cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.tb2rbp038
title: Data for empirical example in: An effect size for comparing the strength of morphological integration across studies

description: Understanding how and why phenotypic traits covary is a major interest in
 evolutionary biology. Biologists have long sought to characterize the
 extent of morphological integration in organisms, but comparing levels of
 integration for a set of traits across taxa has been hampered by the lack
 of a reliable summary measure and testing procedure. Here we propose a
 standardized effect size for this purpose, calculated from the relative
 eigenvalue variance, Vrel. First we evaluate several eigenvalue dispersion
 indices under various conditions, and show that only Vrel remains stable
 across samples size and the number of variables. We then demonstrate that
 Vrel accurately characterizes input patterns of covariation, so long as
 redundant dimensions are excluded from the calculations. However, we also
 show that the variance of the sampling distribution of Vrel depends on
 input levels of trait covariation, making Vrel unsuitable for direct
 comparisons. As a solution, we propose transforming Vrel to a standardized
 effect size (Z-score) for representing the magnitude of integration for a
 set of traits. We also propose a two-sample test for comparing the
 strength of integration between taxa, and show that this test displays
 appropriate statistical properties. We provide software for implementing
 the procedure, and an empirical example illustrates its use.
description: An HDI-120 surface scanner was used to capture 3D surface scans
 of individual os coxa from adult specimens, and from each surface scan,
 the locations of eight three-dimensional landmarks were digitized using
 the program Landmark. Landmark configurations were then subjected to a
 Generalized Procrustes Analysis using the R-package geomorph, and a set of
 seven linear distance measures were then derived from the
 Procrustes-aligned coordinates. Additionally, we multiplied the Procrustes
 coordinates by size, and then calculated a second set of linear distance
 measures from these size-incorporated coordinates. 
description: Files may be opened and processed in R, using the package
 geomorph.
DOI: 10.22008/promice/data/ice_discharge/d/v02/crhzy6
title: sector.nc

description: Sector discharge, coverage, and error. Git hash: be7fce3
DOI: 10.22008/promice/data/ice_discharge/d/v02/fe8wot
title: gate_coverage.csv

description: Gate coverage. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/eqmp8k
title: gate_D.csv

description: Gate discharge. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/novbon
title: gate_err.csv

description: Gate errors. Git hash: 0eef835
DOI: 10.5061/dryad.zkh1893cn
title: Genetic variation data to explore the phylogeny of Iberian weedy rice

description: Weedy rice, a damaging conspecific weed of cultivated rice, has arisen
 multiple times independently around the world. Understanding all weedy
 rice origins is necessary to create more effective weed management
 strategies. The origins of weedy rice in Spain and Portugal, where there
 are no native Oryza species are unknown. We used genotyping-by-sequencing
 (GBS) to understand the origin of Iberian weedy rice and its relationship
 to other weedy, wild, and cultivated rice groups worldwide. This is the
 variant call format (vcf) file for all 481 accessions including weedy,
 wild, and cultivated rice. We used this dataset to understand the
 phylogenetic relationships and population structure among these different
 populations, especially the Iberian weedy rice. We find that weedy rice in
 the Iberian Peninsula has primarily evolved through de-domestication of
 temperate japonica cultivars, with minor origins from exotic weedy rice.
description: Details in the publication. 
description: Text editor or vcftools.
DOI: 10.22008/promice/data/ice_discharge/d/v02/cdudr9
title: gate.nc

description: Gate discharge, coverage, error, and metadata. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/dpkcnk
title: GIS_D.csv

description: Greenland discharge. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/ljaovr
title: GIS_coverage.csv

description: Greenland coverage. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/hsw2uy
title: GIS_err.csv

description: Greenland error. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/0xqdtq
title: README.txt

description: README. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/onfymt
title: GIS.nc

description: Greenland discharge, coverage, and error. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/itntkd
title: region_coverage.csv

description: Regional coverage. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/8pvli7
title: region_err.csv

description: Regional error. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/dyseud
title: region_D.csv

