cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48815/n5rp44
title: Predicting fractures and pain using chest x-rays
title: A Nightingale Open Science dataset

description: For many older patients—and some younger ones—a fracture marks the beginning of the end. The fracture itself is seldom fatal; but it sets off a downward spiral of pain, decreased mobility, physical deconditioning, debility, and ultimately death. This is why screening for osteoporosis, recommended today for women starting at age 65, is so critical: the appearance of bones on a special type of x-ray (called a DEXA scan) shows us who is at high risk of fractures, and lets us start treatments to prevent them before they happen. Given the massive costs of fractures—to both patients, and the health care system, which a recent report put at nearly $60 billion for fractures in US Medicare patients alone—it’s clear that our current screening strategies are not adequate. For one thing, despite established guidelines calling for universal screening over age 65, the vast majority of women don’t get it—not to mention the fact that many fractures occur in men and younger people, for whom guidelines don’t recommend screening. So it would be very useful to find another way to predict fractures at scale, using routinely available data. The chest x-ray is, by far, the most commonly-performed radiological study in the world, done when patients see their doctor for a cough, chest or back pain, before surgery, in the ER, on admission to the hospital, and in a variety of other settings. An interesting fact about the ‘chest’ x-ray is that it also gets a very clear view of the spine, from neck to the upper lumbar area. And the spine is an excellent place to assess the quality and quantity of bone, which may hold signal for predicting future fractures.
DOI: 10.48815/n54w2v
title: Diagnosing 'silent' heart attack using ECG waveforms
title: A Nightingale Open Science dataset

description: Every year, millions of heart attacks happen around the world. But up to 78% of them are undiagnosed or “silent”. This means a large fraction of people with heart attack never get the cocktail of drugs known to save lives, by preventing future heart attacks and sudden death. Today, doctors can order tests (like MRIs or ultrasounds) to diagnose patients when they suspect a prior heart attack. But the reason so many heart attacks remain silent is precisely because doctors and patients don’t even suspect a heart attack has happened. Finding new ways to diagnose these undiagnosed heart attacks at scale could dramatically expand access to life-saving medications. And because of our close partnership with the county health system that sourced these data, algorithms developed on the platform, once validated, have a clear pathway for making it into clinical use and helping real patients. Electrocardiograms (ECGs) are a cheap, widespread test done everywhere in the health care system: during annual checkups, ER visits, before surgical procedures, etc. Doctors have learned to diagnose some limited signs of prior heart attack on ECGs (like ‘Q waves’), but these coarse findings still miss about 80% of prior heart attacks. We know that algorithms can match human performance on ECG interpretation—but could they do better, by systematically mining ECG waveforms for signals that might identify prior heart attacks? We don’t know, because there have not historically been datasets linking ECGs to high-quality labels on prior heart attack.
DOI: 10.48815/n5159b
title: Identifying high-risk breast cancer using digital pathology images
title: A Nightingale Open Science dataset

description: Every year, 40 million women get a mammogram; some go on to have an invasive biopsy to better examine a concerning area. Underneath these routine tests lies a deep—and disturbing—mystery. Since the 1990s, we have found far more 'cancers', which has in turn prompted vastly more surgical procedures and chemotherapy. But death rates from metastatic breast cancer have hardly changed. When a pathologist looks at a biopsy slide, she is looking for known signs of cancer: tubules, cells with atypical looking nuclei, evidence of rapid cell division. These features, first identified in 1928, still underlie critical decisions today: which women must receive urgent treatment with surgery and chemotherapy? And which can be prescribed “watchful waiting”, sparing them invasive procedures for cancers that would not harm them?
DOI: 10.6084/m9.figshare.23949507.v2
title: Dataset

description: dune dataset
DOI: 10.48815/n5h59p
title: Detecting Active Tuberculosis Bacilli on TB Smears
title: A Nightingale Open Science dataset

