cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ghzjc8
title: Occurrence Download

description: A dataset containing 405 records in GBIF matching the query:

SELECT
 GBIF_ISEA3HCode(
 8, 
 decimalLatitude,
 decimalLongitude,
 COALESCE(coordinateUncertaintyInMeters, 1000)
 ) AS geogrid_id,
 CASE 
 WHEN classKey IN ('359') THEN 'Mammals'
 WHEN classKey IN ('212') THEN 'Birds'
 WHEN familyKey IN ('1103', '1104', '494', '495', '1105', '496', '497', '1106', '498', '499', '1107', '537', '538', '1153', '547', '1162', '548', '549', '550', '1163', '1164', '1165', '1166', '1167', '1305', '1067', '1306', '1307', '1308', '1068', '1069', '587', '1310', '588', '589', '1311', '1312', '1313', '590', '708', '890', '774', '889', '773', '772', '888', '765', '879') THEN 'Bony Fish'
 WHEN classKey IN ('131') THEN 'Amphibians'
 WHEN classKey IN ('216') THEN 'Insects'
 WHEN classKey IN ('11418114', '11569602', '11592253', '11493978') THEN 'Reptiles'
 WHEN phylumKey IN ('52') THEN 'Molluscs'
 WHEN classKey IN ('367') THEN 'Arachnids'
 WHEN classKey IN ('220', '196') THEN 'Flowering Plants'
 WHEN classKey IN ('194', '244', '228', '282') THEN 'Gymnosperms'
 WHEN orderKey IN ('392') THEN 'Ferns'
 WHEN phylumKey IN ('35') THEN 'Mosses'
 WHEN phylumKey IN ('95') THEN 'Sac Fungi'
 WHEN phylumKey IN ('34') THEN 'Basidiomycota'
 END AS taxonomic_group,
 COUNT(DISTINCT speciesKey) AS species_count
FROM occurrence
WHERE countryCode = 'IS'
 AND hasCoordinate = TRUE
 AND hasGeospatialIssues = FALSE
 AND coordinateUncertaintyInMeters &lt;= 2000
 AND occurrenceStatus = 'PRESENT'
 AND speciesKey IS NOT NULL
 AND basisOfRecord != 'FOSSIL_SPECIMEN'
 AND taxonRank = 'SPECIES'
 AND (
 classKey IN ('359', '212', '131', '216', '11418114', '11569602', '11592253', '11493978', '367', '220', '196', '194', '244', '228', '282')
 OR familyKey IN ('1103', '1104', '494', '495', '1105', '496', '497', '1106', '498', '499', '1107', '537', '538', '1153', '547', '1162', '548', '549', '550', '1163', '1164', '1165', '1166', '1167', '1305', '1067', '1306', '1307', '1308', '1068', '1069', '587', '1310', '588', '589', '1311', '1312', '1313', '590', '708', '890', '774', '889', '773', '772', '888', '765', '879')
 OR phylumKey IN ('52', '35', '95', '34')
 OR orderKey IN ('392')
 )
GROUP BY geogrid_id, taxonomic_group;


 The dataset's 405 records were derived from 110 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002505-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tubupj
title: Occurrence Download

description: A dataset containing 31230 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Falco peregrinus",
 "Year 2001-2026"
 ]
}

 The dataset includes 31230 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002522-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e155161
title: Advance Layoff Notice Data from the WARN Act

description: We collect novel and timely data from advance layoff notices filed under the Worker Adjustment and Retraining Notification (WARN) Act. The act requires larger employers to notify affected workers at least 60 days before a potential mass layoff. We assemble WARN data from across the United States, and for many large states our data begin in the 1990s. We aggregate these data into an unbalanced, monthly panel of the state-level number of workers affected by WARN notices, and we update this panel twice a month. We also aggregate this panel to a national-level indicator of job loss (the "WARN factor") using a dynamic factor model.<br>

