cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48331/scielodata.d04oij
title: Benthic Fish Project

description: This study was carried out in the mainstem channels of the lower reaches of the major whitewater rivers of Central Amazon: Japurá, Madeira and Purus rivers. We established 10 sampling sites along each river course, separated by ~30 km. Sampling took place during rising and receding water seasons (Figure 1), from 2012 to 2014: Madeira River, September 2012 (receding) and March 2013 (rising); Japurá River, April 2013 (rising) and August 2013 (receding); Purus River, September 2013 (receding) and April 2014 (rising). Fish were sampled using a bottom trawl net with an external mesh size of 4 cm and an internal net in the form of a “funnel” (0.5 m height, 3m long and 3m wide; 5 mm mesh size). A parallel trawl was performed at each sampling site, following the current direction, with 10 min total duration time per trawl.
DOI: 10.3535/jf3-2w6-77p
title: Solanum decipiens Opiz

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/lef-6yr-xqy
title: Solanum decipiens Opiz

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/c1h-fp0-rfb
title: Sisymbrium officinale (L.) Scop.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/dnq-7c5-nph
title: Sisymbrium officinale (L.) Scop.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.48331/scielodata.d04oij/hofsli
title: Benthic Fish Project.txt

description: Abundance Data of benthic fish
DOI: 10.3535/65t-lzm-8x1
title: Sisymbrium officinale (L.) Scop.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/77b-2hx-cjq
title: Solanum decipiens Opiz

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/vgh-wsd-7cv
title: Solanum decipiens Opiz

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.48331/scielodata.azms8g
title: Data for: 2021-0822 Validação do Ludic-Quest à ludicidade de jogos na saúde: jogabilidade e emoções em campo
title: Validação do Ludic-Quest à ludicidade de jogos na saúde: jogabilidade e emoções em campo

description: Suplemento do artigo Validação do Ludic-Quest à ludicidade de jogos na saúde: jogabilidade e emoções em campo, submetido à Reben à publicação. O objetivo do estudo foi Validar o constructo e a confiabilidade do instrumento Ludic-Quest à avaliação da ludicidade de jogos na saúde, por meio de fatores latentes à jogabilidade/aprendizagem e às emoções na partida. Apresentamos os resultados da análise fatorial e do Alfa Cronbach realizadas no SPSS, por dimensão analisada, com descrição das variáveis.
description: Data for: Validação do Ludic-Quest à ludicidade de jogos na saúde: jogabilidade e emoções em campo 
Autora correspondente: Maria Raquel Gomes Maia Pires - ENF/FS/UnB
e-mail: maiap@unb.br; rakas.mr@gmail.com
Dados coletados no período de outubro de 2018 a junho de 2019. O artigo integra projeto financiado pelo Cnpq, realizado com a participação de pesquisadoras/es da UnB, UCB-DF e UMCS-SP.
DOI: 10.3535/anw-l1w-vxx
title: Solanum decipiens Opiz

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/vaw-dxe-rfj
title: Sisymbrium officinale (L.) Scop.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/jbk-dzb-w95
title: Solanum decipiens Opiz

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.48331/scielodata.azms8g/ge47gv
title: Suplemento1_AnaliseFatorial_LudicQuest_2021_11_26.pdf

description: Suplemento do artigo Validação do Ludic-Quest à ludicidade de jogos na saúde: jogabilidade e emoções em campo
DOI: 10.3535/qwc-ya6-8ax
title: Solanum decipiens Opiz

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/r04-6cg-h9m
title: Solanum decipiens Opiz

