cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17544113
title: KneechatData:Conversational Dataset from Surgical Patient Interviews

description: This dataset contains transcribed and annotated excerpts from 41 semi-structured preoperative interviews with patients on the waiting list for total knee arthroplasty (TKA). The corpus comprises 635 utterances (segments), annotated with utterance type, thematic categories and an valence score. 

Origin and data collectionInterviews were conducted by trained research staff at Hospital Costa del Sol (Málaga, Spain). Conversations were recorded, transcribed verbatim, and segmented into utterances. Of the 635 segments, 226 were labeled as Spontaneous Comment and 55 as Explicit Question; the remainder were marked Clinically Irrelevant.

Processing and annotations





Thematic annotations were produced via a hybrid pipeline: automated phrase clustering and zero-shot classification followed by manual review by clinical researchers.




Sentiment scores were computed automatically for each utterance using the Spanish-language BERT model pysentimiento/robertuito (Hugging Face). Valence is provided as a continuous score in the range [−1,+1][-1, +1][−1,+1] and as a discretized category (Very Negative / Negative / Neutral / Positive / Very Positive).



Variables (main columns)





Patient ID — pseudonymized patient identifier (e.g., P_1).




Utterance (Spanish) — transcribed text of the segment in Spanish.




Utterance Type — Spontaneous Comment / Explicit Question / Clinically Irrelevant.




Categories "Explicit Question" — thematic label(s) for questions (e.g., Logistics & waiting times, Recovery process).




Categories "Spontaneous Comment" — thematic label(s) for comments (e.g., Pain & complications, Daily routine, Fear / worry / anxiety).




Valence Score — continuous saentiment valence (float, −1.0 to +1.0).




Valence Category — discretized valence label derived from Valence Score.



Valence computationValence = P(positive) −P(negative), where probabilities are produced by the RoBERTuito model for each utterance. Higher values indicate more positive affect; lower values indicate more negative affect.

Ethics and privacyAll data have been pseudonymized: direct identifiers (names, addresses, phone numbers, etc.) were removed prior to release. The original study received ethics approval (Protocol No. 003_feb24) and all participants provided written informed consent for the use of de-identified data in research. Although anonymized, interview text may contain sensitive information; users should handle the data responsibly and comply with applicable data protection rules.

Contact & citationFor questions, contact: mzapaher@gmail.com Please cite the dataset and the related manuscript when using these data.
DOI: 10.5281/zenodo.17544112
title: KneechatData:Conversational Dataset from Surgical Patient Interviews

description: This dataset contains transcribed and annotated excerpts from 41 semi-structured preoperative interviews with patients on the waiting list for total knee arthroplasty (TKA). The corpus comprises 635 utterances (segments), annotated with utterance type, thematic categories and an valence score. 

Origin and data collectionInterviews were conducted by trained research staff at Hospital Costa del Sol (Málaga, Spain). Conversations were recorded, transcribed verbatim, and segmented into utterances. Of the 635 segments, 226 were labeled as Spontaneous Comment and 55 as Explicit Question; the remainder were marked Clinically Irrelevant.

Processing and annotations





Thematic annotations were produced via a hybrid pipeline: automated phrase clustering and zero-shot classification followed by manual review by clinical researchers.




Sentiment scores were computed automatically for each utterance using the Spanish-language BERT model pysentimiento/robertuito (Hugging Face). Valence is provided as a continuous score in the range [−1,+1][-1, +1][−1,+1] and as a discretized category (Very Negative / Negative / Neutral / Positive / Very Positive).



Variables (main columns)





Patient ID — pseudonymized patient identifier (e.g., P_1).




Utterance (Spanish) — transcribed text of the segment in Spanish.




Utterance Type — Spontaneous Comment / Explicit Question / Clinically Irrelevant.




Categories "Explicit Question" — thematic label(s) for questions (e.g., Logistics & waiting times, Recovery process).




Categories "Spontaneous Comment" — thematic label(s) for comments (e.g., Pain & complications, Daily routine, Fear / worry / anxiety).




Valence Score — continuous saentiment valence (float, −1.0 to +1.0).




Valence Category — discretized valence label derived from Valence Score.



Valence computationValence = P(positive) −P(negative), where probabilities are produced by the RoBERTuito model for each utterance. Higher values indicate more positive affect; lower values indicate more negative affect.

