cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/j9f93ffsxj.2
title: Identificación microbiológica de patógenos en aguas de centros educativos de Norte de Santander

description: Se plantea que la calidad microbiológica del agua de consumo en los centros educativos de Norte de Santander no cumple con los estándares establecidos en la normatividad vigente, presentando niveles elevados de contaminación por microorganismos patógenos e indicadores de contaminación fecal, lo que sugiere fallas en los procesos de almacenamiento y tratamiento del agua potable.
Descripción de los Datos y Metodología:Para evaluar la calidad microbiológica del agua de consumo, se recolectaron un total de 80 muestras en 20 centros educativos ubicados en tres municipios de Norte de Santander: Cúcuta, Pamplona y Los Patios. La recolección se realizó durante cuatro muestreos a lo largo de seis meses, aplicando el método de filtración por membrana para la identificación de bacterias aerobias mesófilas, Escherichia coli, coliformes totales y fecales, Pseudomonas spp. y Salmonella spp. según normas técnicas establecidas.
Adicionalmente, se realizaron mediciones in situ de parámetros fisicoquímicos del agua, incluyendo temperatura, pH y cloro residual, así como encuestas al personal de mantenimiento para evaluar las condiciones de almacenamiento del agua.

Hallazgos

Se identificó crecimiento de bacterias aerobias mesófilas en el 77,5% de las muestras.
La presencia de coliformes totales se registró en el 84% de las muestras y coliformes fecales en el 72%.
Escherichia coli estuvo presente en el 21% de las muestras analizadas.
Pseudomonas spp. se detectó en el 73% de las muestras, mientras que Salmonella spp. se halló en el 10,5%.

En el 70% de los centros educativos no se encontró cloro residual, lo que indica un deficiente proceso de desinfección del agua.

Solo un 22,5% de las muestras analizadas cumplió con el límite permisible de microorganismos según la normatividad colombiana.

En la mayoría de las instituciones evaluadas, el mantenimiento de los tanques de almacenamiento se realiza solo una vez al año, lo que contribuye al riesgo de contaminación microbiana.

. La presencia de coliformes y E. coli indica contaminación fecal reciente, lo que representa un riesgo significativo para la salud de los estudiantes y el personal educativo. Adicionalmente, la detección de Pseudomonas spp. y Salmonella spp. sugiere condiciones de almacenamiento deficientes y posible formación de biopelículas en las redes de distribución de agua.

La ausencia de cloro residual en la mayoría de las muestras confirma fallas en el proceso de potabilización, lo que permite la proliferación de microorganismos patógenos. Estos hallazgos resaltan la necesidad de implementar estrategias correctivas, tales como el mantenimiento regular de los sistemas de almacenamiento, la verificación constante de los niveles de desinfección y la educación sanitaria 
Este estudio enfatiza la importancia de la vigilancia microbiológica continua del agua de consumo en entornos escolares y la necesidad de medidas inmediatas para mitigar los riesgos asociados a la contaminación del recurso hídrico
DOI: 10.17632/r9g8jy7gjr.1
title: Noble gas data: Noble Gas Analysis to Identify Groundwater in Baltic Sea Sediments of the Mecklenburg Bay

description: Dataset accompanying the paper "Using Noble Gas Analysis to Identify Groundwater in Baltic Sea Sediments of the Mecklenburg Bay".
DOI: 10.17632/kjp99ctwrh.1
title: Data：Generational Engagement in Rural Revitalization A TPB-SEM Analysis of Chinese Youth with Evidence from Zhejiang Province

description: The research data were obtained from a multi-stage sampling survey conducted in Zhejiang Province during July-August 2024. The research team refined the questionnaire design through a pre-survey involving 50 youths each from Hangzhou and Ningbo. The formal investigation utilized stratified random sampling, with sample sizes allocated proportionately across 11 prefecture-level cities based on population data from China’s Seventh National Population Census (2020), while controlling for gender, generational cohort (Y/Z generations), and urban-rural distribution. Ultimately, 1,211 youths were randomly selected from 44 counties (cities/districts), yielding 1,119 valid questionnaires (92.40% recovery rate), including 620 from Gen Z and 499 from Gen Y. Data analysis indicated that Gen Y demonstrated significantly higher willingness to participate compared to Gen Z (63.93% vs. 53.23%).
DOI: 10.17632/tvf9nwtdhn.1
title: Raw data for manuscript "Heterotypic droplet formation by pro-inflammatory S100A9 and neurodegenerative disease-related alpha-synuclein"