description: Regional discharge. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/9668m3
title: sector_coverage.csv

description: Sector coverage. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/k3zbv5
title: region.nc

description: Regional discharge, coverage, and error. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/hqk5jy
title: sector_D.csv

description: Sector discharge. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/k5c58c
title: sector.nc

description: Sector discharge, coverage, and error. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/vv9jgn
title: sector_err.csv

description: Sector error. Git hash: 0eef835
DOI: 10.22008/promice/data/ice_discharge/d/v02/nssdds
title: gate_coverage.csv

description: Gate coverage. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/uioolu
title: gate_D.csv

description: Gate discharge. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/6obqv3
title: gate_err.csv

description: Gate errors. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/ugw3xd
title: gate.nc

description: Gate discharge, coverage, error, and metadata. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/3ltwvi
title: GIS_coverage.csv

description: Greenland coverage. Git hash: 92396c4
DOI: 10.5061/dryad.2rbnzs7r0
title: Data from: Evaluation of fecal metabarcoding for studying the diets of sympatric dusky salamanders (Desmognathus)

description: Amphibian diet studies often rely on visual identification of prey
 obtained through forced regurgitation or dissection. These approaches are
 somewhat invasive and often lack taxonomic specificity, which can
 discourage diet studies involving at-risk species and limit fine-scale
 investigations of diet composition. Here, we employ and assess a
 non-invasive molecular technique to characterize the diets of three
 co-occurring stream-dwelling salamander species (Desmognathus ocoee,
 Desmognathus monticola, and Desmognathus quadramaculatus) and investigate
 possible dietary partitioning within and among species. We used DNA
 metabarcoding to classify the arthropod prey communities from fecal
 samples of field-collected salamanders and investigated associations with
 predator species and snout-vent length (SVL). Of 200 salamanders captured
 and held for 24 hrs, 38 (19%) produced fecal samples containing arthropod
 DNA. We identified 53 prey taxa, of which 27 we could classify to species,
 12 to genus, 10 to family, and 4 to order. We found no evidence of dietary
 partitioning among species or by SVL. Individual fecal samples generally
 contained few taxa, and few taxa were shared among samples, suggesting
 that our sample size likely limited the power of our inference. Our
 results support the utility of fecal metabarcoding as a non-invasive and
 taxonomically precise alternative to traditional diet analysis techniques.
 However, researchers should also consider the challenges associated with
 fecal metabarcoding (e.g., infrequent defecation by study organisms)
 before using it to complement more traditional methods.
DOI: 10.22008/promice/data/ice_discharge/d/v02/fnzaup
title: GIS_err.csv

description: Greenland error. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/td7xpt
title: GIS_D.csv

description: Greenland discharge. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/hk7r7b
title: GIS.nc

description: Greenland discharge, coverage, and error. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/xkxcau
title: README.txt

description: README. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/24wchj
title: region_coverage.csv

description: Regional coverage. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/5u97ru
title: region_err.csv

description: Regional error. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/zrpjwm
title: region_D.csv

description: Regional discharge. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/wk4ncx
title: sector_coverage.csv

description: Sector coverage. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/zgafn6
title: region.nc

description: Regional discharge, coverage, and error. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/fqjgr9
title: sector_D.csv

description: Sector discharge. Git hash: 92396c4
DOI: 10.5061/dryad.bnzs7h4dg
title: A proteome-wide map of chaperone-assisted protein refolding in a cytosol-like milieu

