cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18660902
title: ПРОФИЛАКТИКА И РЕАБИЛИТАЦИЯ МЕНИСКОПАТИИ В СФЕРЕ СПОРТИВНОЙ МЕДИЦИНЫ

description: Менископатия — это одно из наиболее распространённых заболеваний коленного сустава, особенно в спортивной среде. Под этим термином понимают патологическое состояние менисков, возникающее как вследствие травм, так и на фоне дегенеративных изменений. Мениски — это хрящевые образования, расположенные между бедренной и большеберцовой костями. Их основная функция — амортизация и стабилизация сустава при движении.
DOI: 10.5281/zenodo.18660903
title: ПРОФИЛАКТИКА И РЕАБИЛИТАЦИЯ МЕНИСКОПАТИИ В СФЕРЕ СПОРТИВНОЙ МЕДИЦИНЫ

description: Менископатия — это одно из наиболее распространённых заболеваний коленного сустава, особенно в спортивной среде. Под этим термином понимают патологическое состояние менисков, возникающее как вследствие травм, так и на фоне дегенеративных изменений. Мениски — это хрящевые образования, расположенные между бедренной и большеберцовой костями. Их основная функция — амортизация и стабилизация сустава при движении.
DOI: 10.15468/dl.nhpnmx
title: Occurrence Download

description: A dataset containing 488 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Buteo buteo buteo",
 "Year 2022-2022"
 ]
}

 The dataset includes 488 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022043-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ytm9u2
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lecanora stenotropa",
 "Country is one of (Norway)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "Year is greater than or equal to 1986",
 "Year is less than or equal to 2025",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Machine Observation"
 ]
 }
 }
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022045-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18660848
title: Dataset for Systematic Review: Computational Enhancement of Behavioral Activation in Depression: A Systematic Review of AI Methods and Neurobiological Markers

description: This dataset supports the systematic review titled "Computational Enhabcement of Behavioral Activation in Depression: A Systematic Review of AI Methods and Neurobiological Markers" (Tallón Fuentes et al.).

It contains metadata for 65 included studies (updated search to February 1, 2026), extracted from PubMed, Scopus, ACM Digital Library, and Web of Science. The review focuses on the integration of artificial intelligence (AI) and computational methods with behavioral activation (BA) interventions, neurobiological mechanisms (e.g., hippocampal changes, BDNF, HPA axis), frailty considerations, and advanced detection/monitoring approaches.

Files included:- dataset.csv: Main metadata table with 65 studies, including Authors, Year, Title, Journal, Volume/Pages, DOI, and Full Citation.- S1_File.docx: Retrospective review protocol.- Online Resource 1.pdf: Full search terms, strings, and strategy (including Boolean operators and variants).- S2_File.docx: Completed PRISMA 2020 checklist for systematic reviews.

The data were curated following PRISMA 2020 guidelines and support reproducibility of the review's findings on AI-enhanced BA, multimodal neuroimaging, digital phenotyping, and frailty moderation in depression.

License: CC BY 4.0 (Creative Commons Attribution 4.0 International). Users are free to share and adapt the material with appropriate credit to the authors.

Keywords: depression, behavioral activation, artificial intelligence, machine learning, neuroimaging, frailty, digital mental health, multimodal fusion, systematic review, PRISMA

DOI: 10.5281/zenodo.18660848
DOI: 10.5281/zenodo.18451336
title: Dataset for Systematic Review: Computational Enhancement of Behavioral Activation in Depression: A Systematic Review of AI Methods and Neurobiological Markers

description: This dataset supports the systematic review titled "Computational Enhabcement of Behavioral Activation in Depression: A Systematic Review of AI Methods and Neurobiological Markers" (Tallón Fuentes et al.).

It contains metadata for 65 included studies (updated search to February 1, 2026), extracted from PubMed, Scopus, ACM Digital Library, and Web of Science. The review focuses on the integration of artificial intelligence (AI) and computational methods with behavioral activation (BA) interventions, neurobiological mechanisms (e.g., hippocampal changes, BDNF, HPA axis), frailty considerations, and advanced detection/monitoring approaches.

Files included:- dataset.csv: Main metadata table with 65 studies, including Authors, Year, Title, Journal, Volume/Pages, DOI, and Full Citation.- S1_File.docx: Retrospective review protocol.- Online Resource 1.pdf: Full search terms, strings, and strategy (including Boolean operators and variants).- S2_File.docx: Completed PRISMA 2020 checklist for systematic reviews.

The data were curated following PRISMA 2020 guidelines and support reproducibility of the review's findings on AI-enhanced BA, multimodal neuroimaging, digital phenotyping, and frailty moderation in depression.

