cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/9v7bxv3dcc.1
title: AITouch - Data

description: Touch-based interaction data were captured for 37 volunteers through an ad-hoc mobile application developed to this purpose. Different scenes were proposed so that the most common gestures were captured in order to study behavioral patterns, mostly oriented to authentication purposes. Every user was appropriately informed about the information gathered and its intended use, requesting their consent. During the data acquisition process itself, all the users used the same device, a Samsung Tab S4, to guarantee that the data was not dependent on the device used. Moreover, to ensure that the user sticks to the guidelines provided and to guarantee the quality of the data being gathered, we monitored every user during such a process.
On the one hand, raw data is available in JSON format, which includes essentially positions, timestamps and contact surface measurements for each active finger. This results in around 24,000 events (700,000 individual measurements) as they were recorded by the application, with no further processing.
On the other hand, we also provide some features derived from motion and geometric characteristics, which have already been sanitized (spurious or outlier samples were removed) and preprocessed (average values are provided for each event, rather than for individual measurements). In addition, the code used to extract and sanitize such features is also available so that the user can reproduce, adapt or even extend them to conduct their own research.
DOI: 10.15468/dl.xnxqsy
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oxalis corniculata L.",
 "Country is Iran (Islamic Republic of)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 33 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081571-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/85k77s9x6t
title: Elemental and Mg isotope geochemistry in Svalbard

description: This dataset report elemental and Mg isotope compositions in meltwater, suspended sediment and bedrock samples collected from Svalbard.
DOI: 10.17632/85k77s9x6t.1
title: Elemental and Mg isotope geochemistry in Svalbard

description: This dataset report elemental and Mg isotope compositions in meltwater, suspended sediment and bedrock samples collected from Svalbard.
DOI: 10.15468/dl.mt25y3
title: Occurrence Download

description: A dataset listing the 1704 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-43.51184 -19.1629,-43.49783 -19.20245,-43.50211 -19.22386,-43.52923 -19.26754,-43.52902 -19.29881,-43.51847 -19.31814,-43.52096 -19.34476,-43.48653 -19.37329,-43.48363 -19.43191,-43.44859 -19.44184,-43.39898 -19.4422,-43.38184 -19.43185,-43.30867 -19.4347,-43.25228 -19.41186,-43.23586 -19.38866,-43.18839 -19.39009,-43.14717 -19.33606,-43.10023 -19.3287,-43.09345 -19.30764,-43.0657 -19.26687,-43.0003 -19.27123,-42.94827 -19.24194,-42.96422 -19.18225,-42.95279 -19.16155,-42.95708 -19.13942,-42.99277 -19.12015,-42.99634 -19.09445,-43.03417 -19.08446,-43.07415 -19.05662,-43.10199 -19.00736,-43.15196 -18.97024,-43.16623 -18.94597,-43.17694 -18.89672,-43.20407 -18.87959,-43.20556 -18.84132,-43.19271 -18.80992,-43.22905 -18.75466,-43.26474 -18.71469,-43.30329 -18.69185,-43.30526 -18.66838,-43.33451 -18.66532,-43.35832 -18.6443,-43.39807 -18.62443,-43.40465 -18.59191,-43.38752 -18.55122,-43.36753 -18.52766,-43.37538 -18.5091,-43.37824 -18.447,-43.38752 -18.44343,-43.42597 -18.46814,-43.4431 -18.51526,-43.42834 -18.53905,-43.44391 -18.56835,-43.47166 -18.58087,-43.47487 -18.59728,-43.51703 -18.61846,-43.50341 -18.65314,-43.50544 -18.67801,-43.48522 -18.7341,-43.51734 -18.77081,-43.53494 -18.77462,-43.58302 -18.74898,-43.5931 -18.77122,-43.59576 -18.80707,-43.61661 -18.84084,-43.65648 -18.83879,-43.65924 -18.86145,-43.64544 -18.8766,-43.64761 -18.90219,-43.66856 -18.90641,-43.68924 -18.92897,-43.6881 -18.94416,-43.63453 -19.0156,-43.61617 -19.09838,-43.56763 -19.12114,-43.55406 -19.13788,-43.55014 -19.16546,-43.51184 -19.1629))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 2013-2025"
 ]
}

 The dataset's 1704 records were derived from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081560-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.44xz5z
title: Occurrence Download

description: A dataset containing 611 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Italy",
 "GadmGid is ITA.1_1",
 "TaxonKey is one of (Ephemeroptera, Trichoptera, Plecoptera)"
 ]
}

