cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18607527
title: NGS data: Coordinated histone acetylation and methylation establish cell type-specific transcriptional programs in the mouse airway epithelium.

description: This dataset contains RNA sequencing (RNA-seq) data generated from Mouse Tracheal Epithelial Cells (MTECs) derived from the airway epithelium and cultured at the air–liquid interface (ALI). The experiments evaluate transcriptional changes associated with BET inhibition, Dot1l inhibition, and Pitx1 knockdown (Pitx1-KD) at different stages of differentiation. In addition, raw ChIP sequencing (ChIP-seq) data with annotated peaks of H3K27ac, H3K79me3 and Brd3 in ALI 6 are provided. 

In the RNA-seq files provided, the following nomenclature was used for sample annotation:



"Control 4.1–4.3" correspond to untreated cells at ALI 4, whereas "Control 6.1–6.3" correspond to untreated cells at ALI 6.

“DMSO” represent controls for (+)-JQ1–treated cultures named "JQ1. In these experiments, cells were exposed to (+)-JQ1 from ALI 2 to ALI 6 and harvested at ALI 6.

“MOC” correspond to knockdown controls (Luc-KD) for the Pitx1-KD condition named "MOKD", at ALI 6 and ALI 14.

“MOD” represent DMSO-treated controls for cultures exposed to the Dot1l inhibitor SGC0946 (named "MOSGC") from ALI 2 to ALI 6,  and harvested at ALI 6.


Raw reads were pseudoaligned to Mus musculus (mm10) genome using kallisto. Counts correspond to unnormalized read numbers assigned to annotated genes and are suitable for downstream differential expression analyses.

We also provide Excel files containing gene classification according to differential expression status (up-regulated, down-regulated, or non-regulated) for the experimental conditions described above. In the file named “Genes UP-DOWN-NON regulated ALI4vsALI6_JQ1vsDMSO_ALI6,” genes are categorized based on their response to (+)-JQ1 treatment and are additionally annotated according of H3K27ac binding. In the file named “Genes UP-DOWN-NON regulated_SGC0946_ALI6,” genes are categorized based on their response to SGC0946 with annotation based on H3K79me3 biding. These tables are intended to facilitate integration of transcriptional changes with chromatin features and to support comparative or enrichment analyses.
DOI: 10.5281/zenodo.18611861
title: HOW AI IS TRANSFORMING THE FUTURE OF TECHNOLOGY

description: Artificial Intelligence (AI) is quickly changing the future of technology by altering how systems learn, adjust, and work with people. This article looks at the growing impact of AI in important tech areas like software development, healthcare, education, business, and smart infrastructure. By allowing data-driven decision-making, automation, and predictive analytics, AI improves efficiency, accuracy, and innovation on an unmatched scale. The study also looks at new trends such as machine learning, natural language processing, and generative AI, showing how they help create smart, personalized, and scalable solutions. Additionally, the article addresses ethical issues, data privacy challenges, and the need for responsible AI governance to support sustainable technological growth. Overall, AI stands as a key force behind future technological progress, changing industries and human-machine teamwork in the digital age.
DOI: 10.5281/zenodo.18611862
title: HOW AI IS TRANSFORMING THE FUTURE OF TECHNOLOGY

description: Artificial Intelligence (AI) is quickly changing the future of technology by altering how systems learn, adjust, and work with people. This article looks at the growing impact of AI in important tech areas like software development, healthcare, education, business, and smart infrastructure. By allowing data-driven decision-making, automation, and predictive analytics, AI improves efficiency, accuracy, and innovation on an unmatched scale. The study also looks at new trends such as machine learning, natural language processing, and generative AI, showing how they help create smart, personalized, and scalable solutions. Additionally, the article addresses ethical issues, data privacy challenges, and the need for responsible AI governance to support sustainable technological growth. Overall, AI stands as a key force behind future technological progress, changing industries and human-machine teamwork in the digital age.
DOI: 10.17632/265nm6v26d
title: Data and code: Terrestrial ecosystem nitrogen cycling in response to field warming: Global patterns and future trends

