cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57761/q7vy-5w24
title: Cotality Smart Data Platform: Pre-Foreclosure

description: If you have questions or concerns, please contact Regina Roberts at regirob@stanford.edu.
description: Title: CoreLogic Smart Data Platform (SDP) Pre-Foreclosure 

The CoreLogic Pre-Foreclosure data documents about 35 million property transactions representing pre-foreclosure events. These transactions occurred in U.S. states (excluding Vermont), the U.S. Virgin Islands and Washington, D.C. CoreLogic has been collecting pre-foreclosure data since 2000.

Transaction events include Notice of Default, Lis Pendens, Release of Lis Pendens and Final Judgment. Transactions illustrate the pre-foreclosure events leading up to a foreclosure or sale at auction. Transaction data can include property address, default date, default amount, document type (Notice of Default, Lis Pendens, etc.), court filing details, attorney, beneficiary or plaintiff name, borrower name, lender, trustee, final judgment amount and any relevant auction information. Transactions also include a subject transaction, which identifies the original transaction (usually Deed of Trust or another prior activity) to which a transaction applies. Activities recorded and delivered support transactions within both judicial and non-judicial states.

The CoreLogic Smart Data Platform (SDP) Pre-Foreclosure data was formerly known as the CoreLogic Pre-Foreclosure data. The CoreLogic SDP Property data contains almost all of the variables that were included in the CoreLogic Pre-Foreclosure data.
description: Pre-foreclosure data comes from four types of documents:



- Final Judgment of Foreclosure
- Lis Pendens
- Notices of Default
- Release of Lis Pendens

<!-- -->



These documents are sourced from U.S. County Assessor and Recorder offices, and newspapers. The data is collected, cleaned and normalized by CoreLogic. Data is bundled together in a pipe-delimited text file, which has been uploaded to Redivis for preview, extraction and light analysis.


description: 

The Property, Mortgage, Owner Transfer, Historical Property and Pre-Foreclosure data can be linked on the CLIP, a unique identification number assigned to each property.



For more information about included variables, please see ***Core\_Logic\_SDP\_Pre\_Foreclosure\_Codebook.xlsx*** (under **Supporting files**).





For a count of records per FIPS code, please see ***pre\_foreclosure\_counts.txt ***(under **Supporting files**).



For more information about how the CoreLogic Smart Data Platform: Pre-Foreclosure data compares to legacy data, please see ***Legacy\_Content\_Mapping.pdf ***(under **Supporting files**).





For more information about the terms of use, please see

***2022\_corelogic\_sdp\_end\_user\_license\_agreement.pdf*** (under **Supporting files**).
DOI: 10.15468/dl.xjczhw
title: Occurrence Download

description: A dataset containing 1517 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tangara heinei)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 1517 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030080-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16782728
title: Evaluation of various traditional machine learning techniques for predicting the effect of different hamstring muscle stretching methods among male soccer players
DOI: 10.15468/dl.f35yy7
title: Occurrence Download

description: A dataset containing 703492 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'CO'
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 703492 records were derived from 4016 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030050-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wtb4je
title: Occurrence Download

description: A dataset containing 173406 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Year 2024-2025"
 ]
}

 The dataset includes 173406 records from 189 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011414-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18235369
title: CAFE Framework - Biosignal Data for Elderly Monitoring

description: Preprocessed biosignal data (ECG, EOG) used in the CAFE framework study for bandwidth-efficient elderly care monitoring. This dataset accompanies the paper "Compact Adaptive Feature Extraction (CAFE): A Bandwidth-Aware Multi-Domain Monitoring Framework for Elderly Care" published in F1000Research.
DOI: 10.5281/zenodo.18010062
title: Decoding the Glycomic Code: Beyond Genetic Centrality in Chemical Glycobiology Integrated with Bio-Inorganic Chemistry