description: Tuberculosis is one of the leading infectious causes of death worldwide . Each year, millions of individuals contract and develop active TB without knowing . Case identification and treatment are the primary methods for controlling spread as there is no effective TB vaccine for adults. Unfortunately, delays in diagnosis are common, especially in resource-limited settings, and can worsen individual outcomes and perpetuate transmission of the disease. Recently, global TB control has been severely impacted by the effects of the COVID-19 pandemic on health care systems, and mortality from TB is on the rise for the first time in over a decade. Despite recent advances in genomic testing, the pillar of diagnosing active pulmonary TB remains microscopy for the identification of mycobacterium tuberculosis in the respiratory sputum. This requires microscopists with the time and training to accurately review all suspected cases of TB, which may not be routinely available in rural or developing nations. Automated digital microscopy has been proposed as a cost-effective solution . An automated algorithm that could reliably detect mycobacterium on samples from patients with suspected TB would lead to more accurate and timely diagnosis of active TB in many places that currently have barriers to both.
DOI: 10.13011/m3-dhjy-e943
title: Investigation of grain size and geometrically necessary dislocation density dependence of flow stress in Mg-4Al by using nanoindentation
DOI: 10.5066/p959nrf3
title: Brewer's sparrow density in the Wyoming Basins Ecoregional Assessment area

description: Map of Brewer's sparrow density (birds/ha) in relation to vegetation, abiotic, and anthropogenic features.
DOI: 10.6084/m9.figshare.23959329
title: unannotated efferocytosis images

description: unannotated efferocytosis images
DOI: 10.6084/m9.figshare.23959329.v1
title: unannotated efferocytosis images

description: unannotated efferocytosis images
DOI: 10.6084/m9.figshare.23959332
title: annotated ncfem1 images

description: annotated ncfem1 images
DOI: 10.6084/m9.figshare.23959332.v1
title: annotated ncfem1 images

description: annotated ncfem1 images
DOI: 10.6084/m9.figshare.23959335
title: Quantification Killifish LIver image analysis

description: RNAscope quantification<br>To quantify the rate of hepatocyte efferocytosis, single Z-plane images were analyzed using the image analysis software QuPath v0.4.351. Cell boundaries were predicted using the “Cell Detection” function with parameters as modified from default settings as follows: Nucleus background radius = 5 µm, Nucleus minimum area = 5 µm2, Intensity Threshold = 5. Cells were then classified into three cell types: hepatocytes, erythrocytes, and efferocytosing hepatocytes using the “Train object classifier” function, which used the “Artificial neural network (ANN_MLP)” classifier option. Hepatocytes were cells with nearly exclusive fgg signal, erythrocytes were called as cells containing nearly exclusive cahz signal, and efferocytosing hepatocytes were called as cells containing cahz signal on a background consisting of primarily fgg signal. Any cells below the detection threshold likely represent other cell types and were ignored for this analysis. The ANN_MLP classifier was trained on manually annotated data from three biological replicates and checked manually for accuracy before being applied to all images in the dataset (40 images total). The percentage of each cell type was calculated using Microsoft Excel v16.75. To quantify ncFem1 expression in female and male liver cells, DAPI, ncFem1, and ppIB signals were quantified by using ImageJ2 v2.9.0/1.53t. For each animal, 2 images from 2 liver sections were processed (4 images per animal, 40 images total). Images were split into their different channels (DAPI, ncFem1, and ppIB) and a uniform threshold range (Image &gt; Adjust &gt; Threshold) of 30-255 for each channel was applied to all images. The “Analyze Particles” function on FIJI was used to obtain the areas of all detected particles, which were summed in Microsoft Excel v16.75 to generate the total area of the signal of interest per image. The mean of the total area was then calculated for each animal and the ratio of ncFem1:ppIB area was calculated. Those ratios were then divided by the median male ncFem1:ppIB area to compare ncFem1:ppIB ratio to a male baseline. To test for ncFem1 nuclear localization, images were analyzed in QuPath v0.4.3 and cell boundaries (including nuclear boundaries based on DAPI staining) were predicted as described earlier for efferocytosis classification. To infer nuclear and cytoplasmic signal strength, the mean signal intensity of ncFem1 and ppIB channels over the nucleus and cytoplasm of each cell were exported from QuPath. For each channel, the nuclear:cytoplasmic ratio was reported for each cell in each image. The mean nuclear:cytoplasmic ratio was calculated per image and the median value per animal was taken.
DOI: 10.6084/m9.figshare.23959335.v1
title: Quantification Killifish LIver image analysis

