cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.dcrtxy
title: Occurrence Download

description: A dataset containing 1986 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "Geometry POLYGON((-8.91517 41.28053,-8.92141 40.53141,-9.13366 39.89465,-9.7064 39.04659,-9.46591 38.33655,-9.01774 38.18716,-9.00017 37.57203,-9.13198 36.96568,-8.51685 36.94811,-7.78748 36.86023,-7.61173 37.01841,-7.36568 37.05356,-7.31921 37.66587,-6.86527 38.17517,-7.16421 38.46303,-6.87634 39.11626,-7.24171 39.50377,-6.8542 39.68091,-6.69277 40.99337,-6.23081 41.42411,-6.13454 41.70703,-6.36815 41.74249,-6.50549 41.99844,-7.19166 42.02727,-7.415 41.88434,-7.74256 41.94091,-7.89741 41.97665,-8.05524 41.93794,-7.99866 42.02132,-8.18626 42.19999,-8.58827 42.11661,-8.85032 41.94389,-8.95543 41.88506,-8.91517 41.28053))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lucanus cervus (Linnaeus, 1758)",
 "Year 2015-2025"
 ]
}

 The dataset includes 1986 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000151-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/fwm2vtpwby
title: Original data

description: Here's the data from our study
DOI: 10.17632/fwm2vtpwby.1
title: Original data

description: Here's the data from our study
DOI: 10.17632/2gwnjx729x.1
title: Actitudes hacia la Inteligencia Artificial, Ansiedad Digital y Competencias Digitales entre estudiantes universitarios

description: Adoptamos un diseño transversal cuantitativo para recolectar datos de 235 estudiantes universitarios, divididos entre estudios de educación (Educación Primaria e Infantil) y estudios sociales (Educación Social y Trabajo Social). Esta división nos permite explorar diferencias potenciales en las actitudes hacia la IA entre distintos campos de estudio, asegurando una representación adecuada de cada grupo. La muestra se obtuvo de manera incidental, buscando mantener cuotas equilibradas de estudiantes de los cuatro cursos académicos de grado.
El muestreo incidental implicó la selección de participantes disponibles y dispuestos a participar voluntariamente en el estudio en el momento de la recolección de datos. Se intentó equilibrar la muestra en términos de estudiantes de primer, segundo, tercer y cuarto año para cada una de las titulaciones mencionadas. Esto garantiza una diversidad en las respuestas que puede reflejar las diferencias en el nivel de experiencia académica y exposición a la tecnología.

Para medir las actitudes hacia la IA, hemos adaptado escalas validadas y ampliamente reconocidas en estudios académicos. Implementamos la General Attitudes towards Artificial Intelligence Scale (GAAIS) y la Attitudes towards AI Scale (AAIS), siguiendo la metodología de Schepman y Rodway (2022) y Stein et al. (2024). Estas herramientas son fundamentales para obtener una evaluación precisa y detallada de las perspectivas estudiantiles.

La Escala de Actitudes Generales hacia la Inteligencia Artificial (GAAIS) mide las actitudes generales hacia la IA con ocho ítems en dos subdimensiones: actitudes positivas y actitudes negativas hacia la IA. La escala presenta alta fiabilidad, con coeficientes alfa de Cronbach de 0.80 para la subdimensión positiva, 0.84 para la negativa y 0.83 para la escala total.
La Escala de Actitudes hacia la Inteligencia Artificial (AAIS) evalúa las actitudes hacia la IA en tres dimensiones: cognitiva, afectiva y conductual. La fiabilidad de esta escala se evaluó con coeficientes alfa de Cronbach de 0.64 para la subdimensión cognitiva, 0.63 para la afectiva y 0.63 para la conductual, con un coeficiente global de 0.84.
La Escala de Ansiedad hacia la Inteligencia Artificial (AIAS), desarrollada por Wang y Wang (2022), mide la ansiedad específica hacia la IA. Seleccionamos las dos dimensiones principales: aprendizaje (8 ítems) y reemplazo laboral (6 ítems), con alta fiabilidad en este estudio (coeficientes alfa de 0.96 y 0.92, respectivamente).
Para evaluar las competencias digitales, aplicamos el marco DigComp 2.2 (Vuorikari, Kluzer, & Punie, 2022), adaptado al contexto educativo actual. Nos concentramos en "habilidades de comunicación y colaboración" (6 ítems) y "alfabetización en información y datos" (7 ítems), con fiabilidades de 0.64 y 0.81, respectivamente. Adaptamos las opciones de respuesta a una escala de frecuencia para captar mejor las competencias avanzadas en un contexto universitario.
DOI: 10.17632/2gwnjx729x.2
title: Actitudes hacia la Inteligencia Artificial, Ansiedad Digital y Competencias Digitales entre estudiantes universitarios