 The dataset includes 611 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081575-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/9cc8k5v7w4.2
title: BanglaMood: A Rich Audio Dataset for Eight Emotion Recognition in Bangla Speech

description: The BanglaMOOD dataset comprises 3,220 audio files in .wav format, each capturing vocal expressions of human emotion in Bangla. The dataset features eight emotion categories: Angry, Happy, Sad, Neutral, Surprised, Fear, Disappointment, and Excitement. Each audio sample ranges from 1 to 5 seconds in duration, with all files standardized to 5 seconds for consistency in feature extraction. The audio was collected from a wide variety of real-life contexts, including short stories, vlogs, and sports highlights sourced from YouTube, ensuring a broad range of expressive and natural vocalizations. Both male and female voices are included to provide gender diversity. In terms of distribution, the Neutral emotion has the highest number of samples, followed by Angry, while Excitement has the lowest. Specifically, there are 302 Happy samples (185 male and 117 female), 336 Sad samples (152 male and 184 female), 630 Angry samples (421 male and 209 female), 693 Neutral samples (549 male and 144 female), 285 Surprise samples (195 male and 90 female), 391 Fear samples (288 male and 103 female), 330 Disappointment samples (200 male and 130 female), and 253 Excitement samples (200 male and 53 female). In most categories, male voices are more prevalent, except for the Sad emotion, where female samples are more numerous. For the Angry emotion, female voices have the highest number of samples. The dataset is accompanied by visualizations, including a pie chart illustrating the proportion of each emotion class and a bar chart showing the gender distribution for each emotion. All audio files are standardized to a duration of 5 seconds. Overall, the BanglaMOOD dataset offers a diverse and balanced collection of emotional speech samples in Bangla, making it a valuable resource for research and development in speech emotion recognition and related fields.
DOI: 10.17632/8sxvyv96zz
title: FMRP drives mRNP targets into translationally silenced complexes

description: Fragile X Syndrome (FXS) is due to a deficiency in the ubiquitously expressed RNA-binding protein FMRP. While the mechanism of FMRP-mediated translational repression has been attributed primarily to ribosome stalling, we show using immunoprecipitations and polysome profiling of nonpolar- and polar-cell lysates, together with LC-MS/MS analyses, that FMRP largely represses translation initiation by associating with granule constituents to preclude 40S ribosomal subunit binding. We show that FMRP associates with its target mRNAs by binding directly to eIF4E at the 5'-cap in competition with eIF4G1 binding to eIF4E, and that Ataxin-2-Like promotes FMRP binding to the transcribed body. Together with previous data demonstrating that FMRP binds directly to 3'-poly(A)-bound PABPC1, FMRP protects its target mRNAs from translation and decay. The KH1+KH2 domains of FMRP are critical for the co-immunoprecipitation of eIF4E, mRNA targets, Ataxin-2-Like and PABPC1. Our findings supplement FMRP-mediated ribosome-stalling data, suggesting that FMRP largely mediates the sequestration of its mRNA targets from translation initiation and degradation in a network of FMRP molecules that simultaneously associate with cap-bound eIF4E, GC-rich mRNA regions, and poly(A)-bound PABPC1.
DOI: 10.17632/8sxvyv96zz.1
title: FMRP drives mRNP targets into translationally silenced complexes

description: Fragile X Syndrome (FXS) is due to a deficiency in the ubiquitously expressed RNA-binding protein FMRP. While the mechanism of FMRP-mediated translational repression has been attributed primarily to ribosome stalling, we show using immunoprecipitations and polysome profiling of nonpolar- and polar-cell lysates, together with LC-MS/MS analyses, that FMRP largely represses translation initiation by associating with granule constituents to preclude 40S ribosomal subunit binding. We show that FMRP associates with its target mRNAs by binding directly to eIF4E at the 5'-cap in competition with eIF4G1 binding to eIF4E, and that Ataxin-2-Like promotes FMRP binding to the transcribed body. Together with previous data demonstrating that FMRP binds directly to 3'-poly(A)-bound PABPC1, FMRP protects its target mRNAs from translation and decay. The KH1+KH2 domains of FMRP are critical for the co-immunoprecipitation of eIF4E, mRNA targets, Ataxin-2-Like and PABPC1. Our findings supplement FMRP-mediated ribosome-stalling data, suggesting that FMRP largely mediates the sequestration of its mRNA targets from translation initiation and degradation in a network of FMRP molecules that simultaneously associate with cap-bound eIF4E, GC-rich mRNA regions, and poly(A)-bound PABPC1.
DOI: 10.5519/qd.zck80e8a
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2182 records)
DOI: 10.17632/39tbt2jd7r
title: Egyptian Sign Language