<b><br>Data Collection</b><br>We update the data twice a month by collecting WARN
notices from state websites. For many states, we have extended our historical
data by using digital archives of the internet and contacting state
officials.<br> <br><b>Citation</b><br>To learn more about
the data and the dynamic factor model, see:<br>Krolikowski, Pawel M. and Kurt G. Lunsford. 2022. “Advance Layoff Notices and Aggregate Job Loss.” Federal Reserve Bank of Cleveland, Working Paper no. 20-03R. https://doi.org/10.26509/frbc-wp-202003R.<br>Krolikowski, Pawel M., and Kurt G. Lunsford. 2024 “Advance Layoff Notices and Aggregate Job Loss.” Journal of Applied Econometrics. https://doi.org/10.1002/jae.3032.<b><br><br></b><b>File Description</b><br>The file labelled WARNFiles_YYYYMMDD.zip in the
main directory contains our most recent data. All data included in this zip
file were collected as of the date listed in the file name. Previous
vintages of our data appear in the `Archived_Vintages’ folder, with the same
naming convention.<br>
<br>The zip files contain three files:<br>
<br>


1. 
README.txt

<br>2. 
WARNData_NSA_YYYYMMDD.csv


 
 This .csv contains the number of workers affected by
 WARN notices by state and month. These data are not seasonally adjusted.
 These data are the input into the dynamic factor model.
 


3. 
WARNFactors_YYYYMMDD.csv


 
 This .csv contains the output of the dynamic factor
 model. The output is labeled as follows:
 
 WARN Factor: the estimates of the factor from the
 dynamic factor model.
 MSE: the estimated mean squared errors
 of the WARN factor.
 WARN_hat: the number of workers affected by
 WARN notices as implied by the WARN factor.
 WARN_sum: the sum of the number of
 workers affected by WARN notices from several states that form a
 balanced panel beginning in January 2006. These states can change every
 update.<b>Disclaimer</b><br>These data are
updated by the authors and are not an official product of the Federal Reserve Bank of Cleveland.<br>
description: Smallest Geographic Unit: State
DOI: 10.7294/27940746.v1
title: Dataset for: Chronic stress and fear in fast- and slow-growing broilers as they age and gain weight when raised in simple or complex environments

description: The objective was to assess how environmental complexity influences fear and chronic stress in fast- and slow-growing broiler chickens as they age and gain weight. Six-hundred fast-growing (Ross 708) and 600 slow-growing (Hubbard Redbro Mini) broilers were raised in 24 pens with simple (standard) or complex (permanent and temporary enrichments) pens. Tonic immobility (TI) tests were performed on 6 broilers/pen to assess fear, with 3 sampled for primary wing feathers to assess feather corticosterone (fCORT) concentration at 4, 5, and 6 weeks of age and 1, 2, and 3 kg of body weight (TI n=576, feather n=288). Feather brightness (indicating melanin content) was calculated from images. fCORT was assayed using an ELISA. Fast-growing broilers remained in TI longer (106±6 sec) than slow-growing broilers (82±6 sec) at equal ages (p=0.013). Heavier birds remained in TI longer (105±8 sec) than lighter birds (73±8 sec; p=0.011). Feather brightness and fCORT concentration were not correlated in slow-growing broilers (ρ=0.085, p=0.320), but were negatively correlated in fast-growing broilers (ρ=-0.286; p=0.001). Young (7.5 ng/g; p=0.002) and light (8.8 ng/g; p&lt;0.001) broilers had higher fCORT concentrations than older (5.0 ng/g) and heavier (4.7 ng/g) ones. Fear increased with age, suggesting reduced self-efficacy. fCORT concentration declined with age and weight gain, suggesting stress response downregulation.
DOI: 10.3886/e155161v98
title: Advance Layoff Notice Data from the WARN Act

description: We collect novel and timely data from advance layoff notices filed under the Worker Adjustment and Retraining Notification (WARN) Act. The act requires larger employers to notify affected workers at least 60 days before a potential mass layoff. We assemble WARN data from across the United States, and for many large states our data begin in the 1990s. We aggregate these data into an unbalanced, monthly panel of the state-level number of workers affected by WARN notices, and we update this panel twice a month. We also aggregate this panel to a national-level indicator of job loss (the "WARN factor") using a dynamic factor model.<br>