License: CC BY 4.0 (Creative Commons Attribution 4.0 International). Users are free to share and adapt the material with appropriate credit to the authors.

Keywords: depression, behavioral activation, artificial intelligence, machine learning, neuroimaging, frailty, digital mental health, multimodal fusion, systematic review, PRISMA

DOI: 10.5281/zenodo.18660848
DOI: 10.15468/dl.sjgprd
title: Occurrence Download

description: A dataset containing 5 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)"
 ]
 }
 },
 "Year is greater than or equal to 1900",
 "TaxonKey is Eulalia hirtifolia"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022047-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5167/uzh-291643
title: Moerckia blyttii (Mørch) Brockm.
DOI: 10.5167/uzh-291644
title: Moerckia flotoviana (Nees) Schiffn.
DOI: 10.5167/uzh-291645
title: Prasanthus suecicus (Gottsche) Lindb.
DOI: 10.5167/uzh-291646
title: Syzygiella autumnalis (DC.) K.Feldberg & al.
DOI: 10.5167/uzh-291647
title: Lepidozia reptans (L.) Dumort.
DOI: 10.5281/zenodo.18661078
title: Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland

description: Der Datensatz "Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland" des Robert Koch-Instituts dokumentiert die tägliche intensivmedizinische Versorgungslage seit der COVID-19-Pandemie. Basierend auf Meldungen aller intensivbettenführenden Krankenhäuser in Deutschland erfasst das DIVI-Intensivregister Echtzeitdaten zu belegten und freien Intensivbetten. Die Erhebung differenziert nach Altersgruppen, Regionen und Versorgungsstufen. COVID-19-Fälle auf Intensivstationen werden gesondert ausgewiesen. Die Daten stehen aggregiert auf Bundes-, Landes- und Kreisebene zur Verfügung. Damit bildet der Datensatz eine Grundlage für die Überwachung von Kapazitäten, die Koordination von Behandlungskapazitäten und politische Entscheidungsprozesse während der Pandemie und darüber hinaus.
DOI: 10.15139/s3/whucvc
title: Data Management and Sharing Plan for: CAREER: Knowledge Exchange for Supporting Youth with or at Risk for Mathematics Difficulty

description: The Data Management and Sharing Plan describes the scientific data to be generated and/or used in the research and outlines a strategy for managing and sharing project data.
description: Data Management and Sharing Plan
DOI: 10.15468/dl.ar7yn9
title: Occurrence Download

description: A dataset containing 37175 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Year 2022-2022"
 ]
}

 The dataset includes 37175 records from 302 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022046-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yjdg2d
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Souroubea didyma"
 ]
}

 The dataset includes 30 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022049-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18661066
title: INTERPRETATION AND PERFORMANCE SKILLS DIRECTED BY MUNAVVARA ABDULLAYEVA: A SCIENTIFIC-THEORETICAL ANALYSIS

description: This article studies the synthesis of directing and acting based on Munavvara Abdullayeva's textbook "The Art of Directing" and her staging activities. The article covers the analysis of the work, the stages of creating an artistic image, and the methodological foundations of directing solutions based on sources.
DOI: 10.5281/zenodo.18661065
title: INTERPRETATION AND PERFORMANCE SKILLS DIRECTED BY MUNAVVARA ABDULLAYEVA: A SCIENTIFIC-THEORETICAL ANALYSIS

description: This article studies the synthesis of directing and acting based on Munavvara Abdullayeva's textbook "The Art of Directing" and her staging activities. The article covers the analysis of the work, the stages of creating an artistic image, and the methodological foundations of directing solutions based on sources.
DOI: 10.21227/gb06-sd77
title: "GENERATIVE ARTIFICIAL INTELLIGENCE CHATBOT."

description: "Artificial intelligence has revolutionized digital interactions, making chatbots essential forautomating conversations, delivering information, and enhancing user engagement. This projectintroduces an AI-powered chatbot that leverages the DeepSeek LLM to generate concise, engaging,and humor-infused responses, creating a dynamic and enjoyable user experience. The chatbotintegrates authentication, session management, and message storage, allowing users to maintaincontinuous and personalized conversations.Built using Streamlit, the chatbot offers a clean and intuitive user interface, ensuring accessibility forboth technical and non-technical users. The core language model integration is powered by HuggingFace, enabling seamless connectivity with DeepSeek for intelligent response generation. Toefficiently manage user data, Astra DB (NoSQL) is used, ensuring fast, scalable, and reliable storagefor chat histories and user sessions. This architecture enables smooth, real-time interactions whilepreserving context across different sessions.A key challenge addressed in this project is server congestion in DeepSeek LLM, which can lead todelays or unavailability. To mitigate this, the chatbot features a fallback mechanism, ensuringuninterrupted usability even during high traffic. Future improvements may include integratingalternative LLMs, implementing local model hosting, or leveraging caching techniques to enhanceresponse speed and reliability.This project highlights the potential of large language models in creating intelligent and engagingchatbots. By incorporating secure authentication, session persistence, and scalable databasemanagement, it delivers an efficient, responsive, and enjoyable conversational experience, makingAI-driven interactions more accessible and reliable for users."
DOI: 10.6084/m9.figshare.31323763.v2
title: Wiedemann–Franz law violation and topological magnons in a Kagome metal