description: This dataset contains raw video recordings of 55 commonly used Egyptian Sign Language (ESL) words, captured using a high-resolution camera for machine learning and computer vision research. Each video demonstrates a single ESL sign performed by a native signer under consistent lighting and background conditions.

The goal of this dataset is to support the development and benchmarking of vision-based sign language recognition systems, especially those targeting Arabic-speaking communities.

📚 Key Features:
55 distinct ESL classes, representing everyday words and phrases frequently used in Egyptian culture.
10 video samples per class, totaling 550 labeled videos.
Filmed using a Logitech HD Webcam at 720p resolution and 30 FPS.
The dataset includes natural signer variations (slight movement differences), supporting real-world model training.
DOI: 10.17632/39tbt2jd7r.2
title: Egyptian Sign Language

description: This dataset contains raw video recordings of 55 commonly used Egyptian Sign Language (ESL) words, captured using a high-resolution camera for machine learning and computer vision research. Each video demonstrates a single ESL sign performed by a native signer under consistent lighting and background conditions.

The goal of this dataset is to support the development and benchmarking of vision-based sign language recognition systems, especially those targeting Arabic-speaking communities.

📚 Key Features:
55 distinct ESL classes, representing everyday words and phrases frequently used in Egyptian culture.
10 video samples per class, totaling 550 labeled videos.
Filmed using a Logitech HD Webcam at 720p resolution and 30 FPS.
The dataset includes natural signer variations (slight movement differences), supporting real-world model training.
DOI: 10.5281/zenodo.15776772
title: Thematic RGB maps of surface mineralogy from Aramo (Spain) pilot site

description: Product description



Semi-automatically produced thematic RGB maps of surface mineralogy from visible and near-infrared (VNIR) - short-wave infrared (SWIR) data.





Mining life-cycle phase



Exploration (land)





Pilot site



Aramo (Spain)





Partner in charge



VTT   





Contact persons



Marko Savolainen





Data format



Raster, documents & Binary Interleave (BIL) format, ENVI readable spectral data cube





Reference system



EPSG:32630 - WGS 84 / UTM zone 30N





New/existing dataset



New





Link to Metadata



In preparation





Link to Zenodo



In preparation
DOI: 10.5281/zenodo.15776773
title: Thematic RGB maps of surface mineralogy from Aramo (Spain) pilot site

description: Product description



Semi-automatically produced thematic RGB maps of surface mineralogy from visible and near-infrared (VNIR) - short-wave infrared (SWIR) data.





Mining life-cycle phase



Exploration (land)





Pilot site



Aramo (Spain)





Partner in charge



VTT   





Contact persons



Marko Savolainen





Data format



Raster, documents & Binary Interleave (BIL) format, ENVI readable spectral data cube





Reference system



EPSG:32630 - WGS 84 / UTM zone 30N





New/existing dataset



New





Link to Metadata



In preparation





Link to Zenodo



In preparation
DOI: 10.5281/zenodo.15776785
title: Revistas de concurso BUCLM 2025
DOI: 10.15468/dl.nzc4x4
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oxalis corniculata L.",
 "Country is Malaysia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 46 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081577-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28607818
title: A deep dive into four thyroglobulin immunoassays from analytical perspective