description: Nitrogen cycling regulates ecosystem productivity and carbon sequestration in terrestrial ecosystems, yet its response to climate warming remains uncertain. Here, we compiled the most comprehensive dataset to date, synthesizing 7,941 observations from 413 field warming experiments worldwide and combining them with random forest regression and Community Land Model (CLM) simulations.
DOI: 10.17632/265nm6v26d.1
title: Data and code: Terrestrial ecosystem nitrogen cycling in response to field warming: Global patterns and future trends

description: Nitrogen cycling regulates ecosystem productivity and carbon sequestration in terrestrial ecosystems, yet its response to climate warming remains uncertain. Here, we compiled the most comprehensive dataset to date, synthesizing 7,941 observations from 413 field warming experiments worldwide and combining them with random forest regression and Community Land Model (CLM) simulations.
DOI: 10.15468/dl.f4k7pp
title: Occurrence Download

description: A dataset containing 15216 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Solanum torvum",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 15216 records from 364 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008901-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/7f2h4tkp78
title: Beam data for Activation Benchmarks at the CERF facility at CERN

description: Beam data and averaged irradiation profiles evaluated for FLUKA simulations of the "Activation benchmarks at the CERF facility at CERN" paper.
DOI: 10.17632/7f2h4tkp78.1
title: Beam data for Activation Benchmarks at the CERF facility at CERN

description: Beam data and averaged irradiation profiles evaluated for FLUKA simulations of the "Activation benchmarks at the CERF facility at CERN" paper.
DOI: 10.25452/figshare.plus.30027313
title: Time-Synchronized Multi-Sensor Drone Dataset (TSMS-Drone)

description: To provide a publicly available data set for developing robust drone detection and classification systems, a multi-sensor dataset composed of time-synchronized signals from Frequency-Modulated Continuous Wave (FMCW) radar, Continuous Wave (CW) radar, and Radio Frequency (RF) receiver is provided. The dataset includes raw and processed signals collected from four different types of commercial drones and one non-drone target, and signals for different sensors are collected simultaneously. Measurements were taken across distances ranging from 2 to 30 meters in 2-meter intervals, with repeated trials under controlled conditions to ensure consistency. It contains both raw signals and processed representations—range-Doppler maps, Doppler spectrum, and power spectral densities—facilitating signal-level and image-based analysis. This time-synchronized dataset offers a reliable resource for developing and evaluating multimodal sensor-fusion strategies, distance-aware classification models, and advanced Artificial Intelligence (AI)-based detection algorithms, including supervised and self-supervised learning methods.
DOI: 10.25452/figshare.plus.30027313.v1
title: Time-Synchronized Multi-Sensor Drone Dataset (TSMS-Drone)

description: To provide a publicly available data set for developing robust drone detection and classification systems, a multi-sensor dataset composed of time-synchronized signals from Frequency-Modulated Continuous Wave (FMCW) radar, Continuous Wave (CW) radar, and Radio Frequency (RF) receiver is provided. The dataset includes raw and processed signals collected from four different types of commercial drones and one non-drone target, and signals for different sensors are collected simultaneously. Measurements were taken across distances ranging from 2 to 30 meters in 2-meter intervals, with repeated trials under controlled conditions to ensure consistency. It contains both raw signals and processed representations—range-Doppler maps, Doppler spectrum, and power spectral densities—facilitating signal-level and image-based analysis. This time-synchronized dataset offers a reliable resource for developing and evaluating multimodal sensor-fusion strategies, distance-aware classification models, and advanced Artificial Intelligence (AI)-based detection algorithms, including supervised and self-supervised learning methods.
DOI: 10.15468/dl.wpb6z3
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bulbophyllum lucidum",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 13 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008902-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/fs2r7zvmj3
title: PHQ-9 Annotated Social Media Posts Dataset for Automated Depression Severity Assessment

description: 1. Overview
This dataset contains a structured collection of social media posts annotated using the Patient Health Questionnaire-9 (PHQ-9) framework to support automated depression severity assessment through Natural Language Processing techniques. It has been developed to facilitate computational modeling of depression symptoms and severity levels based on textual expressions shared on publicly accessible platforms. The dataset is designed to support research in computational mental health, affective computing, and machine learning-driven psychological assessment.

2. Data Source and Collection
The textual data were collected from publicly accessible social media platforms. Only openly available posts were included, and no private messages or restricted content were accessed. During the collection process, all identifying information such as usernames, profile identifiers, and direct metadata were removed to ensure anonymity. The resulting dataset consists solely of cleaned textual content prepared for research purposes.