description: The journey by which proteins navigate their energy landscapes to their
 native structures is complex, involving (and sometimes requiring) many
 cellular factors and processes operating in partnership with a given
 polypeptide chain’s intrinsic energy landscape.  The
 cytosolic environment and its complement of chaperones play critical roles
 in granting many proteins safe passage to their native states; however, it
 is challenging to interrogate the folding process for large numbers of
 proteins in a complex background with most biophysical
 techniques.  Hence, most chaperone-assisted protein
 refolding studies are conducted in defined buffers on single purified
 clients.  Here, we develop a limited-proteolysis mass
 spectrometry approach paired within an isotope-labeling strategy to
 globally monitor the structures of refolding E. coli proteins in the
 cytosolic medium and with the chaperones, GroEL/ES (Hsp60) and
 DnaK/DnaJ/GrpE (Hsp70/40).  GroEL can refold the
 majority (85%) of the E. coli proteins for which we have data, and is
 particularly important for restoring acidic proteins and proteins with
 high molecular weight, trends that come to light because our assay
 measures the structural outcome of the refolding process itself, rather
 than binding or aggregation.  For the most part, DnaK
 and GroEL refold a similar set of proteins, supporting the view that
 despite their vastly different structures, these two chaperones both
 unfold misfolded states, as one mechanism in
 common.  Finally, we identify a cohort of proteins that
 are intransigent to being refolded with either
 chaperone.  The data support a model in which
 chaperone-nonrefolders may fold most efficiently cotranslationally, and
 then remain kinetically trapped in their native conformations.
description: Please see attached manuscript for full detailed methods on data
 collection and processing. In brief, Proteome Discoverer (PD) Software
 Suite (v2.4, Thermo Fisher) and the Minora Algorithm were used to analyze
 mass spectra and perform Label Free Quantification (LFQ) of detected
 peptides. The data were searched against Escherichia coli (UP000000625,
 Uniprot) reference proteome database. For peptide identification, either
 the PD Sequest HT node (for non-pseudo-SILAC samples) or PD MSFragger node
 (pseudo-SILAC) were used, each using a semi-tryptic search allowing up to
 2 missed cleavages. PD LFQ data was exported in a three tiered hierarchy
 (Protein &gt; Peptide Group &gt; Consensus Feature) and analyzed
 using our in-house script Analyzer_V18.py (available on GitHub). Analyzer
 compiles each sequenced peptide, along with its associated metadata, the
 identity of the peptide as tryptic or half-tryptic (and if so, the
 location of the proteinase K cleavage site), abundance ratio, normalized
 abundance ratio, and P-value, coefficient of variation and outputs it into
 a _out.txt file. 
description: Peptide out files are (.txt) files and can be opened and
 visualized using Microsoft Excel.
DOI: 10.22008/promice/data/ice_discharge/d/v02/1tkjqu
title: sector.nc

description: Sector discharge, coverage, and error. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/usobal
title: sector_err.csv

description: Sector error. Git hash: 92396c4
DOI: 10.22008/promice/data/ice_discharge/d/v02/1ygaxw
title: gate_D.csv

description: Gate discharge. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/rfvmuq
title: gate_coverage.csv

description: Gate coverage. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/lh7sjo
title: gate.nc

description: Gate discharge, coverage, error, and metadata. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/o8ojlk
title: gate_err.csv

description: Gate errors. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/4edjd3
title: GIS_coverage.csv

description: Greenland coverage. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/0k70sv
title: GIS_err.csv

description: Greenland error. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/wkwyfp
title: GIS_D.csv

description: Greenland discharge. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/dkja3x
title: README.txt

description: README. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/erzupz
title: GIS.nc

description: Greenland discharge, coverage, and error. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/gf9ih1
title: region_coverage.csv

description: Regional coverage. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/wsf0gs
title: region_D.csv

description: Regional discharge. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/nr23zn
title: region_err.csv

description: Regional error. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/a1snqp
title: sector_coverage.csv

description: Sector coverage. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/uimwii
title: region.nc

description: Regional discharge, coverage, and error. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/hc7wsv
title: sector_D.csv

description: Sector discharge. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/qhbf1g
title: sector.nc

description: Sector discharge, coverage, and error. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/szg8oo
title: sector_err.csv

description: Sector error. Git hash: b2f9ed7
DOI: 10.22008/promice/data/ice_discharge/d/v02/pntpnv
title: gate_coverage.csv

description: Gate coverage. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/sinply
title: gate_D.csv

description: Gate discharge. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/pdovhy
title: gate_err.csv

description: Gate errors. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/rs9r7k
title: gate.nc