description: Fig.1-5 & Supplemental Information DATA
DOI: 10.17026/ar/di675m
title: Archeologisch inventariserend veldonderzoek, verkennende fase, door middel van boringen Kerkstraat 21-23 te Erp (gemeente Meierijstad) AM24046

description: &lt;p&gt;In juli 2024 is door Aeres Milieu een archeologisch verkennend booronderzoek uitgevoerd aan de Kerkstraat 21-23 te Erp (gemeente Meierijstad). &lt;/p&gt;&lt;p&gt;
Aanleiding voor het laten uitvoeren van dit bodemonderzoek betreft een aanvraag bestemmingsplanwijziging. De diepte van de toekomstige bodemverstoring is op dit moment onbekend, maar uitgaande van een standaard funderingsdiepte zonder onderkeldering, zal de bodemverstoring tot ten minste 80 centimeter (vorstvrije diepte) beneden maaiveld reiken. De onderzoekslocatie ligt volgens de Archeologische Beleidskaart van de voormalige gemeente Veghel (2014) in een zone met een hoge archeologische verwachting. Binnen het bestemmingsplan Erp (2013) geldt de dubbelbestemming Waarde – Archeologie. Voor deze zone geldt een onderzoeksplicht bij bodemingrepen met een oppervlakte groter dan 100 m2 en een verstoringsdiepte vanaf 30 cm -mv. Middels deze kaart heeft de gemeente aangegeven dat de locatie onderzoeksplichtig is.&lt;/p&gt;&lt;p&gt;
In dit kader is door Lycens B.V. in 2021 reeds een bureauonderzoek voor het plangebied uitgevoerd. Uit het bureauonderzoek kwam naar voren dat de archeologische verwachting voor de aanwezigheid van sporen en/of vondsten middelhoog werd geschat voor de perioden laat-paleolithicum tot en met het neolithicum. Voor de daaropvolgende perioden tot de nieuwe tijd geldt een hoge verwachting voor de aanwezigheid van sporen en/of vondsten. Er werd door Lycens B.V. onder andere vanwege deze hoge verwachtingen een vervolgonderzoek in de vorm van een verkennend veldonderzoek aan de hand van boringen aanbevolen. De gemeente Meierijstad heeft in 2024 hier een selectiebesluit over genomen en stemt in met het advies voor het vervolgonderzoek. Op basis van het uitgevoerd verkennend veldonderzoek middels boringen kan worden gesteld dat de bodem binnen het plangebied plaatselijk tot minstens 30 centimeter in de C-horizon is verstoord. Plaatselijk reikt de verstoring tot 140 centimeter beneden maaiveld. Deze verstoring is van dermate grootte en diepte, dat de kans op het aantreffen van intacte archeologische resten laag wordt geacht. Het kan niet worden uitgesloten dat er nog diep reikende sporen aanwezig zouden kunnen zijn, maar gezien de natte omstandigheden wordt de kans op het aantreffen klein geacht en zal de informatiewaarde en kenniswinst gering zijn. &lt;/p&gt;&lt;p&gt;
Voor het plangebied wordt om bovenstaande redenen geen archeologisch vervolgonderzoek noodzakelijk geacht.&lt;/p&gt;
DOI: 10.5281/zenodo.18659748
title: Dataset for "Strongly correlated Josephson junction: proximity effect in the single-layer Hubbard model"

description: The dataset includes raw data, processed data, scripts that generated the data and the scripts that generated the plots in the manuscript.
DOI: 10.5281/zenodo.18659749
title: Dataset for "Strongly correlated Josephson junction: proximity effect in the single-layer Hubbard model"

description: The dataset includes raw data, processed data, scripts that generated the data and the scripts that generated the plots in the manuscript.
DOI: 10.5281/zenodo.18658584
title: TAILWIND Design Tensions

description: Tables with Design Tension values in fairleads and anchors for different scenarios (floater, mooring system) and load cases, as reported in TAILWIND D1.1
DOI: 10.5281/zenodo.18658585
title: TAILWIND Design Tensions

description: Tables with Design Tension values in fairleads and anchors for different scenarios (floater, mooring system) and load cases, as reported in TAILWIND D1.1
DOI: 10.15468/dl.5cg2fc
title: Occurrence Download

description: A dataset containing 4196 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ambrosia artemisiifolia",
 "Year 1990-2000"
 ]
}