description: Adoptamos un diseño transversal cuantitativo para recolectar datos de 235 estudiantes universitarios, divididos entre estudios de educación (Educación Primaria e Infantil) y estudios sociales (Educación Social y Trabajo Social). Esta división nos permite explorar diferencias potenciales en las actitudes hacia la IA entre distintos campos de estudio, asegurando una representación adecuada de cada grupo. La muestra se obtuvo de manera incidental, buscando mantener cuotas equilibradas de estudiantes de los cuatro cursos académicos de grado.
El muestreo incidental implicó la selección de participantes disponibles y dispuestos a participar voluntariamente en el estudio en el momento de la recolección de datos. Se intentó equilibrar la muestra en términos de estudiantes de primer, segundo, tercer y cuarto año para cada una de las titulaciones mencionadas. Esto garantiza una diversidad en las respuestas que puede reflejar las diferencias en el nivel de experiencia académica y exposición a la tecnología.

Para medir las actitudes hacia la IA, hemos adaptado escalas validadas y ampliamente reconocidas en estudios académicos. Implementamos la General Attitudes towards Artificial Intelligence Scale (GAAIS) y la Attitudes towards AI Scale (AAIS), siguiendo la metodología de Schepman y Rodway (2022) y Stein et al. (2024). Estas herramientas son fundamentales para obtener una evaluación precisa y detallada de las perspectivas estudiantiles.

La Escala de Actitudes Generales hacia la Inteligencia Artificial (GAAIS) mide las actitudes generales hacia la IA con ocho ítems en dos subdimensiones: actitudes positivas y actitudes negativas hacia la IA. La escala presenta alta fiabilidad, con coeficientes alfa de Cronbach de 0.80 para la subdimensión positiva, 0.84 para la negativa y 0.83 para la escala total.
La Escala de Actitudes hacia la Inteligencia Artificial (AAIS) evalúa las actitudes hacia la IA en tres dimensiones: cognitiva, afectiva y conductual. La fiabilidad de esta escala se evaluó con coeficientes alfa de Cronbach de 0.64 para la subdimensión cognitiva, 0.63 para la afectiva y 0.63 para la conductual, con un coeficiente global de 0.84.
La Escala de Ansiedad hacia la Inteligencia Artificial (AIAS), desarrollada por Wang y Wang (2022), mide la ansiedad específica hacia la IA. Seleccionamos las dos dimensiones principales: aprendizaje (8 ítems) y reemplazo laboral (6 ítems), con alta fiabilidad en este estudio (coeficientes alfa de 0.96 y 0.92, respectivamente).
Para evaluar las competencias digitales, aplicamos el marco DigComp 2.2 (Vuorikari, Kluzer, & Punie, 2022), adaptado al contexto educativo actual. Nos concentramos en "habilidades de comunicación y colaboración" (6 ítems) y "alfabetización en información y datos" (7 ítems), con fiabilidades de 0.64 y 0.81, respectivamente. Adaptamos las opciones de respuesta a una escala de frecuencia para captar mejor las competencias avanzadas en un contexto universitario.
DOI: 10.15468/dl.akbhw8
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lithocarpus vinhensis A.Camus"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000153-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2gwnjx729x.3
title: Actitudes hacia la Inteligencia Artificial, Ansiedad Digital y Competencias Digitales entre estudiantes universitarios