description: This conceptual framework challenges the entrenched DNA-centric paradigm in molecular biology by positioning glycans as pivotal dynamic regulators of post-translational protein function, akin to a hardware-software duality in computational systems. By seamlessly integrating chemical glycobiology with bio-inorganic chemistry, this work elucidates the indispensable role of trace elements as cofactors in glycosylation enzymes. Through rigorous mathematical derivations, sophisticated probabilistic modeling, advanced Python-based simulations leveraging biochemical data, comprehensive sensitivity analyses (including detailed Sobol indices with variance decomposition and Hilbert-Schmidt indices), Bayesian inference, uncertainty quantification, and explicit falsifiability criteria, we dissect the nonlinear dynamics of glycan structures and their profound implications in immunology, oncology, neurology, and allied fields. Furthermore, we incorporate cutting-edge applications of artificial intelligence in genomics and glycomics, such as machine learning for glycosylation site prediction, deep learning for structural modeling, variant analysis, and drug discovery. Bolstered by evidence from meticulously verified peer-reviewed sources, this manuscript delineates a falsifiable strategy for re-engineering cellular glyco-envelopes for therapeutic interventions. It addresses inherent limitations, stringent validation standards, methodological enhancements, potential challenges, and falsifiability, while furnishing supplementary materials for profound insights into computational models, statistical analyses, and AI integrations.Keywords: Glycobiology, Bio-inorganic Chemistry, Glycosylation, Artificial Intelligence, Sensitivity Analysis, Therapeutic Engineering
DOI: 10.5281/zenodo.18235607
title: Decoding the Glycomic Code: Beyond Genetic Centrality in Chemical Glycobiology Integrated with Bio-Inorganic Chemistry

description: This conceptual framework challenges the entrenched DNA-centric paradigm in molecular biology by positioning glycans as pivotal dynamic regulators of post-translational protein function, akin to a hardware-software duality in computational systems. By seamlessly integrating chemical glycobiology with bio-inorganic chemistry, this work elucidates the indispensable role of trace elements as cofactors in glycosylation enzymes. Through rigorous mathematical derivations, sophisticated probabilistic modeling, advanced Python-based simulations leveraging biochemical data, comprehensive sensitivity analyses (including detailed Sobol indices with variance decomposition and Hilbert-Schmidt indices), Bayesian inference, uncertainty quantification, and explicit falsifiability criteria, we dissect the nonlinear dynamics of glycan structures and their profound implications in immunology, oncology, neurology, and allied fields. Furthermore, we incorporate cutting-edge applications of artificial intelligence in genomics and glycomics, such as machine learning for glycosylation site prediction, deep learning for structural modeling, variant analysis, and drug discovery. Bolstered by evidence from meticulously verified peer-reviewed sources, this manuscript delineates a falsifiable strategy for re-engineering cellular glyco-envelopes for therapeutic interventions. It addresses inherent limitations, stringent validation standards, methodological enhancements, potential challenges, and falsifiability, while furnishing supplementary materials for profound insights into computational models, statistical analyses, and AI integrations.Keywords: Glycobiology, Bio-inorganic Chemistry, Glycosylation, Artificial Intelligence, Sensitivity Analysis, Therapeutic Engineering
DOI: 10.5281/zenodo.14864967
title: Catálogo de soluciones de adaptación al cambio climático basadas en la naturaleza.

description: El proyecto “Sistematización de soluciones basadas en naturaleza en España" (E:SBN) llevó a cabo una amplia identificación y recopilación de soluciones basadas en la naturaleza en España, como referente de buenas prácticas para la adaptación adecuada al cambio climático en cada territorio. El proyecto cuenta con el apoyo de la Fundación Biodiversidad del Ministerio para la Transición Ecológica y el Reto Demográfico, a través de la Convocatoria de subvenciones para la realización de proyectos que contribuyan a implementar el Plan Nacional de Adaptación al Cambio Climático 2021-2030 (PNACC).
DOI: 10.5281/zenodo.14864966
title: Catálogo de soluciones de adaptación al cambio climático basadas en la naturaleza.