3. Annotation Framework
Each post was annotated according to the nine clinical criteria of the PHQ-9 instrument. These criteria include reduced interest or pleasure, depressed mood, sleep disturbances, fatigue, appetite changes, feelings of guilt or worthlessness, concentration difficulties, psychomotor changes, and suicidal ideation.

For each entry, symptom presence and intensity were evaluated. Individual symptom indicators were recorded, and a cumulative PHQ-9 score was calculated. Based on this total score, each post was categorized into standard severity levels: minimal, mild, moderate, moderately severe, or severe depression.

4. Data Structure
The dataset is provided in CSV format and includes structured variables such as a unique anonymized identifier, cleaned textual content, nine symptom-level indicators, total PHQ-9 score, categorical severity label, and numerical encoding where applicable.

5. Preprocessing Procedures
To ensure data quality and consistency, several preprocessing steps were applied. These include text normalization, removal of URLs and special characters, elimination of duplicate entries, and handling of missing values. No synthetic or artificially generated text has been included; all entries are derived from authentic social media content.

6. Research Applications
The dataset can be utilized for a variety of research purposes, including depression severity classification, symptom-level prediction, traditional machine learning modeling, deep learning approaches, transformer-based language models, and explainable artificial intelligence in mental health research.

It is intended strictly for academic and research use. Researchers are expected to follow ethical guidelines and institutional policies when using the dataset.
DOI: 10.17632/fs2r7zvmj3.1
title: PHQ-9 Annotated Social Media Posts Dataset for Automated Depression Severity Assessment

description: 1. Overview
This dataset contains a structured collection of social media posts annotated using the Patient Health Questionnaire-9 (PHQ-9) framework to support automated depression severity assessment through Natural Language Processing techniques. It has been developed to facilitate computational modeling of depression symptoms and severity levels based on textual expressions shared on publicly accessible platforms. The dataset is designed to support research in computational mental health, affective computing, and machine learning-driven psychological assessment.

2. Data Source and Collection
The textual data were collected from publicly accessible social media platforms. Only openly available posts were included, and no private messages or restricted content were accessed. During the collection process, all identifying information such as usernames, profile identifiers, and direct metadata were removed to ensure anonymity. The resulting dataset consists solely of cleaned textual content prepared for research purposes.

3. Annotation Framework
Each post was annotated according to the nine clinical criteria of the PHQ-9 instrument. These criteria include reduced interest or pleasure, depressed mood, sleep disturbances, fatigue, appetite changes, feelings of guilt or worthlessness, concentration difficulties, psychomotor changes, and suicidal ideation.

For each entry, symptom presence and intensity were evaluated. Individual symptom indicators were recorded, and a cumulative PHQ-9 score was calculated. Based on this total score, each post was categorized into standard severity levels: minimal, mild, moderate, moderately severe, or severe depression.

4. Data Structure
The dataset is provided in CSV format and includes structured variables such as a unique anonymized identifier, cleaned textual content, nine symptom-level indicators, total PHQ-9 score, categorical severity label, and numerical encoding where applicable.

5. Preprocessing Procedures
To ensure data quality and consistency, several preprocessing steps were applied. These include text normalization, removal of URLs and special characters, elimination of duplicate entries, and handling of missing values. No synthetic or artificially generated text has been included; all entries are derived from authentic social media content.

6. Research Applications
The dataset can be utilized for a variety of research purposes, including depression severity classification, symptom-level prediction, traditional machine learning modeling, deep learning approaches, transformer-based language models, and explainable artificial intelligence in mental health research.