description: Adoptamos un diseño transversal cuantitativo para recolectar datos de 235 estudiantes universitarios, divididos entre estudios de educación (Educación Primaria e Infantil) y estudios sociales (Educación Social y Trabajo Social). Esta división nos permite explorar diferencias potenciales en las actitudes hacia la IA entre distintos campos de estudio, asegurando una representación adecuada de cada grupo. La muestra se obtuvo de manera incidental, buscando mantener cuotas equilibradas de estudiantes de los cuatro cursos académicos de grado.
El muestreo incidental implicó la selección de participantes disponibles y dispuestos a participar voluntariamente en el estudio en el momento de la recolección de datos. Se intentó equilibrar la muestra en términos de estudiantes de primer, segundo, tercer y cuarto año para cada una de las titulaciones mencionadas. Esto garantiza una diversidad en las respuestas que puede reflejar las diferencias en el nivel de experiencia académica y exposición a la tecnología.

Para medir las actitudes hacia la IA, hemos adaptado escalas validadas y ampliamente reconocidas en estudios académicos. Implementamos la General Attitudes towards Artificial Intelligence Scale (GAAIS) y la Attitudes towards AI Scale (AAIS), siguiendo la metodología de Schepman y Rodway (2022) y Stein et al. (2024). Estas herramientas son fundamentales para obtener una evaluación precisa y detallada de las perspectivas estudiantiles.

La Escala de Actitudes Generales hacia la Inteligencia Artificial (GAAIS) mide las actitudes generales hacia la IA con ocho ítems en dos subdimensiones: actitudes positivas y actitudes negativas hacia la IA. La escala presenta alta fiabilidad, con coeficientes alfa de Cronbach de 0.80 para la subdimensión positiva, 0.84 para la negativa y 0.83 para la escala total.
La Escala de Actitudes hacia la Inteligencia Artificial (AAIS) evalúa las actitudes hacia la IA en tres dimensiones: cognitiva, afectiva y conductual. La fiabilidad de esta escala se evaluó con coeficientes alfa de Cronbach de 0.64 para la subdimensión cognitiva, 0.63 para la afectiva y 0.63 para la conductual, con un coeficiente global de 0.84.
La Escala de Ansiedad hacia la Inteligencia Artificial (AIAS), desarrollada por Wang y Wang (2022), mide la ansiedad específica hacia la IA. Seleccionamos las dos dimensiones principales: aprendizaje (8 ítems) y reemplazo laboral (6 ítems), con alta fiabilidad en este estudio (coeficientes alfa de 0.96 y 0.92, respectivamente).
Para evaluar las competencias digitales, aplicamos el marco DigComp 2.2 (Vuorikari, Kluzer, & Punie, 2022), adaptado al contexto educativo actual. Nos concentramos en "habilidades de comunicación y colaboración" (6 ítems) y "alfabetización en información y datos" (7 ítems), con fiabilidades de 0.64 y 0.81, respectivamente. Adaptamos las opciones de respuesta a una escala de frecuencia para captar mejor las competencias avanzadas en un contexto universitario.
DOI: 10.15468/dl.63nsaz
title: Occurrence Download

description: A dataset containing 2868 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Tree Diversity and Dynamics of the Forest of Seu Nico (Brazil/MG)"
 ]
}

 The dataset includes 2868 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000146-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/6wvtm5tf7j.1
title: kindlin dimerization

description: inputfiles and output for md simulations of kindlin dimerization
DOI: 10.17632/t62vysxjn3.1
title: Domestic Edge Computing Terminal Pest Identification System Based on Large Model Compression and Lightweight: Innovative Application of Transformer-ConvLSTM and Adaptive Deployment

description: The dataset is a publicly available collection of agricultural pest images, designed to include a variety of common pest species found in agricultural environments. It features 20 distinct pest types, such as rice planthoppers, aphids, spider mites, cabbage caterpillars, among others. The images, which are primarily captured in real-world agricultural settings, come with corresponding labels, making them highly relevant for practical applications. This dataset effectively reflects the diversity and distribution of pests in actual agricultural scenarios.
DOI: 10.17632/rxhmv8wrpg.1
title: Geographic patterns of functional and phylogenetic niche breadth in Holarctic fleas (Siphonaptera)