<b><br>Data Collection</b><br>We update the data twice a month by collecting WARN
notices from state websites. For many states, we have extended our historical
data by using digital archives of the internet and contacting state
officials.<br> <br><b>Citation</b><br>To learn more about
the data and the dynamic factor model, see:<br>Krolikowski, Pawel M. and Kurt G. Lunsford. 2022. “Advance Layoff Notices and Aggregate Job Loss.” Federal Reserve Bank of Cleveland, Working Paper no. 20-03R. https://doi.org/10.26509/frbc-wp-202003R.<br>Krolikowski, Pawel M., and Kurt G. Lunsford. 2024 “Advance Layoff Notices and Aggregate Job Loss.” Journal of Applied Econometrics. https://doi.org/10.1002/jae.3032.<b><br><br></b><b>File Description</b><br>The file labelled WARNFiles_YYYYMMDD.zip in the
main directory contains our most recent data. All data included in this zip
file were collected as of the date listed in the file name. Previous
vintages of our data appear in the `Archived_Vintages’ folder, with the same
naming convention.<br>
<br>The zip files contain three files:<br>
<br>


1. 
README.txt

<br>2. 
WARNData_NSA_YYYYMMDD.csv


 
 This .csv contains the number of workers affected by
 WARN notices by state and month. These data are not seasonally adjusted.
 These data are the input into the dynamic factor model.
 


3. 
WARNFactors_YYYYMMDD.csv


 
 This .csv contains the output of the dynamic factor
 model. The output is labeled as follows:
 
 WARN Factor: the estimates of the factor from the
 dynamic factor model.
 MSE: the estimated mean squared errors
 of the WARN factor.
 WARN_hat: the number of workers affected by
 WARN notices as implied by the WARN factor.
 WARN_sum: the sum of the number of
 workers affected by WARN notices from several states that form a
 balanced panel beginning in January 2006. These states can change every
 update.<b>Disclaimer</b><br>These data are
updated by the authors and are not an official product of the Federal Reserve Bank of Cleveland.<br>
description: Smallest Geographic Unit: State
DOI: 10.7294/27940746
title: Dataset for: Chronic stress and fear in fast- and slow-growing broilers as they age and gain weight when raised in simple or complex environments

description: The objective was to assess how environmental complexity influences fear and chronic stress in fast- and slow-growing broiler chickens as they age and gain weight. Six-hundred fast-growing (Ross 708) and 600 slow-growing (Hubbard Redbro Mini) broilers were raised in 24 pens with simple (standard) or complex (permanent and temporary enrichments) pens. Tonic immobility (TI) tests were performed on 6 broilers/pen to assess fear, with 3 sampled for primary wing feathers to assess feather corticosterone (fCORT) concentration at 4, 5, and 6 weeks of age and 1, 2, and 3 kg of body weight (TI n=576, feather n=288). Feather brightness (indicating melanin content) was calculated from images. fCORT was assayed using an ELISA. Fast-growing broilers remained in TI longer (106±6 sec) than slow-growing broilers (82±6 sec) at equal ages (p=0.013). Heavier birds remained in TI longer (105±8 sec) than lighter birds (73±8 sec; p=0.011). Feather brightness and fCORT concentration were not correlated in slow-growing broilers (ρ=0.085, p=0.320), but were negatively correlated in fast-growing broilers (ρ=-0.286; p=0.001). Young (7.5 ng/g; p=0.002) and light (8.8 ng/g; p&lt;0.001) broilers had higher fCORT concentrations than older (5.0 ng/g) and heavier (4.7 ng/g) ones. Fear increased with age, suggesting reduced self-efficacy. fCORT concentration declined with age and weight gain, suggesting stress response downregulation.
DOI: 10.15468/dl.rfx76g
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "DistanceFromCentroidInMeters is greater than or equal to 2000",
 "Geometry POLYGON ((105.610544 14.999999, 179.999961 14.999999, 179.999961 65.08290100000001, 105.610544 65.08290100000001, 105.610544 14.999999))",
 "TaxonKey is Amblypomacentrus breviceps"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002524-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xhwjg8
title: Occurrence Download

description: A dataset containing 11964 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Picumnus lafresnayi"
 ]
}