It is intended strictly for academic and research use. Researchers are expected to follow ethical guidelines and institutional policies when using the dataset.
DOI: 10.15468/dl.ndfcqh
title: Occurrence Download

description: A dataset containing 97 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Callisthene major pilosa",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 97 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008903-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18611883
title: OLIY TA'LIM MUASSASALARI MOLIYAVIY TA'MINOTINI MUSTAHKAMLASHDA XALQARO GRANDLARDAN FOYDALANISH SAMARADORLIGI

description: Mazkur maqolada oliy ta’lim muassasalarining moliyaviy barqarorligini ta’minlashda xalqaro grantlardan foydalanish samaradorligi tahlil qilinadi. Tadqiqotda oliy ta’lim tizimini moliyalashtirish manbalari, xususan, xalqaro grantlar orqali qo‘shimcha mablag‘larni jalb etish mexanizmlari va ularning ta’lim sifati hamda ilmiy-tadqiqot faoliyatiga ta’siri o‘rganilgan. Shuningdek, rivojlangan davlatlar tajribasi asosida xalqaro grantlarni boshqarishning samarali usullari tahlil qilinib, mahalliy oliy ta’lim muassasalari uchun amaliy tavsiyalar ishlab chiqilgan. Tadqiqot natijalari oliy ta’lim muassasalarining moliyaviy mustaqilligini oshirish va raqobatbardoshligini kuchaytirishga xizmat qiladi.
DOI: 10.5281/zenodo.18611882
title: OLIY TA'LIM MUASSASALARI MOLIYAVIY TA'MINOTINI MUSTAHKAMLASHDA XALQARO GRANDLARDAN FOYDALANISH SAMARADORLIGI

description: Mazkur maqolada oliy ta’lim muassasalarining moliyaviy barqarorligini ta’minlashda xalqaro grantlardan foydalanish samaradorligi tahlil qilinadi. Tadqiqotda oliy ta’lim tizimini moliyalashtirish manbalari, xususan, xalqaro grantlar orqali qo‘shimcha mablag‘larni jalb etish mexanizmlari va ularning ta’lim sifati hamda ilmiy-tadqiqot faoliyatiga ta’siri o‘rganilgan. Shuningdek, rivojlangan davlatlar tajribasi asosida xalqaro grantlarni boshqarishning samarali usullari tahlil qilinib, mahalliy oliy ta’lim muassasalari uchun amaliy tavsiyalar ishlab chiqilgan. Tadqiqot natijalari oliy ta’lim muassasalarining moliyaviy mustaqilligini oshirish va raqobatbardoshligini kuchaytirishga xizmat qiladi.
DOI: 10.15468/dl.q2nffm
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corydoras fowleri",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 13 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008904-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u2s9mk
title: Occurrence Download

description: A dataset containing 11060 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 Bouteloua gracilis"
 ]
}

 The dataset includes 11060 records from 216 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008905-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.quebdz
title: Occurrence Download

description: A dataset containing 247934 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oxalis acetosella L.",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2010",
 "HasCoordinate is true"
 ]
}

 The dataset includes 247934 records from 693 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006866-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wmxcky
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Draba alajica Litv."
 ]
}

 The dataset includes 12 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0157285-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pkvmpy
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Draba aleutica E.Ekman ex Hultén"
 ]
}

 The dataset includes 30 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0157282-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rsayjx
title: Occurrence Download

description: A dataset containing 6637 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anser brachyrhynchus Baillon, 1834",
 "DatasetKey is Observation.org, Nature data from around the World",
 "Country is one of (Germany, France, Spain, Portugal, Denmark, Austria, Italy, Switzerland, Greece, Poland, Hungary, Slovenia, Sweden, Norway, Slovakia, Montenegro, Croatia, North Macedonia, Serbia, Iceland, Ireland, United Kingdom of Great Britain and Northern Ireland, Albania, Bosnia and Herzegovina, Bulgaria, Belgium, Netherlands)"
 ]
}

 The dataset includes 6637 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0177024-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3534ar
title: Occurrence Download

description: A dataset listing the 451577 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Fossil, Specimen, Living Specimen)",
 "PublishingOrg is 2cd829bb-b713-433d-99cf-64bef11e5b3e"
 ]
}

 The dataset's 451577 records were derived from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0220460-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xxbuyp
title: Occurrence Download

description: A dataset containing 1845 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hippocamelus bisulcus (Molina, 1782)"
 ]
}

 The dataset includes 1845 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0177030-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2ypyz9
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Pleurothallis saccatilabia C.Schweinf., Pleurothallis nelsonii Ames)"
 ]
}

 The dataset includes 56 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0157287-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.btmyce
title: Occurrence Download

description: A dataset containing 519690 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 0.0-72.0",
 "DatasetKey is iNaturalist Research-grade Observations",
 "IucnRedListCategory is LC",
 "Month is January",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 519690 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0174935-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.