cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1385377.10fu
title: SH1385377.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1385379.10fu
title: SH1385379.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1385381.10fu
title: SH1385381.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1385382.10fu
title: SH1385382.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1385385.10fu
title: SH1385385.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1385389.10fu
title: SH1385389.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.3535/jqt-2rs-6tm
title: Nassarius graphiterus (Hombron & Jacquinot, 1848)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/xp9-ldf-as5
title: Subulona E. von Martens, 1889

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/d1d-9ta-zj3
title: Nassarius albescens (Dunker, 1846)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/apl-xx5-6t0
title: Nassarius nodicinctus (A. Adams, 1852)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/tm3-vg8-czg
title: Tritia incrassata (Strøm, 1768)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/kbn-v96-x8g
title: Pseudopeas Putzeys, 1899

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/vwv-ezp-802
title: Nassarius concavus S.-P. Zhang, S.-Q. Zhang & H.-T. Li, 2021

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/0ff-tzz-g7g
title: Nassarius massemini H. H. Kool, 2020

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/rs6-0r5-mvx
title: Nassarius acuminatus (Marrat, 1880)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/g79-63d-a2y
title: Pyrgiscus rufus (R. A. Philippi, 1836)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/2cf-7v2-bdd
title: Nassarius bimaculosus (A. Adams, 1852)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/p53-ldg-8he
title: Nassarius horridus (Dunker, 1847)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ryl-1zp-snf
title: Bullia sendersi Kilburn, 1978

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/xsf-1cj-25q
title: Nassarius obvelatus (Deshayes, 1833)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/a2a-xdb-svd
title: Nassarius vitiensis (Hombron & Jacquinot, 1848)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/hc5-5ef-vs5
title: Nassarius lochi H. H. Kool, 1996

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/1wx-tzz-svk
title: Nassarius fenistratus (Marrat, 1877)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/t8a-cj5-c08
title: Nassarius rainbowae Gili, 2015

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/y6p-mxy-4x5
title: Phrontis antillarum (A. d'Orbigny, 1847)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qly-z4w-h5k
title: Bullia pura Melvill, 1885

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/sml-8wz-avc
title: Turbonilla acutissima Monterosato, 1884

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/bm2-535-g29
title: Phrontis antillarum (A. d'Orbigny, 1847)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/jdx-061-2sd
title: Nassarius nakayamai (Habe, 1958)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/7e4-vdg-fyr
title: Phrontis complanata (Powys, 1835)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/a4r-85j-dsk
title: Phrontis hotessieriana (A. d'Orbigny, 1842)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/rh1-8wg-gsd
title: Nassarius Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3tj-jct-wqt
title: Nassarius labordei (Giner Mari, 1929)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/9c8-by5-4d8
title: Nassarius humboldti Gili & H. H. Kool, 2022

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/76m-dnp-y18
title: Cyllene owenii J. E. Gray, 1834

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/f2p-fhw-s7b
title: Nassarius obvelatus (Deshayes, 1833)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/cgb-cx5-5q2
title: Nassarius Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/jzd-0vw-bl5
title: Nassarius Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3g7-d5s-ddx
title: Nassarius conoidalis (Deshayes, 1833)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/n3v-dm7-nyl
title: Nassarius Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/13a-cdx-5e7
title: Bullia diluta (Krauss, 1848)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/g6h-z8q-l0z
title: Phrontis versicolor (C. B. Adams, 1852)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qz0-2eq-kem
title: Demoulia nataliae Kilburn, 1972

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/c4y-6t0-274
title: Nassarius Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/yvt-qe1-3qa
title: Nassarius dekkeri H. H. Kool, 2001

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.31359985
title: FDA 510(k) clearances for chronic pain devices: regulatory trends and safety outcomes from 1976–2025