 The dataset includes 4196 records from 301 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022050-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.875b75
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lecanora salicicola",
 "Country is one of (Norway)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "Year is greater than or equal to 1986",
 "Year is less than or equal to 2025",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Machine Observation"
 ]
 }
 }
 ]
}

 The dataset includes 15 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022051-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r63fwe
title: Occurrence Download

description: A dataset containing 137 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aedes saxicola"
 ]
}

 The dataset includes 137 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022052-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zkae2q
title: Occurrence Download

description: A dataset containing 766 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Buteo buteo buteo",
 "Year 2023-2023"
 ]
}

 The dataset includes 766 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022053-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.31347244
title: Supplementary information files for "Inferring structure of cortical neuronal networks from activity data: a statistical physics approach<b>"</b>

description: Supplementary files for article "Inferring structure of cortical neuronal networks from activity data: a statistical physics approach"<br><br>Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process. We devise a probabilistic method for inferring the effective network structure by integrating techniques from Bayesian statistics, statistical physics, and principled machine learning. The method and resulting algorithm allow one to infer the effective network structure, identify the excitatory and inhibitory type of its constituents, and predict neuronal spiking activity by employing the inferred structure. We validate the method and algorithm’s performance using synthetic data, spontaneous activity of an in silico emulator, and realistic in vitro neuronal networks of modular and homogeneous connectivity, demonstrating excellent structure inference and activity prediction. We also show that our method outperforms commonly used existing methods for inferring neuronal network structure. Inferring the evolving effective structure of neuronal networks will provide new insight into the learning process due to stimulation in general and will facilitate the development of neuron-based circuits with computing capabilities.<br><br>© The Author(s), CC BY 4.0
DOI: 10.17028/rd.lboro.31347244.v1
title: Supplementary information files for "Inferring structure of cortical neuronal networks from activity data: a statistical physics approach<b>"</b>

description: Supplementary files for article "Inferring structure of cortical neuronal networks from activity data: a statistical physics approach"<br><br>Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process. We devise a probabilistic method for inferring the effective network structure by integrating techniques from Bayesian statistics, statistical physics, and principled machine learning. The method and resulting algorithm allow one to infer the effective network structure, identify the excitatory and inhibitory type of its constituents, and predict neuronal spiking activity by employing the inferred structure. We validate the method and algorithm’s performance using synthetic data, spontaneous activity of an in silico emulator, and realistic in vitro neuronal networks of modular and homogeneous connectivity, demonstrating excellent structure inference and activity prediction. We also show that our method outperforms commonly used existing methods for inferring neuronal network structure. Inferring the evolving effective structure of neuronal networks will provide new insight into the learning process due to stimulation in general and will facilitate the development of neuron-based circuits with computing capabilities.<br><br>© The Author(s), CC BY 4.0
DOI: 10.5281/zenodo.18661103
title: GENERATIVE ARTIFICIAL INTELLIGENCE CHATBOT.

description: Artificial intelligence has revolutionized digital interactions, making chatbots essential forautomating conversations, delivering information, and enhancing user engagement. This projectintroduces an AI-powered chatbot that leverages the DeepSeek LLM to generate concise, engaging,and humor-infused responses, creating a dynamic and enjoyable user experience. The chatbotintegrates authentication, session management, and message storage, allowing users to maintaincontinuous and personalized conversations.Built using Streamlit, the chatbot offers a clean and intuitive user interface, ensuring accessibility forboth technical and non-technical users. The core language model integration is powered by HuggingFace, enabling seamless connectivity with DeepSeek for intelligent response generation. Toefficiently manage user data, Astra DB (NoSQL) is used, ensuring fast, scalable, and reliable storagefor chat histories and user sessions. This architecture enables smooth, real-time interactions whilepreserving context across different sessions.A key challenge addressed in this project is server congestion in DeepSeek LLM, which can lead todelays or unavailability. To mitigate this, the chatbot features a fallback mechanism, ensuringuninterrupted usability even during high traffic. Future improvements may include integratingalternative LLMs, implementing local model hosting, or leveraging caching techniques to enhanceresponse speed and reliability.This project highlights the potential of large language models in creating intelligent and engagingchatbots. By incorporating secure authentication, session persistence, and scalable databasemanagement, it delivers an efficient, responsive, and enjoyable conversational experience, makingAI-driven interactions more accessible and reliable for users.