description: Serum thyroglobulin immunometric assays (sTg) are crucial for monitoring differentiated thyroid cancer (DTC) treatment. However, challenges such as anti-thyroglobulin autoantibodies (TgAb) and assay variability hinder evaluations. This study assessed four sTg methods—three second-generation (Architect, Access, Elecsys) and one first-generation (Immulite)—following Clinical and Laboratory Standards Institute (CLSI) and American Thyroid Association (ATA) guidelines. The study compared sTg<sub>(Architect)</sub>, sTg<sub>(Access)</sub>, sTg<sub>(Elecsys)</sub>, and sTg<sub>(Immulite)</sub>. Precision was evaluated per CLSI EP05-A3, while the lower limits of detection (LLD) were assessed using EP17-A2. Passing–Bablok and Bland–Altman analyses were conducted as per EP09c, and semi-quantitative comparisons used Kappa statistics. The second-generation sTgs (Architect, Access, Elecsys) exhibited satisfactory precision (&lt;7% coefficient of variation, CV%), unlike sTg<sub>(Immulite)</sub>, which showed significant deviations and inadequate sensitivity for DTC recurrence (Limit of quantitation, LoQ = 4.59 μg/L). Second-generation sTgs had strong correlations (<i>r</i> &gt; 0.884) across all concentration ranges (≤1, 1-10, &gt;10 μg/L), with biases (slope: 1.131-2.027). sTg<sub>(Immulite)</sub> correlated well with second-generation methods for concentrations &gt;10 μg/L (<i>r</i> &gt; 0.945) but less so for &lt;10 μg/L (<i>r</i> &lt; 0.642). TgAb significantly impacted sTg<sub>(Immulite)</sub>. Kappa statistics revealed strong agreement among second-generation methods (κ &gt; 0.800) but lower concordance with sTg<sub>(Immulite),</sub> especially in TgAb(+) samples (κ: 0.562-0.653). Agreement ratios were high for second-generation methods (0.667-1.000) but variable for sTg<sub>(Immulite)</sub>, particularly at lower concentrations and in TgAb(+) cases (0.097-0.727). sTg<sub>(Immulite)</sub> did not meet LLD and precision criteria for DTC monitoring, facing issues with TgAb interference. Second-generation sTgs demonstrated consistent performance across all concentrations.
DOI: 10.6084/m9.figshare.28607818.v1
title: A deep dive into four thyroglobulin immunoassays from analytical perspective

description: Serum thyroglobulin immunometric assays (sTg) are crucial for monitoring differentiated thyroid cancer (DTC) treatment. However, challenges such as anti-thyroglobulin autoantibodies (TgAb) and assay variability hinder evaluations. This study assessed four sTg methods—three second-generation (Architect, Access, Elecsys) and one first-generation (Immulite)—following Clinical and Laboratory Standards Institute (CLSI) and American Thyroid Association (ATA) guidelines. The study compared sTg<sub>(Architect)</sub>, sTg<sub>(Access)</sub>, sTg<sub>(Elecsys)</sub>, and sTg<sub>(Immulite)</sub>. Precision was evaluated per CLSI EP05-A3, while the lower limits of detection (LLD) were assessed using EP17-A2. Passing–Bablok and Bland–Altman analyses were conducted as per EP09c, and semi-quantitative comparisons used Kappa statistics. The second-generation sTgs (Architect, Access, Elecsys) exhibited satisfactory precision (&lt;7% coefficient of variation, CV%), unlike sTg<sub>(Immulite)</sub>, which showed significant deviations and inadequate sensitivity for DTC recurrence (Limit of quantitation, LoQ = 4.59 μg/L). Second-generation sTgs had strong correlations (<i>r</i> &gt; 0.884) across all concentration ranges (≤1, 1-10, &gt;10 μg/L), with biases (slope: 1.131-2.027). sTg<sub>(Immulite)</sub> correlated well with second-generation methods for concentrations &gt;10 μg/L (<i>r</i> &gt; 0.945) but less so for &lt;10 μg/L (<i>r</i> &lt; 0.642). TgAb significantly impacted sTg<sub>(Immulite)</sub>. Kappa statistics revealed strong agreement among second-generation methods (κ &gt; 0.800) but lower concordance with sTg<sub>(Immulite),</sub> especially in TgAb(+) samples (κ: 0.562-0.653). Agreement ratios were high for second-generation methods (0.667-1.000) but variable for sTg<sub>(Immulite)</sub>, particularly at lower concentrations and in TgAb(+) cases (0.097-0.727). sTg<sub>(Immulite)</sub> did not meet LLD and precision criteria for DTC monitoring, facing issues with TgAb interference. Second-generation sTgs demonstrated consistent performance across all concentrations.
DOI: 10.6084/m9.figshare.28445586
title: Assessment of large language models in medical quizzes for clinical chemistry and laboratory management: implications and applications for healthcare artificial intelligence