description: Data and R code used for investigation of geographic patterns of functional and phylogenetic niche breadth in fleas
DOI: 10.25446/oxford.28376060
title: Questionnaire: Protocol: A metabolomic analysis of convalescent inflammatory conditions

description: Questionnaire for the following study: Protocol: A metabolomic analysis of convalescent inflammatory conditions
DOI: 10.15468/dl.uujjh6
title: Occurrence Download

description: A dataset containing 2868 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Tree Diversity and Dynamics of the Forest of Seu Nico (Brazil/MG)"
 ]
}

 The dataset includes 2868 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000145-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/5dnsdg4r8g.1
title: FT-IR spectrum of Locust migratoria protein concentrates considering the extraction methods

description: The document presents FT-IR spectra of insect protein concentrates from Locusta migratoria, Acheta domesticus, and Tenebrio molitor obtained using different extraction methods. The four methods analyzed include: Method I (control, alkali extraction-acid precipitation, AE-AP), Method II (salting-in-AE-AP), Method III (AE-AP-salting-out), and Method IV (salting-in-AE-AP-salting-out). These spectra help assess structural changes in proteins due to different isolation techniques, providing insights into their functional properties and potential applications.
DOI: 10.15468/dl.y5tc53
title: Occurrence Download

description: A dataset containing 845 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Galanthus nivalis L.)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1990",
 "Year is less than or equal to 2019"
 ]
}

 The dataset includes 845 records from 104 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000147-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cuf49n
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Senecio elodes Boiss.)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2020"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000156-250226093636041/datasets/export for details.
DOI: 10.17632/gsvjm3s55w.1
title: Functional properties of insect protein concentrates

description: The table presents the functional properties of insect protein concentrates obtained using different isolation methods for three insect species: Tenebrio molitor, Acheta domesticus, and Locusta migratoria. It includes data on turbidity, color (L*, a*, b*), foamability, and foam stability, showing how different extraction techniques (alkali extraction, salting-in, salting-out, and salting-in/out) affect these properties. The results indicate that salting-out generally improves brightness (L*) and foam stability, with A. domesticus showing the highest foam stability among the tested species.
DOI: 10.17632/kgsb4gdzxm.1
title: LINF_080017000

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/yvzcswx7f5.1
title: The impact of China's protectionist policies on the capitalization of Chinese technology companies

description: In my research, I study the impact of China's protectionist policies on the market value of Chinese technology companies. The presented dataset covers the period from 2014 to 2024 and contains quotes from 14 Chinese technology companies and 3 market indexes: Shenzhen Component Index, Shanghai Stock Exchange and Hong Kong Stock Exchange. In this time period, I study the impact of 20 laws that could potentially have an impact on the capitalization of Chinese technology companies.
DOI: 10.17632/psz2jpxbgt.1
title: LINF_080017200

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/pknw8xp45v.1
title: LINF_090005000

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/cw4d3c5yhy.1
title: LINF_090005300

description: Tetratricopeptide repeat domain-containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/t3v7c836v5.1
title: Data-OF

description: Effect of organic fertilization on soil pH
DOI: 10.17632/63pv25n6yj.1
title: LINF_090005400

description: Peroxin 11 | PEX11; Leishmania infantum (strain JPCM5)
DOI: 10.17632/hf88ddrbx6.1
title: LINF_090006000

description: RNA-binding protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/6dxb9g44px.1
title: Quantum Music Interaction System and Work Evaluation Questionnaire Survey Database