Ethics and privacyAll data have been pseudonymized: direct identifiers (names, addresses, phone numbers, etc.) were removed prior to release. The original study received ethics approval (Protocol No. 003_feb24) and all participants provided written informed consent for the use of de-identified data in research. Although anonymized, interview text may contain sensitive information; users should handle the data responsibly and comply with applicable data protection rules.

Contact & citationFor questions, contact: mzapaher@gmail.com Please cite the dataset and the related manuscript when using these data.
DOI: 10.7910/dvn/4jparc
title: Replication Data for: Evolution of a fuzzy ribonucleoprotein complex in viral assembly

description: Previously we showed that the genetic diversity of SARS-CoV-2 nucleocapsid (N) protein explores a wide range of biophysical properties facilitated by non-local impact of point mutations to its intrinsically disordered regions (IDRs) (Nguyen et al., 2024). This includes modulation of self-association, such as the creation of a de novo binding interface through the P13L mutation characteristic of Omicron variants. In the present work we focus on the key function of N condensing viral RNA into ribonucleoprotein particles (RNPs) for viral assembly. Lacking high-resolution structural information, biochemical and biophysical approaches have revealed architectural principles of RNPs, which involve cooperative interactions of several protein-protein and protein-RNA interfaces, initiated through oligomerization of conserved transient helices in the central disordered linker of N. Here we study the impact of defining N-protein mutations in variants of concern on RNP formation, using biophysical tools, a virus-like particle assay, and reverse genetics experiments. We find convergent evolution in repeated, independent introduction of amino acid substitutions strengthening existing binding interfaces, compensating for other substitutions that promote viral replication but decrease RNP stability. Furthermore, we show P13L of Omicron enhances RNP assembly and increases viral fitness. Overall, our data reveal RNP complexes to be highly variable not only in sequence and conformations, but also in thermodynamic and kinetic stability, with its pleomorphism affecting basic architectural principles. We hypothesize that the formation of polydisperse, fuzzy N-RNA clusters with multiple distributed weak binding interfaces optimizes reversible RNA condensation, while supporting host adaptation and allowing for a large sequence space to be explored.
DOI: 10.25574/26948
title: Chandra X-ray Observatory ObsId 26948

description: Resolving an orphan cloud as a signpost of ICM clumping
DOI: 10.15468/dl.6j6yjc
title: Occurrence Download

description: A dataset containing 24 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
 GBIF_WITHIN('POLYGON((-1.9225675720643807 52.990450040360834,-0.891243570761663 52.990450040360834,-0.891243570761663 54.021774041663555,-1.9225675720643807 54.021774041663555,-1.9225675720643807 52.990450040360834))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (CAST(occurrence.taxonkey AS INTEGER) = 2440958 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 2440958 OR CAST(occurrence.kingdomkey AS INTEGER) = 2440958 OR CAST(occurrence.phylumkey AS INTEGER) = 2440958 OR CAST(occurrence.classkey AS INTEGER) = 2440958 OR CAST(occurrence.orderkey AS INTEGER) = 2440958 OR CAST(occurrence.familykey AS INTEGER) = 2440958 OR CAST(occurrence.genuskey AS INTEGER) = 2440958 OR CAST(occurrence.specieskey AS INTEGER) = 2440958)
 AND (occurrence."year" &gt;= 2001 AND occurrence."year" &lt;= 2025)
 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.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 24 records were derived from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020040-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15472/qoblkv
title: Colección de Microorganismos Universidad Católica de Manizales

description: La Colección de Microorganismos de la Universidad Católica de Manizales – CMUCM, tiene como objetivo salvaguardar el recurso microbiológico aislado de muestras ambientales, de industria, del sector agrícola y muestras clínicas hasta agentes biológicos de nivel 2, provenientes de las investigaciones llevadas a cabo en la UCM, de los actores académicos de la región y las empresas que así lo requieran. Convirtiéndose en fuente de investigación primaria para la comunidad científica nacional e internacional en apoyo de la investigación, difusión y formación de los recursos humanos.El presente conjunto de datos contiene 15 registros biológicos (RR.BB) provenientes de los departamentos de Caldas y Valle del Cauca, recolectados durante los periodos de octubre y noviembre del 2021 y marzo y julio de 2023. Los RR.BB pertenecen a los reinos Fungi y Bacteria. El filo Ascomycota se encuentra dividido en tres clases: Dothideomycetes, Eurotiomycetes y Sordariomycetes. Los filos Proteobacteria y Pseudomonadota divididos en las clases Gammaproteobacteria y Betaproteobacterium. Un 33.34% está clasificado a nivel de especie y un 66.66% a género.
DOI: 10.25574/29396
title: Chandra X-ray Observatory ObsId 29396