description: Large language models (LLMs) have demonstrated high performance across various fields due to their ability to understand, generate, and manipulate human language. However, their potential in specialized medical domains, such as clinical chemistry and laboratory management, remains underexplored. This study evaluated the performance of nine LLMs using zero-shot prompting on 109 clinical problem-based quizzes from peer-reviewed journal articles in the Laboratory Medicine Online (LMO) database. These quizzes covered topics in clinical chemistry, toxicology, and laboratory management. The models, including GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro, along with their earlier or smaller versions, were assigned roles as clinical chemists or laboratory managers to simulate real-world decision-making scenarios. Among the evaluated models, GPT-4o achieved the highest overall accuracy, correctly answering 81.7% of the quizzes, followed by GPT-4 Turbo (76.1%), Claude 3 Opus (74.3%), and Gemini 1.5 Pro (69.7%), while the lowest performance was observed with Gemini 1.0 Pro (51.4%). GPT-4o performed exceptionally well across all quiz types, including single-select, open-ended, and multiple-select questions, and demonstrated particular strength in quizzes involving figures, tables, or calculations. These findings highlight the ability of LLMs to effectively apply their pre-existing knowledge base to specialized clinical chemistry inquiries without additional fine-tuning. Among the evaluated models, GPT-4o exhibited superior performance across different quiz types, underscoring its potential utility in assisting healthcare professionals in clinical decision-making.
DOI: 10.6084/m9.figshare.28445586.v1
title: Assessment of large language models in medical quizzes for clinical chemistry and laboratory management: implications and applications for healthcare artificial intelligence

description: Large language models (LLMs) have demonstrated high performance across various fields due to their ability to understand, generate, and manipulate human language. However, their potential in specialized medical domains, such as clinical chemistry and laboratory management, remains underexplored. This study evaluated the performance of nine LLMs using zero-shot prompting on 109 clinical problem-based quizzes from peer-reviewed journal articles in the Laboratory Medicine Online (LMO) database. These quizzes covered topics in clinical chemistry, toxicology, and laboratory management. The models, including GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro, along with their earlier or smaller versions, were assigned roles as clinical chemists or laboratory managers to simulate real-world decision-making scenarios. Among the evaluated models, GPT-4o achieved the highest overall accuracy, correctly answering 81.7% of the quizzes, followed by GPT-4 Turbo (76.1%), Claude 3 Opus (74.3%), and Gemini 1.5 Pro (69.7%), while the lowest performance was observed with Gemini 1.0 Pro (51.4%). GPT-4o performed exceptionally well across all quiz types, including single-select, open-ended, and multiple-select questions, and demonstrated particular strength in quizzes involving figures, tables, or calculations. These findings highlight the ability of LLMs to effectively apply their pre-existing knowledge base to specialized clinical chemistry inquiries without additional fine-tuning. Among the evaluated models, GPT-4o exhibited superior performance across different quiz types, underscoring its potential utility in assisting healthcare professionals in clinical decision-making.
DOI: 10.17632/jrbx34k77g
title: Mushroom Disease Dataset (Healthy, Single Infected & Mixed Infected)

description: Mushroom cultivation bags, categorized into healthy, single-infected, and mixed-infected classes, were photographed at the Mushroom Development Institute in Savar, Dhaka, Bangladesh. The dataset comprises a total of 761 high-resolution images, including 299 healthy samples, 147 single-infected samples, and 315 mixed-infected samples. The infected images depict contamination caused by green mold, black mold, or a combination of both. The mixed-infected category represents bags affected by multiple pathogens or overlapping infection patterns, typically involving both types of mold.
All images were originally captured in HEIC format using an iPhone 11 Pro Max and were later converted to JPG format for standardization and broader compatibility.
DOI: 10.17632/jrbx34k77g.2
title: Mushroom Disease Dataset (Healthy, Single Infected & Mixed Infected)

description: Mushroom cultivation bags, categorized into healthy, single-infected, and mixed-infected classes, were photographed at the Mushroom Development Institute in Savar, Dhaka, Bangladesh. The dataset comprises a total of 761 high-resolution images, including 299 healthy samples, 147 single-infected samples, and 315 mixed-infected samples. The infected images depict contamination caused by green mold, black mold, or a combination of both. The mixed-infected category represents bags affected by multiple pathogens or overlapping infection patterns, typically involving both types of mold.
All images were originally captured in HEIC format using an iPhone 11 Pro Max and were later converted to JPG format for standardization and broader compatibility.
DOI: 10.15468/dl.6bjg63
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oxalis corniculata L.",
 "Country is Marshall Islands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081578-250525065834625/datasets/export for details.