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.6084/m9.figshare.29175722
title: Machine Learning and Artificial Intelligence in Suicide Prevention: A Bibliometric Analysis of Emerging Trends and Implications for Nursing

description: Nurses play a crucial role in suicide prevention, yet the integration of artificial intelligence and machine learning technologies into nursing practice remains understudied. This research examines how these technologies can enhance nurses’ ability to identify and intervene with at-risk patients. A systematic bibliometric analysis and thematic mapping approach was employed. The Web of Science database was searched for relevant publications from January 2019 to October 2024. The initial search yielded 883 publications, with 257 meeting the inclusion criteria after systematic screening. Analysis revealed six distinct research clusters, with machine learning-based behavioral prediction emerging as the dominant theme. Findings indicate significant potential for integrating artificial intelligence-supported tools into nursing workflows, particularly in risk assessment and early intervention. Natural language processing and ecological momentary assessment emerged as promising approaches for enhancing nurse-patient communication and monitoring. These findings suggest opportunities for nurses to leverage artificial intelligence technologies in suicide prevention while maintaining the essential human element of care. This study provides evidence-based guidance for nurses implementing artificial intelligence-supported suicide prevention tools while maintaining therapeutic relationships and professional judgment in clinical practice.
DOI: 10.6084/m9.figshare.29175722.v1
title: Machine Learning and Artificial Intelligence in Suicide Prevention: A Bibliometric Analysis of Emerging Trends and Implications for Nursing

description: Nurses play a crucial role in suicide prevention, yet the integration of artificial intelligence and machine learning technologies into nursing practice remains understudied. This research examines how these technologies can enhance nurses’ ability to identify and intervene with at-risk patients. A systematic bibliometric analysis and thematic mapping approach was employed. The Web of Science database was searched for relevant publications from January 2019 to October 2024. The initial search yielded 883 publications, with 257 meeting the inclusion criteria after systematic screening. Analysis revealed six distinct research clusters, with machine learning-based behavioral prediction emerging as the dominant theme. Findings indicate significant potential for integrating artificial intelligence-supported tools into nursing workflows, particularly in risk assessment and early intervention. Natural language processing and ecological momentary assessment emerged as promising approaches for enhancing nurse-patient communication and monitoring. These findings suggest opportunities for nurses to leverage artificial intelligence technologies in suicide prevention while maintaining the essential human element of care. This study provides evidence-based guidance for nurses implementing artificial intelligence-supported suicide prevention tools while maintaining therapeutic relationships and professional judgment in clinical practice.
DOI: 10.3535/l0n-fqn-57x
title: Saponaria officinalis L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/m7n-ljv-fhe
title: Reichardia picroides (L.) Roth

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.48331/scielodata.azms8g/xyv2pb
title: README

description: Readme do suplemento Validação do Ludic-Quest à ludicidade de jogos na saúde: jogabilidade e emoções em campo
DOI: 10.3535/51q-jaw-5tb
title: Senecio vulgaris L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/s3w-dk3-wdh
title: Senecio vulgaris L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/l1e-wjs-jqh
title: Senecio vulgaris L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/mrv-n5w-rw5
title: Senecio vulgaris L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/33r-56l-syd
title: Reichardia picroides (L.) Roth

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.48331/scielodata.93ealf
title: Data for: 2021-0836 Shift work schedule and damage to the health of nursing workers at Brazilian public hospital

description: Banco de dados do projeto financiado pelo Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) edital Universal Notice 2016, processo 402986/2016-4.
DOI: 10.3535/k7s-bbb-le1
title: Stellaria media (L.) Vill.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/w5x-jlt-103
title: Pulicaria dysenterica (L.) Bernh.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/xd0-7bw-f6k
title: Senecio vulgaris L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.48331/scielodata.93ealf/qnshcl
title: arquivo_de_dados

description: BANCO DE DADOS RELACIONADO
DOI: 10.6084/m9.figshare.29120577
title: Adaptation Using Spatially Distributed Gaussian Processes

description: We consider the accuracy of an approximate posterior distribution in nonparametric regression problems by combining posterior distributions computed on subsets of the data defined by the locations of the independent variables. We show that this approximate posterior retains the rate of recovery of the full data posterior distribution, where the rate of recovery adapts to the smoothness of the true regression function. As particular examples we consider Gaussian process priors based on integrated Brownian motion and the Matérn kernel augmented with a prior on the length scale. Besides theoretical guarantees we present a numerical study of the methods both on synthetic and real world data. We also propose a new aggregation technique, which numerically outperforms previous approaches. Finally, we demonstrate empirically that spatially distributed methods can adapt to local regularities, potentially outperforming the original Gaussian process. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29120577.v2
title: Adaptation Using Spatially Distributed Gaussian Processes