description: Detecting the X-ray Emission of the Black Hole Gaia BH3
DOI: 10.25574/29414
title: Chandra X-ray Observatory ObsId 29414

description: C-BASS: A Chandra Legacy Survey of AGN at the Highest Spatial Resolutions
DOI: 10.25574/29416
title: Chandra X-ray Observatory ObsId 29416

description: Proper motion of central compact object in SNR RCW 103
DOI: 10.25574/29417
title: Chandra X-ray Observatory ObsId 29417

description: A Galactic SNR and its Quiescent Black Hole
DOI: 10.25574/29421
title: Chandra X-ray Observatory ObsId 29421

description: Stellar activity with TESS and Chandra
DOI: 10.15468/dl.e7w3m5
title: Occurrence Download

description: A dataset containing 52311 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Paulownia tomentosa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 52311 records from 424 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020042-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xtcdzc
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Echinopsis chrysantha"
 ]
}

 The dataset includes 16 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020079-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6kefr5
title: Occurrence Download

description: A dataset containing 846015 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hemichromis elongatus, Butis koilomatodon, Lepomis gibbosus, Hypsophrys nicaraguensis, Oreochromis spilurus, Symphysodon discus, Syngnathus abaster, Sarotherodon occidentalis, Coptodon rendalli, Poecilia sphenops, Synodontis eupterus, Bidyanus bidyanus, Omobranchus ferox, Prochilodus lineatus, Alburnus alburnus, Poecilia reticulata, Australoheros facetus, Trichopodus leerii, Clarias macrocephalus, Acipenser nudiventris, Macrognathus siamensis, Perca flavescens, Oncorhynchus gorbuscha, Monopterus albus, Perccottus glenii, Alosa sapidissima, Cichla monoculus, Paracheirodon innesi, Chelon auratus, Barbonymus schwanefeldii)",
 "HasCoordinate is true",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 846015 records from 1021 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020047-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3255ex
title: Occurrence Download

description: A dataset containing 539 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Parahyaena brunnea"
 ]
}

 The dataset includes 539 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020081-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ych4e6
title: Occurrence Download

description: A dataset containing 3968 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Antarctic Jellyfish"
 ]
}

 The dataset includes 3968 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020083-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18751/climate/scenarios/ch2025/data/1.0/
title: Climate CH2025 datasets
DOI: 10.5281/zenodo.17544515
title: The effects of sulfur on near-liquidus phase relations of highly reduced mafic silicate melts with implications on magmatism in Mercury

description: tables and supplementary material to manuscript preparation
DOI: 10.15468/dl.rd7s23
title: Occurrence Download

description: A dataset containing 4499764 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pterygoplichthys joselimaianus, Salmo trutta, Ctenopharyngodon idella, Trigonostigma heteromorpha, Trichopodus trichopterus, Tropheops tropheops, Thayeria boehlkei, Pelmatolapia mariae, Coregonus peled, Danio albolineatus, Umbra krameri, Pterygoplichthys disjunctivus, Ictiobus cyprinellus, Paracheirodon axelrodi, Oncorhynchus keta, Trichromis salvini, Coregonus macrophthalmus, Parachromis managuensis, Horabagrus brachysoma, Oncorhynchus clarkii, Hyphessobrycon herbertaxelrodi, Gambusia holbrooki, Coregonus maraenoides, Kuhlia rupestris, Lepomis megalotis, Lepomis auritus, Geophagus brasiliensis, Xenentodon cancila, Channa argus, Ponticola kessleri)",
 "HasCoordinate is true",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 4499764 records from 970 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020045-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1388484.10fu
title: SH1388484.10FU

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/sh1388488.10fu
title: SH1388488.10FU

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/sh1388492.10fu
title: SH1388492.10FU

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/sh1388496.10fu
title: SH1388496.10FU

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/sh1388498.10fu
title: SH1388498.10FU

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/sh1388499.10fu
title: SH1388499.10FU

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.