 The dataset includes 11964 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002525-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15851243
title: Axonal Eif5a hypusination controls local translation and mitigates defects in FUS-ALS

description: Local protein synthesis is vital for neuronal function, but its dysregulation in neurodegenerative diseases remains poorly defined. Here we applied spatial transcriptomics to adult mouse motor nerve axons and cell bodies to enable subcellular mapping. Among transcripts found in mature axons, the most enriched biological process is protein translation, and localization of translation machinery was confirmed using multiplexed single-molecule spatial transcriptomics combined with immunofluorescence. Amyotrophic lateral sclerosis (ALS)-associated mutations in the RNA-binding protein fused in sarcoma (FUS), which suppress local translation, disrupt the compartment-specific RNA signatures, including components of the translation machinery. In particular, eukaryotic initiation factor 5a (Eif5a), a translation factor involved in elongation and termination, is found to be locally impaired in mutant FUS axons with reduced levels of its active hypusinated form. Axon-specific treatment with polyamine spermidine restores Eif5a hypusination and ameliorates mutant FUS-dependent neuronal defects, including suppression of local protein synthesis. Finally, in vivo spermidine treatment reduces ALS-related toxicity in mutant FUS and TDP-43 Drosophila models, which may have implications for therapy development.
DOI: 10.5281/zenodo.15851242
title: Axonal Eif5a hypusination controls local translation and mitigates defects in FUS-ALS

description: Local protein synthesis is vital for neuronal function, but its dysregulation in neurodegenerative diseases remains poorly defined. Here we applied spatial transcriptomics to adult mouse motor nerve axons and cell bodies to enable subcellular mapping. Among transcripts found in mature axons, the most enriched biological process is protein translation, and localization of translation machinery was confirmed using multiplexed single-molecule spatial transcriptomics combined with immunofluorescence. Amyotrophic lateral sclerosis (ALS)-associated mutations in the RNA-binding protein fused in sarcoma (FUS), which suppress local translation, disrupt the compartment-specific RNA signatures, including components of the translation machinery. In particular, eukaryotic initiation factor 5a (Eif5a), a translation factor involved in elongation and termination, is found to be locally impaired in mutant FUS axons with reduced levels of its active hypusinated form. Axon-specific treatment with polyamine spermidine restores Eif5a hypusination and ameliorates mutant FUS-dependent neuronal defects, including suppression of local protein synthesis. Finally, in vivo spermidine treatment reduces ALS-related toxicity in mutant FUS and TDP-43 Drosophila models, which may have implications for therapy development.
DOI: 10.6084/m9.figshare.25721488
title: Estimated impact and value of blood-based colorectal cancer screening at varied adherence compared with stool-based screening

description: This analysis estimated the outcomes of triennial blood-based colorectal cancer (CRC) screening at various adherence, including perfect adherence, compared with triennial multi-target stool DNA (mt-sDNA) screening at the reported real-world adherence rate. The validated CRC-AIM model simulated a US cohort of average-risk individuals receiving triennial screening with mt-sDNA or blood-based test from ages 45 to 75 years. Modeled specificity and sensitivity were based on reported data. Adherence was set at a real-world rate of 65.6% for mt-sDNA and at 65.6%, relative 10% incremental increases from 65.6%, or 100% for the blood-based test. Costs of mt-sDNA and the blood-based test were based on prices for clinically available tests ($508.87 and $895, respectively). Value-based pricing was estimated at a willingness-to-pay threshold of $100,000. Both tests resulted in life-years gained (LYG), reduced CRC cases, and reduced deaths versus no screening. With adherence for mt-sDNA set at 65.6% and for blood-based test set at 100%, mt-sDNA resulted in 30% more LYG, 52% more averted CRC cases, and 32% more averted CRC deaths. At reported sensitivity and specificity rates, mt-sDNA at 65.6% adherence dominates (is more effective and less costly) the blood-based test at any adherence. There was no price at which triennial screening with the blood-based test at any adherence was cost-effective compared with mt-sDNA at 65.6% adherence. Triennial screening with mt-sDNA resulted in better clinical outcomes at a lower cost compared with the modeled blood-based test even at perfect adherence, supporting application of blood-based tests only as a secondary screening option. Blood-based colorectal cancer screening has lower diagnostic accuracy, lower clinical and health outcomes, and is more expensive than mt-sDNA, even with perfect blood-based screening participation. Although better than no screening at all, blood-based testing is unlikely to exceed performance of stool-based assessment unless a blood-based test is able to meaningfully detect precancerous growths.
DOI: 10.6084/m9.figshare.25592332
title: Exploring kidney dialysis costs in the United States: a scoping review

