRUCE experiment is a multi-year cooperative project among scientists of the Oak Ridge National Laboratory operated by UT-Battelle, LLC and the USDA Forest Service, Northern Research Station, with funding from the US Department of Energy, Biological and Environmental Research Program.
DOI: 10.21227/j49f-xh11
title: Training dataset

description: This dataset is the supplementary material of an IEEE RAL paper named "Design of Fully Controllable and Continuous Programmable Surface Based on Machine Learning". It includes the z-displacement data derived from the FEA simulation, voltage input data derived from Matlab, and dataset for inverse application. The detailed description can be found in that paper.
DOI: 10.25345/c5m85r
title: MassIVE MSV000088411 - Identification of Endogenous Peptides in MALDI-TOF-MS based COVID-19 diagnostics
title: Mass spectrometry (MS) based diagnostic detection of 2019 novel coronavirus infectious disease (COVID19) has been postulated to be a useful alternative to classical PCR based diagnostics. These MS based approaches have the potential to be both rapid, sensitive and can be done onsite without requiring a dedicated laboratory or depending on constrained supply chains (i.e., reagents and consumables). Matrix Assisted Laser Desorption Ionization (MALDI) time of flight (TOF) MS, has a long and established history of microorganism detection. Previously, we have shown that automated machine learning (ML) enhanced MALDI TOF MS diagnostics of nasal swabs can be both sensitive and specific for COVID19 detection. The underlying molecules responsible for this detection are generally unknown nor required for this automated ML platform to detect COVID19. However, the identification of these molecules is important for both understanding the diagnostic test itself and potentially the biology of the underlying infection. Here, we used nanoscale liquid chromatography tandem MS to identify endogenous peptides found in COVID19 positive anterior nares swab saline transport media to characterize mass over charge (m/z) values observed by the MALDI TOF MS method. We identified 14,270 endogenous peptides across 1,245 proteins groups that primarily comprise poly immunoglobulin receptor, actin, statherin, glyceraldehyde3phosphate dehydrogenase, basic salivary prolinerich protein 1 and histones. We also show that SARSCoV2 viral peptides were not readily detected and are highly unlikely to be responsible for the accuracy of MALDI based SARSCoV2 diagnostics.
DOI: 10.5281/zenodo.3678563
title: Simulated NGS read datasets for bacterial pathogenic potential prediction

description: ## Predicting pathogenic potentials from NGS reads: novel bacterial species This repository contains simulated Illumina read datasets for bacterial pathogenic potential prediction and associated metadata extracted from the IMG Database (https://img.jgi.doe.gov/). The reads are 250bp long and were simulated with Mason (https://www.seqan.de/apps/mason/) from genomes downloaded from NCBI. The training-validation-test split was done on the species level to ensure "novelty" of validation and test species. The training sets contain 10 million reads per class, validation sets - 1.25 million reads per class, and test sets - 1.25 million paired reads per class. Additional, imbalanced training sets contain 2.5 million "nonpathogenic" and 17.5 million "pathogenic" reads, keeping the mean covarage constant for all species. The temporal benchmark test set contains reads from 3 additional pathogenic species in the Pantoea genus. ## Predicting pathogenic potentials from NGS reads: novel strains of known species The BacPaCS datasets contain reads simulated from the dataset compiled by Barash et al. (https://doi.org/10.1093/bioinformatics/bty928). It this case, the training-validation-test split was done on the strain level (so different strains of the same species may be present in all three sets).
description: {"references": ["Barash, E. et al. (2019), BacPaCS\u2014Bacterial Pathogenicity Classification via Sparse-SVM. Bioinformatics, 35(12), 2001\u20132008", "Chen, I.-M. A. et al. (2019). IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Research, 47(D1), D666\u2013D677."]}
DOI: 10.5281/zenodo.3678562
title: Simulated NGS read datasets for bacterial pathogenic potential prediction

description: ## Predicting pathogenic potentials from NGS reads: novel bacterial species This repository contains simulated Illumina read datasets for bacterial pathogenic potential prediction and associated metadata extracted from the IMG Database (https://img.jgi.doe.gov/). The reads are 250bp long and were simulated with Mason (https://www.seqan.de/apps/mason/) from genomes downloaded from NCBI. The training-validation-test split was done on the species level to ensure "novelty" of validation and test species. The training sets contain 10 million reads per class, validation sets - 1.25 million reads per class, and test sets - 1.25 million paired reads per class. Additional, imbalanced training sets contain 2.5 million "nonpathogenic" and 17.5 million "pathogenic" reads, keeping the mean covarage constant for all species. The temporal benchmark test set contains reads from 3 additional pathogenic species in the Pantoea genus. ## Predicting pathogenic potentials from NGS reads: novel strains of known species The BacPaCS datasets contain reads simulated from the dataset compiled by Barash et al. (https://doi.org/10.1093/bioinformatics/bty928). It this case, the training-validation-test split was done on the strain level (so different strains of the same species may be present in all three sets).
description: {"references": ["Barash, E. et al. (2019), BacPaCS\u2014Bacterial Pathogenicity Classification via Sparse-SVM. Bioinformatics, 35(12), 2001\u20132008", "Chen, I.-M. A. et al. (2019). IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Research, 47(D1), D666\u2013D677."]}
DOI: 10.6073/pasta/91782ca741d448eff8ebe63fa387b351
title: Greenup values for interior alaska 1976 - Present