description: We consider the accuracy of an approximate posterior distribution in nonparametric regression problems by combining posterior distributions computed on subsets of the data defined by the locations of the independent variables. We show that this approximate posterior retains the rate of recovery of the full data posterior distribution, where the rate of recovery adapts to the smoothness of the true regression function. As particular examples we consider Gaussian process priors based on integrated Brownian motion and the Matérn kernel augmented with a prior on the length scale. Besides theoretical guarantees we present a numerical study of the methods both on synthetic and real world data. We also propose a new aggregation technique, which numerically outperforms previous approaches. Finally, we demonstrate empirically that spatially distributed methods can adapt to local regularities, potentially outperforming the original Gaussian process. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.3535/8q4-lyh-qln
title: Senecio vulgaris L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/gef-0mv-qy6
title: Senecio vulgaris L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.6084/m9.figshare.29120577.v1
title: Adaptation using spatially distributed Gaussian Processes

description: We consider the accuracy of an approximate posterior distribution in nonparametric regression problems by combining posterior distributions computed on subsets of the data defined by the locations of the independent variables. We show that this approximate posterior retains the rate of recovery of the full data posterior distribution, where the rate of recovery adapts to the smoothness of the true regression function. As particular examples we consider Gaussian process priors based on integrated Brownian motion and the Matérn kernel augmented with a prior on the length scale. Besides theoretical guarantees we present a numerical study of the methods both on synthetic and real world data. We also propose a new aggregation technique, which numerically outperforms previous approaches. Finally, we demonstrate empirically that spatially distributed methods can adapt to local regularities, potentially outperforming the original Gaussian process.
DOI: 10.3535/c9c-9xp-z73
title: Senecio vulgaris L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/s3w-nab-gha
title: Senecio vernalis Waldst. & Kit.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.48331/scielodata.93ealf/tqqgbn
title: README.pdf

description: Descrição detalhada dos dados
DOI: 10.6084/m9.figshare.28695866
title: Network Goodness-of-Fit for the Block-Model Family

description: The block-model family has four popular network models (SBM, DCBM, MMSBM, and DCMM). A fundamental problem is, how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with two main ideas. The first is to use cycle count statistics as a general recipe for GoF. The second is a novel network fitting scheme. GoF-MSCORE is a flexible GoF approach, and we further extend it to SBM, DCBM, and MMSBM. This gives rise to a series of GoF metrics covering each of the four models in the block-model family. We show that for each of the four models, if the assumed model is correct, then the corresponding GoF metric converges to N(0,1) as the network sizes diverge. We also analyze the powers and show that these metrics are optimal in many settings. In comparison, many other GoF ideas face challenges: they may lack a parameter-free limiting null, or are nonoptimal in power, or face an analytical hurdle. Note that a parameter-free limiting null is especially desirable as many network models have a large number of unknown parameters. The limiting nulls of our GoF metrics are always N(0,1), which are parameter-free as desired. For 12 frequently-used real networks, we use the proposed GoF metrics to show that DCMM fits well with almost all of them. We also show that SBM, DCBM, and MMSBM do not fit well with many of these networks, especially when the networks are relatively large. To complement with our study on GoF, we also show that the DCMM is nearly as broad as the rank-<i>K</i> network model. Based on these results, we recommend the DCMM as a promising model for undirected networks. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.28695866.v2
title: Network Goodness-of-Fit for the Block-Model Family