description: We characterized regulatory and safety patterns of FDA 510(k)-cleared chronic pain devices over five decades. We analyzed 1718 chronic pain devices cleared from 1976–2025, assessing device modality, review times, recalls (2002–2025), and market concentration (Herfindahl – Hirschman Index, HHI). Most devices were noninvasive neuromodulation (1,486 [86.5%]), followed by invasive neuromodulation (162 [9.4%]), energy-based (67 [3.9%]), and digital therapeutics (3 [0.2%]). Median FDA review was 117 days (IQR, 69–220) – shorter for invasive (90 days; IQR, 42–156) than noninvasive neuromodulation (120 days; IQR, 70–222). US-origin devices declined from 97.6% (1976–1985) to 43.1% (2016–2025). Invasive devices declined 45% from peak (12.3%, 1996–2005) to 6.7% (2016–2025), while digital therapeutics emerged post-2016. Among 843 devices evaluated for safety, invasive devices had 14-fold higher recall rates than noninvasive (100 vs. 7.1 per 1000); median time to recall was 3.9 years (IQR, 2.9–4.7). Market concentration differed markedly: invasive devices highly concentrated (HHI, 3710; single manufacturer 58%) versus fragmented noninvasive markets (HHI, 43). Despite shorter regulatory review, invasive chronic pain devices demonstrated substantially higher recall rates. The shift toward globally sourced noninvasive technologies reflects evolving regulatory-technological considerations.
DOI: 10.6084/m9.figshare.31359985.v2
title: FDA 510(k) clearances for chronic pain devices: regulatory trends and safety outcomes from 1976–2025

description: We characterized regulatory and safety patterns of FDA 510(k)-cleared chronic pain devices over five decades. We analyzed 1718 chronic pain devices cleared from 1976–2025, assessing device modality, review times, recalls (2002–2025), and market concentration (Herfindahl – Hirschman Index, HHI). Most devices were noninvasive neuromodulation (1,486 [86.5%]), followed by invasive neuromodulation (162 [9.4%]), energy-based (67 [3.9%]), and digital therapeutics (3 [0.2%]). Median FDA review was 117 days (IQR, 69–220) – shorter for invasive (90 days; IQR, 42–156) than noninvasive neuromodulation (120 days; IQR, 70–222). US-origin devices declined from 97.6% (1976–1985) to 43.1% (2016–2025). Invasive devices declined 45% from peak (12.3%, 1996–2005) to 6.7% (2016–2025), while digital therapeutics emerged post-2016. Among 843 devices evaluated for safety, invasive devices had 14-fold higher recall rates than noninvasive (100 vs. 7.1 per 1000); median time to recall was 3.9 years (IQR, 2.9–4.7). Market concentration differed markedly: invasive devices highly concentrated (HHI, 3710; single manufacturer 58%) versus fragmented noninvasive markets (HHI, 43). Despite shorter regulatory review, invasive chronic pain devices demonstrated substantially higher recall rates. The shift toward globally sourced noninvasive technologies reflects evolving regulatory-technological considerations.
DOI: 10.3535/rxp-4r6-7jq
title: Lindholmiola corcyrensis (Rossmässler, 1838)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/z1g-knm-qsp
title: Vitrea neglecta Damjanov & L. Pintér, 1969

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.31359985.v1
title: FDA 510(k) clearances for chronic pain devices: regulatory trends and safety outcomes from 1976-2025

description: We characterized regulatory and safety patterns of FDA 510(k)-cleared chronic pain devices over five decades. We analyzed 1718 chronic pain devices cleared from 1976–2025, assessing device modality, review times, recalls (2002–2025), and market concentration (Herfindahl – Hirschman Index, HHI). Most devices were noninvasive neuromodulation (1,486 [86.5%]), followed by invasive neuromodulation (162 [9.4%]), energy-based (67 [3.9%]), and digital therapeutics (3 [0.2%]). Median FDA review was 117 days (IQR, 69–220) – shorter for invasive (90 days; IQR, 42–156) than noninvasive neuromodulation (120 days; IQR, 70–222). US-origin devices declined from 97.6% (1976–1985) to 43.1% (2016–2025). Invasive devices declined 45% from peak (12.3%, 1996–2005) to 6.7% (2016–2025), while digital therapeutics emerged post-2016. Among 843 devices evaluated for safety, invasive devices had 14-fold higher recall rates than noninvasive (100 vs. 7.1 per 1000); median time to recall was 3.9 years (IQR, 2.9–4.7). Market concentration differed markedly: invasive devices highly concentrated (HHI, 3710; single manufacturer 58%) versus fragmented noninvasive markets (HHI, 43). Despite shorter regulatory review, invasive chronic pain devices demonstrated substantially higher recall rates. The shift toward globally sourced noninvasive technologies reflects evolving regulatory-technological considerations.
DOI: 10.15468/dl.w36hu7
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "CoordinateUncertaintyInMeters is less than or equal to 4969.0",
 "GbifRegion is Africa",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Delphinium dasycaulon"
 ]
}