description: Greenup, the frequent rapid transformation of Interior Alaska from Brown space to spring green as the leaves deciduous trees burst forth, is suddenly enough to allow assignment of a single date in a given area to this culmination of a key biological process. Greenup is important for more than just an aesthetic or biological perspective. For example, once Greenup has occurred, there is a rapid increase in evapotranspiration. Which in turn has important consequences in the areas mesoscale meteorology. Here, dates for green around Fairbanks for more than 20 springs are correlated against several different temperature derived indices calculated entirely from routinely available climatological data. We examined a variety of degree day statistics and temperature thresholds. The relationships between these parameters and Greenup dates in Interior Alaska should be useful in projecting future greenup dates. They may also be useful to post analyze Greenup dates in years for which there is meteorological data but no surviving record of Greenup dates.
DOI: 10.6073/pasta/0c60c37f60d7f77f8646018d821e6481
title: Bonanza Creek Experimental Forest: Hourly Soil Moisture at varying depths from 2002 to Present

description: A collection of soil moisture from upland and floodplain sites. Moisture is recorded from a range of depths: 5, 10, 20, and 50 cm. Data was recorded using Campbell Scientific dataloggers. The sensors in use are CS615 water content reflectometers. The period average is recorded and converted to volumetric water content using the Topp's equation.
DOI: 10.6073/pasta/8366b043fbb4dfc220196425284d90a7
title: Tree inventory data taken at BCEF sites (1989-Present)

description: This is the data from the periodic (3-10 yr interval) tree inventory monitoring of tree growth within the vegetation control plots. In 2013 and 2014, an intial tree inventory was done on each site belonging to the Regional Site Network (RSN). Some young RSN sites had no, or very few trees.
DOI: 10.6073/pasta/a01890214ed4a1e6e449181689b7f604
title: Bonanza Creek Experimental Forest: Hourly Wind Speed and Wind Direction at 3m and 10 m from 1988 to Present

description: This dataset contains the hourly output from Wind sensors for the Bonanza Creek Experimental Forest (BCEF). This includes Level 3 weather stations as well as smaller scale and temporal studies. This data can be sorted and viewed by site, year, hour, height of measurement, mean, min, and max value. Updates of each site are different since some are still on going while other had only a 2-3 year life cycle. Winter measurements may be inaccurate due to snow cover.
DOI: 10.6073/pasta/b8258ab0d1ee0707d3d6fd0ee460545c
title: Bonanza Creek Experimental Forest: Hourly Precipitation data, 1988 to Present

description: Precipitation is measured at many sites within BCEF using TE525 tipping buckets. These sensors are able to detect .254mm of precipitation and record hourly totals of rainfall or throughfall. It should be noted the tipping buckets within forested sites are subject to clogging by forest debris whcih can result in under reporting of rain events as well as over reporting upon release of the collected precipitation. There is data from floodplains to upland sites along a range of successional gradients.
DOI: 10.6073/pasta/a3d132eae808914131dc3f2ddcb5e403
title: Bonanza Creek Experimental Forest: Hourly Relative Humidity (mean, min, max) at 50 cm and 150 cm from 1988 to Present

description: This dataset contains the hourly output from relative humidity sensors for the Bonanza Creek Experimental Forest (BCEF). Most sites have two sensors, one at 50 cm and one at 150cm. This includes Level 3 weather stations as well as smaller scale and temporal studies. This data can be sorted and viewed by site, year, hour, height of measurement, mean, min, and max value. Updates of each site are different since some are still on going while other had only a 2 - 3 year life cycle. This information can be found in the site descriptions and in the metadata.
DOI: 10.6073/pasta/fc1f61d73a5f4cb73b9b4757d5e29be6
title: Bonanza Creek Experimental Forest: Hourly Snow depth data, 1988 to Present

description: Snow depth is measured hourly at both the LTER1 and LTER2 research stations within Bonanza Creek Experimental Forest using a Campbell Scientific data logger and a SR50 sonic snow depth sensor.
DOI: 10.6073/pasta/937dadb2ba822a7a9987f394160ef4f1
title: Bonanza Creek Experimental Forest: Hourly Soil Temperature at varying depths from 1988 to Present

description: A collection of soil temperatures from upland and floodplain sites. Temperatures are recorded from a range of depths: Surface,0, 5, 10, 20, 50, 100, and 200 cm. Depths represent the depth below the organic mineral interface (O/M) that the sensor was placed. Sensors in the organic soil layers were added at some sites and are given a negative depth value to indicate they were placed above the O/M interface. There is also a Surface temperature which is kept in the top few centimeters of the forest floor.