description: The block-model family has four popular network models (SBM, DCBM, MMSBM, and DCMM). A fundamental problem is, how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with two main ideas. The first is to use cycle count statistics as a general recipe for GoF. The second is a novel network fitting scheme. GoF-MSCORE is a flexible GoF approach, and we further extend it to SBM, DCBM, and MMSBM. This gives rise to a series of GoF metrics covering each of the four models in the block-model family. We show that for each of the four models, if the assumed model is correct, then the corresponding GoF metric converges to N(0,1) as the network sizes diverge. We also analyze the powers and show that these metrics are optimal in many settings. In comparison, many other GoF ideas face challenges: they may lack a parameter-free limiting null, or are nonoptimal in power, or face an analytical hurdle. Note that a parameter-free limiting null is especially desirable as many network models have a large number of unknown parameters. The limiting nulls of our GoF metrics are always N(0,1), which are parameter-free as desired. For 12 frequently-used real networks, we use the proposed GoF metrics to show that DCMM fits well with almost all of them. We also show that SBM, DCBM, and MMSBM do not fit well with many of these networks, especially when the networks are relatively large. To complement with our study on GoF, we also show that the DCMM is nearly as broad as the rank-<i>K</i> network model. Based on these results, we recommend the DCMM as a promising model for undirected networks. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.p5276v
title: Occurrence Download

description: A dataset containing 4798 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lygodium palmatum"
 ]
}

 The dataset includes 4798 records from 176 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000712-250710112747441/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/g55jfe
title: 2024 Democracy Checkup

description: Democracy Checkup surveys gather information about the opinions of Canadians when election campaigns are not activating their interest and/or partisan attitudes, and capture general public opinion about democratic values, public policies, and current issues.
DOI: 10.15468/dl.npq4dt
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Pennahia argentata (Houttuyn, 1782)",
 "Year 2000-2025"
 ]
}

 The dataset includes 21 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014097-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uhvmtu
title: Occurrence Download

description: A dataset containing 4932 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)"
 ]
 }
 },
 "TaxonKey is Podalyria Willd."
 ]
}

 The dataset includes 4932 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014099-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/c3r-gq1-7xp
title: Abida polyodon (Draparnaud, 1801)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ck1-gf4-bw9
title: Alvania dorbignyi (Audouin, 1826)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/5ng-mx9-h4v
title: Asperitas inquinata penidae B. Rensch, 1938

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/rd6-p6g-cqa
title: Cardita crassicosta Lamarck, 1819

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/9qg-nmq-ybn
title: Odostomia J. Fleming, 1813

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/bq4-1lt-6g7
title: Odostomia J. Fleming, 1813

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/g04-vd3-e9k
title: Euglesa subtruncata (Malm, 1855)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.29rcz2
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acanthaster planci",
 "Country is Marshall Islands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 32 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008273-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/d9x-tq0-ff2
title: Peringiella elegans (Locard, 1891)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qk6-pz0-2nr
title: Euglesa supina (A. Schmidt, 1851)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/0nc-p8d-akh
title: Philine punctata (J. Adams, 1800)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/mkx-w1c-cbw
title: Neverita didyma (Röding, 1798)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ja6-w0q-01f
title: Vitreolina curva (Monterosato, 1874)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/xw0-zej-6yj
title: Metaxia abrupta (R. B. Watson, 1880)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/gcz-dpl-v30
title: Mitromorpha crenipicta (Dautzenberg, 1889)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/w1t-5qm-48c
title: Mitromorpha olivoidea (Cantraine, 1835)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/hlz-qtn-qsr
title: Volutoconus bednalli (Brazier, 1878)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/j30-n5w-kv0
title: Campylaea planospira (Lamarck, 1822)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/t2t-0qx-2lr
title: Alvania lanciae (Calcara, 1845)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/kvy-e3e-4ps
title: Granaria variabilis (Draparnaud, 1801)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/tfq-mfb-cfr
title: Vallonia pulchella (O. F. Müller, 1774)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/8lb-w9v-np0
title: Omalogyra atomus (R. A. Philippi, 1841)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/nam-sqk-ap4
title: Faustina Kobelt, 1904

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.