description: El proyecto “Sistematización de soluciones basadas en naturaleza en España" (E:SBN) llevó a cabo una amplia identificación y recopilación de soluciones basadas en la naturaleza en España, como referente de buenas prácticas para la adaptación adecuada al cambio climático en cada territorio. El proyecto cuenta con el apoyo de la Fundación Biodiversidad del Ministerio para la Transición Ecológica y el Reto Demográfico, a través de la Convocatoria de subvenciones para la realización de proyectos que contribuyan a implementar el Plan Nacional de Adaptación al Cambio Climático 2021-2030 (PNACC).
DOI: 10.15468/dl.qd3rgy
title: Occurrence Download

description: A dataset containing 7465 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oenothera villosa Thunb."
 ]
}

 The dataset includes 7465 records from 295 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004877-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11682918
title: e-IPO Indonesia Data - Investment
DOI: 10.18167/dvn1/8c9ztn
title: Novelty dataset (animal health, food security, climate change)

description: This dataset contains sets of news article segments in English related to three domains namely animal health, food security and climate change and has been used to fine-tune and evaluate GPT3.5-turbo, GPT4o, DeepSeek-V3, DeBERTa, RoBERTa, BERT, EpidBioELECTRA and EpidGPT models for novelty detection tasks in the three domains. It is composed of 10,660 animal disease, 1,100 food security and 2,200 climate change article segments in csv format with information about the parent articles (segment, doc id, seg id, title, source url, publication date, article domain, article subdomain). Animal health domain is made up of 22 subdomains inclusive of article segments on Avian Influenza (AI) 2310, Highly Pathogenic Avian Influenza (HPAI) 3060, African Swine Fever (ASF) 1000, Foot-and-Mouth disease (FMD) 1165, Bovine Spongiform Encephalopathy (BSE) 770, Brucellosis 435, Peste des Petits Ruminants (PPR) 160, Bluetongue 165, Newcastle disease 155, Glanders 160, Disease X 140, Anthrax 120, West Nile Virus (WNV) 145, Middle East respiratory syndrome (MERS) 215, Infectious Salmon Anaemia (ISA) 110, Equine Influenza (EI) 200, Eastern Equine Encephalitis (EEE) 50, Porcine Reproductive and Respiratory Syndrome (PRRS) 85, Rift valley Fever (RVF) 30, Classical Swine Fever (CSF) 40, Rabies 30, Venezuelan Equine Encephalitis (VEE) 55, Viral Haemorrhagic Septicaemia (VHS) 15. Climate change domain is made up of 7 subdomains inclusive of article segments on flash floods 340, drought 394, wildfires 184, hurricanes 349, heatwaves 385, global warming 299 and tsunamis 340. The original articles dataset (corpus) contains documents from which created segments (drawn from original PADI-Web articles (relevant articles only) and those gotten from GDELT database (for news articles on food security events and climate change events)).
DOI: 10.15468/dl.8rkmph
title: Occurrence Download

description: A dataset containing 510 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "OccurrenceStatus is Present",
 "TaxonKey is Solenopsis geminata (Fabricius, 1804)"
 ]
}

 The dataset includes 510 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015729-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15341043
title: An engineered E. coli Nissle 1917 platform for live bacterial therapeutics
DOI: 10.5281/zenodo.15341044
title: An engineered E. coli Nissle 1917 platform for live bacterial therapeutics
DOI: 10.5281/zenodo.11970707
title: Microbiological agents as notified to EFSA

description: This is the list of “Microbiological agents as notified to EFSA” from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment.

This is supplementing the QPS statements part 11, part 12, part 13, part 14, part 15, part 16, part 17, part 18, part 19 and the 2022 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers.

EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; xlsx; biohaz@efsa.europa.eu
DOI: 10.5281/zenodo.14697175
title: Microbiological agents as notified to EFSA

description: This is the list of “Microbiological agents as notified to EFSA” from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment.

This is supplementing the QPS statements part 11, part 12, part 13, part 14, part 15, part 16, part 17, part 18, part 19, part 20, part 21 and the 2022 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers.

EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; xlsx; biohaz@efsa.europa.eu
DOI: 10.5281/zenodo.3607184
title: Microbial species as notified to EFSA

description: This is the list of “<strong>Microbi</strong><strong>ological agents </strong><strong>as notified to EFSA</strong><strong>”</strong> from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment. This is supplementing the QPS statement part 11 and the 2019 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers. EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; PDF; biohaz@efsa.europa.eu
DOI: 10.5281/zenodo.4498899
title: Microbial species as notified to EFSA

description: This is the list of “<strong>Microbi</strong><strong>ological agents </strong><strong>as notified to EFSA</strong><strong>”</strong> from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment. This is supplementing the QPS statement part 11, part 12 and the 2019 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers. EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; PDF; biohaz@efsa.europa.eu
DOI: 10.5281/zenodo.4498901
title: Microbial species as notified to EFSA

description: This is the list of “<strong>Microbi</strong><strong>ological agents </strong><strong>as notified to EFSA</strong><strong>”</strong> from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment. This is supplementing the QPS statement part 11, part 12, part 13 and the 2019 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers. EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; PDF; biohaz@efsa.europa.eu
DOI: 10.5281/zenodo.4917414
title: Microbial species as notified to EFSA

description: This is the list of “<strong>Microbi</strong><strong>ological agents </strong><strong>as notified to EFSA</strong><strong>”</strong> from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment. This is supplementing the QPS statement part 11, part 12, part 13, part 14 and the 2019 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers. EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; xlsx; biohaz@efsa.europa.eu
DOI: 10.5281/zenodo.5897793
title: Microbial species as notified to EFSA

description: This is the list of “<strong>Microbi</strong><strong>ological agents </strong><strong>as notified to EFSA</strong><strong>”</strong> from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment. This is supplementing the QPS statement part 11, part 12, part 13, part 14, part 15 and the 2019 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers. EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; xlsx; biohaz@efsa.europa.eu
DOI: 10.5281/zenodo.6854364
title: Microbiological agents as notified to EFSA

description: This is the list of “<strong>Microbi</strong><strong>ological agents </strong><strong>as notified to EFSA</strong><strong>”</strong> from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment. This is supplementing the QPS statements part 11, part 12, part 13, part 14, part 15, part 16 and the 2019 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers. EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; xlsx; biohaz@efsa.europa.eu
DOI: 10.5281/zenodo.7554134
title: Microbiological agents as notified to EFSA

description: This is the list of “<strong>Microbi</strong><strong>ological agents </strong><strong>as notified to EFSA</strong><strong>”</strong> from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment. This is supplementing the QPS statements part 11, part 12, part 13, part 14, part 15, part 16, part 17 and the 2022 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers. EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; xlsx; biohaz@efsa.europa.eu
DOI: 10.5281/zenodo.8124416
title: Microbiological agents as notified to EFSA

description: This is the list of “<strong>Microbi</strong><strong>ological agents </strong><strong>as notified to EFSA</strong><strong>”</strong> from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment. This is supplementing the QPS statements part 11, part 12, part 13, part 14, part 15, part 16, part 17, part 18 and the 2022 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers. EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; xlsx; biohaz@efsa.europa.eu
DOI: 10.5281/zenodo.10477679
title: Microbiological agents as notified to EFSA

description: This is the list of “Microbiological agents as notified to EFSA” from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment.

This is supplementing the QPS statements part 11, part 12, part 13, part 14, part 15, part 16, part 17, part 18, part 19 and the 2022 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers.

EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; xlsx; biohaz@efsa.europa.eu
DOI: 10.5281/zenodo.12793271
title: Microbiological agents as notified to EFSA

description: This is the list of “Microbiological agents as notified to EFSA” from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment.

This is supplementing the QPS statements part 11, part 12, part 13, part 14, part 15, part 16, part 17, part 18, part 19, part 20 and the 2022 Scientific Opinion of the BIOHAZ Panel of EFSA - see related identifiers.

EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps
description: EU; xlsx; biohaz@efsa.europa.eu