description: RNAscope quantification<br>To quantify the rate of hepatocyte efferocytosis, single Z-plane images were analyzed using the image analysis software QuPath v0.4.351. Cell boundaries were predicted using the “Cell Detection” function with parameters as modified from default settings as follows: Nucleus background radius = 5 µm, Nucleus minimum area = 5 µm2, Intensity Threshold = 5. Cells were then classified into three cell types: hepatocytes, erythrocytes, and efferocytosing hepatocytes using the “Train object classifier” function, which used the “Artificial neural network (ANN_MLP)” classifier option. Hepatocytes were cells with nearly exclusive fgg signal, erythrocytes were called as cells containing nearly exclusive cahz signal, and efferocytosing hepatocytes were called as cells containing cahz signal on a background consisting of primarily fgg signal. Any cells below the detection threshold likely represent other cell types and were ignored for this analysis. The ANN_MLP classifier was trained on manually annotated data from three biological replicates and checked manually for accuracy before being applied to all images in the dataset (40 images total). The percentage of each cell type was calculated using Microsoft Excel v16.75. To quantify ncFem1 expression in female and male liver cells, DAPI, ncFem1, and ppIB signals were quantified by using ImageJ2 v2.9.0/1.53t. For each animal, 2 images from 2 liver sections were processed (4 images per animal, 40 images total). Images were split into their different channels (DAPI, ncFem1, and ppIB) and a uniform threshold range (Image &gt; Adjust &gt; Threshold) of 30-255 for each channel was applied to all images. The “Analyze Particles” function on FIJI was used to obtain the areas of all detected particles, which were summed in Microsoft Excel v16.75 to generate the total area of the signal of interest per image. The mean of the total area was then calculated for each animal and the ratio of ncFem1:ppIB area was calculated. Those ratios were then divided by the median male ncFem1:ppIB area to compare ncFem1:ppIB ratio to a male baseline. To test for ncFem1 nuclear localization, images were analyzed in QuPath v0.4.3 and cell boundaries (including nuclear boundaries based on DAPI staining) were predicted as described earlier for efferocytosis classification. To infer nuclear and cytoplasmic signal strength, the mean signal intensity of ncFem1 and ppIB channels over the nucleus and cytoplasm of each cell were exported from QuPath. For each channel, the nuclear:cytoplasmic ratio was reported for each cell in each image. The mean nuclear:cytoplasmic ratio was calculated per image and the median value per animal was taken.
DOI: 10.6084/m9.figshare.23959344.v1
title: unannotated <i>ncfem1</i> images

description: unannotated ncfem1 images
DOI: 10.6084/m9.figshare.23959344
title: unannotated <i>ncfem1</i> images

description: unannotated ncfem1 images
DOI: 10.6084/m9.figshare.23960493
title: 20230816_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.23960493.v1
title: 20230816_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.23959338
title: annotated images efferocytosis

description: annotated images efferocytosis (see main project)
DOI: 10.6084/m9.figshare.23959338.v1
title: annotated images efferocytosis

description: annotated images efferocytosis (see main project)
DOI: 10.5066/p979q8qe
title: Brewer's sparrow distribution in the Wyoming Basins Ecoregional Assessment area

description: Map of Brewer's sparrow distribution developed from an abundance map created using multi-scale vegetation, abiotic, and anthropogenic features.
DOI: 10.6084/m9.figshare.23929635
title: Imaging Mass Cytometry Raw Image Files-Multiple Myeloma

description: Imaging mass cytometry data of multiple myeloma bone marrow and peripheral blood malignant plasma, immune, and other tumor microenvironment cells.
DOI: 10.6084/m9.figshare.23929635.v1
title: Imaging Mass Cytometry Raw Image Files-Multiple Myeloma