description: The increasing prevalence of end-stage renal disease (ESRD) in the United States (US) represents a considerable economic burden due to the high cost of dialysis treatment. This review examines data from real-world studies to identify cost drivers and explore areas where dialysis costs could be reduced. We identified and synthesized evidence published from 2016-2023 reporting direct dialysis costs in adult US patients from a comprehensive literature search of MEDLINE, Embase, and grey literature sources (e.g. US Renal Data System reports). Most identified data related to Medicare expenditures. Overall Medicare spending in 2020 was $29B for hemodialysis and $2.8B for peritoneal dialysis (PD). Dialysis costs accounted for almost 80% of total Medicare expenditures on ESRD beneficiaries. Private insurance payers consistently pay more for dialysis; for example, per person per month spending by private insurers on outpatient dialysis was estimated at $10,149 compared with Medicare spending of $3,364. Dialysis costs were higher in specific high-risk patient groups (e.g. type 2 diabetes, hepatitis C). Spending on hemodialysis was higher than on PD, but the gap in spending between PD and hemodialysis is closing. Vascular access costs accounted for a substantial proportion of dialysis costs. Insufficient detail in the identified studies, especially related to outpatient costs, limits opportunities to identify key drivers. Differences between the studies in methods of measuring dialysis costs make generalization of these results difficult. These findings indicate that prevention of or delay in progression to ESRD could have considerable cost savings for Medicare and private payers, particularly in patients with high-risk conditions such as type 2 diabetes. More efficient use of resources is needed, including low-cost medication, to improve clinical outcomes and lower overall costs, especially in high-risk groups. Widening access to PD where it is safe and appropriate may help to reduce dialysis costs. Previous papers have studied the cost of treating patients who need dialysis for kidney failure. We reviewed these costs and looked for patterns. Dialysis was the most expensive part of treatment for people with kidney disease who have Medicare. Dialysis with private insurance was much more expensive than with Medicare. People with diabetes experienced higher costs of dialysis than those without diabetes. Dialysis in a hospital costs more than dialysis at home. There are opportunities to reduce the cost of dialysis that should be explored further, such as more use of low-cost medication that can prevent the worsening of kidney disease and reduce the need for dialysis.
DOI: 10.6084/m9.figshare.24942176.v2
title: Adverse events and costs among non-metastatic castration-resistant prostate cancer patients

description: Limited real-world evidence exists on the economic burden of adverse events (AEs) to the healthcare system among patients with non-metastatic castration-resistant prostate cancer (nmCRPC) treated with second-generation androgen receptor antagonists (ARAs). Current data is needed to understand real-world clinical event rates among ARAs and the cost of these events. Describe the incidence of non-central nervous system (CNS)-related AEs and CNS-related AEs among nmCRPC patients treated in the United States with second-generation ARAs (apalutamide and enzalutamide) and evaluate healthcare resource utilization (HCRU) and costs for these patients. This was a retrospective observational cohort study using claims data from Optum Clinformatics Data Mart to identify adult males with prostate cancer, castration, no metastases, and &gt;1 claim for apalutamide or enzalutamide. The study was conducted from January 2017 to March 2020, with a patient index identification period from January 2018 to December 2019. AEs were classified as CNS-related or non-CNS-related. Of 605 patients (156 apalutamide and 449 enzalutamide), most were ≥65 years (94%) and had ≥1 non-CNS-related AE (55%). Many had ≥1 CNS-related AE (32%). Pain (12%) and arthralgia (11%) were the most frequently reported non-CNS-related AEs. Fatigue/asthenia (14%) and dizziness (7%) were the most frequently reported CNS-related AEs. Among patients with versus without non-CNS-related AEs, 34% versus 8% had emergency room (ER) events, and 25% versus 2% had inpatient events. Among patients with versus without CNS-related AEs, 41% versus 14% had ER events, and 38% versus 4% had inpatient events. Adjusted per-patient per-year cost (in 2020 USD) differences were significant between patients with and without non-CNS-related AEs ($30,765, <i>p</i> = 0.0018) and between patients with and without CNS-related AEs ($40,689, <i>p</i> = 0.0017). There is significant HCRU and cost burden among nmCRPC patients treated with ARAs developing AEs, highlighting the need for treatments with improved tolerability. Additional studies are warranted to include recently approved agents.