description: Gate discharge, coverage, error, and metadata. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/zpqvfp
title: GIS_coverage.csv

description: Greenland coverage. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/plaftr
title: GIS_err.csv

description: Greenland error. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/x5wzdz
title: GIS_D.csv

description: Greenland discharge. Git hash: 60720b1
DOI: 10.5061/dryad.79cnp5hz2
title: MODIS sea ice leads detections using a U-Net

description: Sea ice leads are long and narrow sea ice fractures.
 Despite accounting for a small fraction of the Arctic surface
 area, leads play a critical role in the energy flux between the ocean and
 atmosphere. As the volume of sea ice in the Arctic has declined
 over recent decades, it is increasingly important to monitor the
 corresponding changes in sea ice leads. An approach described in
 Hoffman et al. 2021 uses artificial intelligence (AI) to detect sea ice
 leads using satellite thermal infrared window data from the Moderate
 Resolution Imaging Spectroradiometer (MODIS). The AI used to detect sea
 ice leads in satellite imagery is a particular kind of
 convolutional neural network, a U-Net. The originally published dataset
 included only a small case study of results. Here, the dataset is expanded
 to include the daily detection of leads since 2002 for the season between
 November through April.
description: AI is used to identify sea ice leads in thermal imagery from the
 11 µm from MODIS (band 31, AQUA, and TERRA imagery). A U-Net detection
 model is run for each satellite overpass and reported as daily aggregated
 results. The lead detection results are projected into a standard 1 km
 resolution EASE-Grid 2.0 projection. The included data arrays are the
 daily number of satellite overpasses, the number of overpasses a lead is
 identified, the maximum lead detection score from the U-Net, and a lead
 mask for each EASE-Grid 2.0 pixel.  Daily files are compressed inside
 November through April seasonal tar files. The daily results are recorded
 as hdf5 format files. For each season, the daily results from November
 through April for each season are combined into a new tar file with gzip
 compression.
description: # MODIS Sea ice leads detections using a U-Net
 [https://doi.org/10.5061/dryad.79cnp5hz2](https://doi.org/10.5061/dryad.79cnp5hz2) ## Description of the data and file structure Seasonal files are tar files compressed with gzip compression. Untar seasonal file to access daily hdf5 leads detection files from November through April. Naming convention: YYYY-YYYY.SATELITE_NAME.tar.gz, where the first year is the year at the start of November and the second year is the end of April. Daily naming convention: YYYDDDSATELITE_NAME.result.h5. Arrays are 7025 x 7025, 1km resolution EASE-Grid 2.0 * coverage_count: The number of satellite overpasses in the day over water pixels * lead_count: The number of satellite overpasses in which the U-Net detection score suggests a lead. A lead detection is generally considered a positive detection where the lead count is greater or equal to 3 repeat observations within a day. Lead counts of 1 or 2 are more likely false positives. * mask: 0=non-lead, 1=lead, 2=no coverage (land and low latitude) * Max_result: maximum lead detection score at each location latlon.nc is a netcdf file that contains the latitude and longitude for each EASE-Grid 2.0 pixel location ## Version changes **01-aug-2024:** Added results for 2022-2023 and 2023-2024 seasons
DOI: 10.22008/promice/data/ice_discharge/d/v02/dwbqmm
title: GIS.nc

description: Greenland discharge, coverage, and error. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/whetdq
title: README.txt

description: README. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/sgmyul
title: region_D.csv

description: Regional discharge. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/tjepwd
title: region_coverage.csv

description: Regional coverage. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/9unikt
title: region_err.csv

description: Regional error. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/fadb9f
title: region.nc

description: Regional discharge, coverage, and error. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/0syqgo
title: sector_coverage.csv

description: Sector coverage. Git hash: 60720b1
DOI: 10.5281/zenodo.17817806
title: Test data for Galaxy IUC Seurat Tools
DOI: 10.22008/promice/data/ice_discharge/d/v02/lz9rpe
title: sector_err.csv

description: Sector error. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/z7doff
title: sector_D.csv