description: Imaging mass cytometry data of multiple myeloma bone marrow and peripheral blood malignant plasma, immune, and other tumor microenvironment cells.
DOI: 10.5066/p967jnpv
title: Lark sparrow density in the Wyoming Basins Ecoregional Assessment area

description: Map of lark sparrow density (birds/ha) in relation to vegetation, abiotic, and anthropogenic features.
DOI: 10.5281/zenodo.8250475
title: Tara Eukaryote Annotated 18s OTU Counts

description: "Marine plankton support global biological and geochemical processes. Surveys of their biodiversity have hitherto been geographically restricted and have not accounted for the full range of plankton size. We assessed eukaryotic diversity from 334 size-fractionated photic-zone plankton communities collected across tropical and temperate oceans during the circumglobal Tara Oceans expedition. We analyzed 18S ribosomal DNA sequences across the intermediate plankton-size spectrum from the smallest unicellular eukaryotes (protists, &gt;0.8 micrometers) to small animals of a few millimeters. Eukaryotic ribosomal diversity saturated at ~150,000 operational taxonomic units, about one-third of which could not be assigned to known eukaryotic groups... Most eukaryotic plankton biodiversity belonged to heterotrophic protistan groups." https://www.science.org/doi/10.1126/science.1261605
DOI: 10.5281/zenodo.8250474
title: Tara Eukaryote Annotated 18s OTU Counts

description: "Marine plankton support global biological and geochemical processes. Surveys of their biodiversity have hitherto been geographically restricted and have not accounted for the full range of plankton size. We assessed eukaryotic diversity from 334 size-fractionated photic-zone plankton communities collected across tropical and temperate oceans during the circumglobal Tara Oceans expedition. We analyzed 18S ribosomal DNA sequences across the intermediate plankton-size spectrum from the smallest unicellular eukaryotes (protists, &gt;0.8 micrometers) to small animals of a few millimeters. Eukaryotic ribosomal diversity saturated at ~150,000 operational taxonomic units, about one-third of which could not be assigned to known eukaryotic groups... Most eukaryotic plankton biodiversity belonged to heterotrophic protistan groups." https://www.science.org/doi/10.1126/science.1261605
DOI: 10.25345/c5x34n29j
title: MassIVE MSV000092662 - G beta 5- PhLP1-CCT crosslinks
title: Crosslink mass spectrometry datasets using long-chain succinimidyl diazirine and trypsin or trypsin/AspN digestion on purified G beta 5-PhLP1-CCT complex.
DOI: 10.18112/openneuro.ds004692.v1.0.0
title: Widespread frontoparietal fMRI activity is greatly affected by changes in criterion placement, not discriminability, during recognition memory and visual detection tests
DOI: 10.5066/p908cnrc
title: Sage sparrow density in the Wyoming Basins Ecoregional Assessment area

description: Map of sage sparrow density (birds/ha) in relation to vegetation, abiotic, and anthropogenic features.
DOI: 10.5281/zenodo.8250519
title: Data from: "Multiscale variability in nutrients and secondary metabolites in a bat-dispersed neotropical fruit"

description: Original data and R code accompanying our paper in <em>Ecology & Evolution</em> by Gelambi M. & Whitehead, S. R. (2023). Ripe fleshy fruits contain not only nutrients but also a diverse array of many secondary metabolites. Nutrients serve as a reward for mutualists, whereas defensive metabolites protect the fruit against pests and predators. The composition of these chemical traits is highly variable, both across different plants and even within repeating structures on the same individual plant. This intraspecific and intraindividual variation has important fitness consequences for both plants and animals, yet patterns of variation and covariation in nutrients and secondary metabolites are not well understood, especially at smaller scales. Here, we investigate the multiscale variation and covariation between nutrients and defensive metabolites in <em>Piper sancti-felicis </em>ripe fruits. Means and variances of sugars, proteins, phenolics, and alkenylphenols vary greatly among plants, and at least 50% of the trait variation occurs at the intraindividual level. Also, we found that proteins, but not sugars, were correlated with phenolics and alkenylphenols at multiple scales, suggesting trait variation in protein content may be more constrained than sugars. Our findings emphasize the importance of examining patterns across scales and provide the groundwork to better understand how complex patterns of variation and covariation in nutrients and defensive metabolites shape ecological interactions surrounding fruits.
DOI: 10.5281/zenodo.8250520
title: Data from: "Multiscale variability in nutrients and secondary metabolites in a bat-dispersed neotropical fruit"