description: The "Quantum Music Interaction System and Work Evaluation Questionnaire Survey Database" was constructed to comprehensively assess the performance of a music interaction generation system based on quantum computing and the quality of its related works. The survey design is scientifically rigorous, with the sample selected from university students in Jinan, Shandong Province, China. From September 15 to December 15, 2024, in strict adherence to the principles of the Declaration of Helsinki, 251 volunteers were recruited, and a total of 242 valid questionnaires were ultimately collected. The questionnaire was designed using the online platform Wenjuanxing and was divided into preparation, trial, and analysis phases to collect data comprehensively, which was subsequently analyzed using SPSS software. The questionnaire materials encompass three main modules: "Basic Information Survey," "System Experience Evaluation," and "Work Quality Evaluation," with investigations conducted across multiple dimensions such as age, gender, and musical background. The system's performance was quantified using a seven-axis evaluation mechanism, while the quality of the works was assessed using a six-dimensional evaluation framework. Moreover, based on the 22 questions in the questionnaire, targeted research hypotheses were proposed—namely, that the tested university student sample is broadly representative, the system's seven-axis interactive control evaluation indicators are robust, and the evaluation results of the two quantum music works are similar—thus providing clear guidance for subsequent analysis and discussion. The database contains four components: the questionnaire survey form, QR codes, an Excel file of the questionnaire results, and the SPSS project file.
DOI: 10.5281/zenodo.14930025
title: The effect of self-depletion on memory integration: the role of positive emotions
DOI: 10.5281/zenodo.14930026
title: The effect of self-depletion on memory integration: the role of positive emotions
DOI: 10.17632/v862gyskr3.1
title: CFC outputs LT and CW

description: We conducted a field test where participants completed the two theoreticalyl strongest carbon footprint calculators (CFCs), calculating their individual, annual CO2-eq emissions. These data shows the outputs of those two CFCs by category and subcategory. All outputs are expressed as tonnes of CO2-eq. 
While the totals are notably similar, variances are seen in categories and subcategories. These variences suggest different underlying methodologies, despite a similar overall total. This may cause confusion for users who want to reduce their emissions based on their largest emissions categories.
DOI: 10.15468/dl.4nge3w
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is South Africa",
 "OccurrenceStatus is Present",
 "TaxonKey is Corchorus olitorius L."
 ]
}

 The dataset includes 26 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000157-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/cjfn8fg8yt.1
title: MARGO-D-24-00269

description: Data of Marine Geology article : Coral reef boulders as recorders of cyclone activity over the last 3,500 years (Fakarava Atoll, Tuamotu, French Polynesia)

Table 1. Uranium-thorium dating of selected coral samples from top surface reef boulders, south-eastern reef rim, Fakarava Atoll. Ages are given in calendar years before present (cal. yr BP), relative to 2024 and in years BC (Before Jesus Christ) or AD (Anno Domini). Age discrepencies betwwen the dated coral samples are given. See Table S1 (supplementary mayerial) for error ranges associated with ages.

Table 2. Morphometric attributes (length, width, height, volume, weight and distance of location relative to the reef front line) of the 19 U/Th dated reef boulders, south-eastern reef rim, Fakarava Atoll, partly based on photogrammetric imagery analysis.

Table 3. Pre-displacement setting and flow velocities (m/s) of four selected boulders, from the south-eastern reef-rim side, Fakarava Atoll (north-west Tuamotu). As the bed angle of the boulders at the pre-displacement location was assumed to be θ=0, there is no difference between the values of lifting motion for both joint-bounded and non-joint bounded boulders 

Table S1. Supplementary material: uranium–thorium dating of coral samples extracted from reef boulders, south-eastern reef rim, Fakarava Atoll. Are given successively laboratory sample codes, field sample (F, FAK) numbers, uranium and thorium contents, isotopic composition with statistical errors – two standard deviations of the mean – and ages. Uranium and thorium contents and isotopic ratios δ234UM = {[(234U/238U)sample / (234U/238U)eq]-1} x 1000, where (234U/238U) sample is the measured atomic ratio and (234U/238U)eq is the atomic ratio at secular equilibrium. δ234U(T) is the initial value and is calculated by the equation: δ234U(0) = δ234Umeans exp(λ234t), where t is the age in years and λ234 is the decay constant for 234U. Ages were corrected for authigenic 230Th using 230Th/232Th) of the detrital fraction = 10 ± 5. Ages are expressed in both calendar years before present (BP), relative to 2024, corrected for detrital content using a 230Th/232Th isotopic ratio = 7 ± 50% and in conventional ages BP (Before Present, relative to 1950).