description: Sector discharge. Git hash: 60720b1
DOI: 10.25349/d9js6q
title: Dynamics of active liquid interfaces

description: Controlling interfaces of phase separating fluid mixtures is key to
 creating diverse functional soft materials. Traditionally, this is
 accomplished with surface-modifying agents. Using a combination of
 experiment and theory, we study how mechanical activity shapes soft
 interfaces of a phase separating mixture of an active and a passive fluid.
 Chaotic flows in the active fluid give rise to giant interfacial
 fluctuations and non-inertial propagating active waves. At high
 activities, stresses disrupt interface continuity and drive droplet pinch-
 off. In this limit, we observe an active emulsion-like steady-state
 comprising finite-sized droplets that constantly break apart, fuse and
 exchange content. Furthermore, when in contact with a solid boundary,
 active interfaces exhibit non-equilibrium wetting transitions, where the
 fluid powered by active stress climbs the wall against gravity. These
 results demonstrate the promise of me- chanically driven interfaces for
 creating a new class of lifelike soft active mat- ter.
DOI: 10.5281/zenodo.17803775
title: Test data for Galaxy IUC Seurat Tools
DOI: 10.22008/promice/data/ice_discharge/d/v02/nrzlsb
title: sector.nc

description: Sector discharge, coverage, and error. Git hash: 60720b1
DOI: 10.22008/promice/data/ice_discharge/d/v02/rt7o13
title: gate_coverage.csv

description: Gate coverage. Git hash: 527531b
DOI: 10.22008/promice/data/ice_discharge/d/v02/0xtdm8
title: gate_D.csv

description: Gate discharge. Git hash: 527531b
DOI: 10.5061/dryad.ncjsxksxn
title: Diversity-conditioned soil strengthens plant diversity-productivity relationship

description: How biodiversity affects terrestrial productivity is important to the
 maintenance of ecosystem services under global change. Although the
 crucial role of plant-soil feedbacks (PSF) in determining
 diversity-productivity relationship has been increasingly recognized in
 recent years, its legacy effects on subsequent diversity-productivity
 relationship are still unclear. We conducted a classic PSF experiment to
 assess how plant diversity-conditioned soils influenced subsequent plant
 diversity-productivity relationships, where three plant diversity levels
 (1, 4, and 8 species) were planted in soils conditioned at three diversity
 levels (conditioned by 1, 4, and 8 species for 3 years). In addition, to
 test the role of soil microbial diversity in mediating the effects of soil
 conditioning diversity, the three plant diversity levels were planted with
 low, moderate, and high soil biodiversity created by a soil inoculum
 dilution. The results showed that plant productivities were promoted by
 mixed-conditioned soils (4 and 8 species) compared to mono-conditioned
 soils (1 species). Productivity was positively related to planted
 diversity in mixed-conditioned soils, while showing no relation to planted
 diversity in mono-conditioned soils. Productivity was promoted by
 soil biodiversity when 4 and 8 species were mixed planted, while
 it did not change when 1 species was planted.  Synthesis and
 applications. Our results highlight that PSF is crucial to strengthening
 the positive effects of biodiversity on productivity, implying that
 diversifying cropping systems should be encouraged in agroecosystem
 management to benefit from positive PSF effects. The importance of soil
 legacy for optimizing plant productivity is particularly important for
 conditioning soils with an intermediate number of plant species.
DOI: 10.22008/promice/data/ice_discharge/d/v02/7cdgf7
title: gate.nc

description: Gate discharge, coverage, error, and metadata. Git hash: 527531b
DOI: 10.22008/promice/data/ice_discharge/d/v02/sbstsb
title: gate_err.csv

description: Gate errors. Git hash: 527531b
DOI: 10.22008/promice/data/ice_discharge/d/v02/iyl2ev
title: GIS_coverage.csv

description: Greenland coverage. Git hash: 527531b
DOI: 10.22008/promice/data/ice_discharge/d/v02/8lo4ho
title: GIS_err.csv

description: Greenland error. Git hash: 527531b