description: Original data and R code accompanying our paper in <em>Ecology & Evolution</em> by Gelambi M. & Whitehead, S. R. (2023). Ripe fleshy fruits contain not only nutrients but also a diverse array of many secondary metabolites. Nutrients serve as a reward for mutualists, whereas defensive metabolites protect the fruit against pests and predators. The composition of these chemical traits is highly variable, both across different plants and even within repeating structures on the same individual plant. This intraspecific and intraindividual variation has important fitness consequences for both plants and animals, yet patterns of variation and covariation in nutrients and secondary metabolites are not well understood, especially at smaller scales. Here, we investigate the multiscale variation and covariation between nutrients and defensive metabolites in <em>Piper sancti-felicis </em>ripe fruits. Means and variances of sugars, proteins, phenolics, and alkenylphenols vary greatly among plants, and at least 50% of the trait variation occurs at the intraindividual level. Also, we found that proteins, but not sugars, were correlated with phenolics and alkenylphenols at multiple scales, suggesting trait variation in protein content may be more constrained than sugars. Our findings emphasize the importance of examining patterns across scales and provide the groundwork to better understand how complex patterns of variation and covariation in nutrients and defensive metabolites shape ecological interactions surrounding fruits.
DOI: 10.5066/p9driwm8
title: Sage thrasher density in the Wyoming Basins Ecoregional Assessment area

description: Map of sage thrasher density (birds/ha) in relation to vegetation, abiotic, and anthropogenic features.
DOI: 10.5066/p972steu
title: Vesper sparrow density in the Wyoming Basins Ecoregional Assessment area

description: Map of vesper sparrow density (birds/ha) in relation to vegetation, abiotic, and anthropogenic features.
DOI: 10.5281/zenodo.8250542
title: PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes

description: Supplementary Data for "PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes"
DOI: 10.5281/zenodo.8250541
title: PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes

description: Supplementary Data for "PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes"
DOI: 10.7916/5xps-y460
title:  Triangular forearm support and rotator cuff syndrome- EMG insight into how it works with 2-year follow-up
DOI: 10.5517/ccdc.csd.cc280mwt
title: CCDC 2085986: Experimental Crystal Structure Determination

description: Related Article: K. Naresh Babu, Fedaa Massarwe, Israa Shioukhi, Ahmad Masarwa|2021|Angew.Chem.,Int.Ed.|60|26199|doi:10.1002/anie.202111164
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc280mxv
title: CCDC 2085987: Experimental Crystal Structure Determination

description: Related Article: K. Naresh Babu, Fedaa Massarwe, Israa Shioukhi, Ahmad Masarwa|2021|Angew.Chem.,Int.Ed.|60|26199|doi:10.1002/anie.202111164
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc280myw
title: CCDC 2085988: Experimental Crystal Structure Determination

description: Related Article: K. Naresh Babu, Fedaa Massarwe, Israa Shioukhi, Ahmad Masarwa|2021|Angew.Chem.,Int.Ed.|60|26199|doi:10.1002/anie.202111164
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.5089038
title: Spatial interpolation of air pollutant and meteorological variables in Central Amazonia

description: This dataset presents data of aerosol, trace-gases and meteorological variables from the Amazon Rainforest region, resulting from an interpolation process. The original data were collected from the GOAmazon 2014/15 project, from the Atmospheric Radiation Measurement (ARM) repository.
DOI: 10.5281/zenodo.5089039
title: Spatial interpolation of air pollutant and meteorological variables in Central Amazonia

description: This dataset presents data of aerosol, trace-gases and meteorological variables from the Amazon Rainforest region, resulting from an interpolation process. The original data were collected from the GOAmazon 2014/15 project, from the Atmospheric Radiation Measurement (ARM) repository.