 The dataset includes 14 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031939-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/m67-yen-ad4
title: Pyrgiscus rufus (R. A. Philippi, 1836)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.kzuc59
title: Occurrence Download

description: A dataset containing 600 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((6.13203 49.30387,6.13041 49.30017,6.12416 49.29843,6.12416 49.29554,6.12682 49.29311,6.12913 49.29322,6.1333 49.29079,6.14488 49.29299,6.15124 49.29785,6.15437 49.29426,6.15807 49.29519,6.15761 49.29681,6.16004 49.29855,6.16374 49.29924,6.16571 49.30098,6.16166 49.30688,6.16212 49.30989,6.1686 49.31313,6.17254 49.31545,6.17347 49.31834,6.17034 49.32112,6.16363 49.32285,6.16085 49.32239,6.15934 49.32409,6.15963 49.32817,6.15306 49.33036,6.13527 49.33371,6.13498 49.33954,6.12929 49.33881,6.11498 49.34821,6.11162 49.34836,6.10885 49.35127,6.10316 49.35011,6.09791 49.35346,6.08989 49.35244,6.0896 49.34894,6.0915 49.34705,6.09004 49.34588,6.09193 49.34296,6.08843 49.34034,6.09266 49.33844,6.09208 49.33552,6.08566 49.33363,6.08654 49.3294,6.09223 49.32809,6.09106 49.33319,6.10331 49.33407,6.10461 49.32986,6.10573 49.32857,6.108 49.3288,6.11031 49.32465,6.10521 49.32352,6.10328 49.31974,6.10568 49.31413,6.11345 49.31602,6.11427 49.31445,6.1169 49.31505,6.11966 49.31382,6.12215 49.31273,6.12515 49.31251,6.12749 49.30576,6.12999 49.30585,6.13017 49.30414,6.13203 49.30387))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year 2021-2026"
 ]
}

 The dataset includes 600 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031940-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/47g-7ye-sg3
title: Varicospira crispata (G. B. Sowerby II, 1842)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.31376593
title: Experimental assessment and hybrid machine learning prediction and optimization of compressive strength of steel fiber reinforced demolition waste concrete

description: With the rapid growth of artificial intelligence, machine learning has emerged as a useful tool in the construction industry for optimizing materials and predicting concrete properties. This study explores the use of steel fiber and recycled demolition waste (DW) to improve concrete’s mechanical properties. The main objective is to predict the compressive strength (CS) of DW-modified fiber-reinforced concrete (FRC) by assessing the effects of cement, RHA, fiber content, natural and demolished waste aggregates, water, and superplasticizer dosages. Seven mix designs with varying fiber and DW levels were experimentally tested. Machine learning methods, including adaptive boosting (ADB), extreme gradient boosting (XGB), random forest (RF), and stacking models (XGB-ADB, XGB-RF), were applied to analyze these variables’ impact on CS. A dataset of 405 points was compiled from literature via a systematic review. The hybrid XGB-RF and XGB models showed the best performance with R<sup>2</sup> values of 0.849 and 0.845, respectively. SHAP analysis identified cement, water, and superplasticizer as key factors affecting CS. Experimental validation supported the modeling results and the development of a graphical user interface. The novelty lies in integrating hybrid ML, explainable analysis, and experimental validation to predict the CS of the modified concrete and support mix design through a user-friendly GUI.
DOI: 10.6084/m9.figshare.31376593.v1
title: Experimental assessment and hybrid machine learning prediction and optimization of compressive strength of steel fiber reinforced demolition waste concrete

description: With the rapid growth of artificial intelligence, machine learning has emerged as a useful tool in the construction industry for optimizing materials and predicting concrete properties. This study explores the use of steel fiber and recycled demolition waste (DW) to improve concrete’s mechanical properties. The main objective is to predict the compressive strength (CS) of DW-modified fiber-reinforced concrete (FRC) by assessing the effects of cement, RHA, fiber content, natural and demolished waste aggregates, water, and superplasticizer dosages. Seven mix designs with varying fiber and DW levels were experimentally tested. Machine learning methods, including adaptive boosting (ADB), extreme gradient boosting (XGB), random forest (RF), and stacking models (XGB-ADB, XGB-RF), were applied to analyze these variables’ impact on CS. A dataset of 405 points was compiled from literature via a systematic review. The hybrid XGB-RF and XGB models showed the best performance with R<sup>2</sup> values of 0.849 and 0.845, respectively. SHAP analysis identified cement, water, and superplasticizer as key factors affecting CS. Experimental validation supported the modeling results and the development of a graphical user interface. The novelty lies in integrating hybrid ML, explainable analysis, and experimental validation to predict the CS of the modified concrete and support mix design through a user-friendly GUI.