description: Raw data for manuscript "Heterotypic droplet formation by pro-inflammatory S100A9 and neurodegenerative disease-related alpha-synuclein". Fluorescence microscopy images, brightfield microscopy images, LLPS and aggregation kinetics, Cryo-EM data.
DOI: 10.17632/tvf9nwtdhn.2
title: Raw data for manuscript "Heterotypic droplet formation by pro-inflammatory S100A9 and neurodegenerative disease-related alpha-synuclein"

description: Raw data for manuscript "Heterotypic droplet formation by pro-inflammatory S100A9 and neurodegenerative disease-related alpha-synuclein". Fluorescence microscopy images, brightfield microscopy images, LLPS and aggregation kinetics, Cryo-EM data.
DOI: 10.17632/5xss424m4p.1
title: Technical and economic data for energy market and capacity payment optimisation in the Dominican Republic's power market

description: A dataset that can be used in an optimisation model of the energy market and the capacity payment in the power market of the Dominican Republic. It can be used to determine the real contribution of variable renewable energy (VRE) to the security of supply, and develop methodologies to calculate de capacity value/credit of VRE. The file in the repository is an Excel workbook called "Dominican Republic power market data".
DOI: 10.17632/5xss424m4p.2
title: Technical and economic data for energy market and capacity payment optimisation in the Dominican Republic's power market

description: A dataset that can be used in an optimisation model of the energy market and the capacity payment in the power market of the Dominican Republic. It can be used to determine the real contribution of variable renewable energy (VRE) to the security of supply, and develop methodologies to calculate de capacity value/credit of VRE. The file in the repository is an Excel workbook called "Dominican Republic power market data".
DOI: 10.17632/5xss424m4p.3
title: Technical and economic data for energy market and capacity payment optimisation in the Dominican Republic's power market

description: A dataset that can be used in an optimisation model of the energy market and the capacity payment in the power market of the Dominican Republic. It can be used to determine the real contribution of variable renewable energy (VRE) to the security of supply, and develop methodologies to calculate de capacity value/credit of VRE. The file in the repository is an Excel workbook called "Dominican Republic power market data".
DOI: 10.17632/7m33zskp2b.1
title: Concentrations, radiocarbon, stable carbon isotope values of dissolved organic carbon in Kongsfjorden (2023)

description: Concentrations, radiocarbon (Δ¹⁴C), and stable carbon isotope values (δ¹³C) of dissolved organic carbon (DOC) collected from Kongsfjorden in 2023, including station information.
DOI: 10.17632/7m33zskp2b.2
title: Concentrations, radiocarbon, stable carbon isotope values of dissolved organic carbon in Kongsfjorden (2023)

description: Concentrations, radiocarbon (Δ¹⁴C), and stable carbon isotope values (δ¹³C) of dissolved organic carbon (DOC) collected from Kongsfjorden in 2023, including station information. Radiocarbon data from UC Irvine are included.
DOI: 10.17632/7m33zskp2b.3
title: Concentrations, radiocarbon, stable carbon isotope values of dissolved organic carbon in Kongsfjorden (2023)

description: Concentrations, radiocarbon (Δ¹⁴C), and stable carbon isotope values (δ¹³C) of dissolved organic carbon (DOC) collected from Kongsfjorden in 2023, including station information. Radiocarbon data from UC Irvine are included.
DOI: 10.17632/7m33zskp2b.4
title: Concentrations, radiocarbon, stable carbon isotope values of dissolved organic carbon in Kongsfjorden (2023)

description: Concentrations, radiocarbon (Δ¹⁴C), and stable carbon isotope values (δ¹³C) of dissolved organic carbon (DOC) collected from Kongsfjorden in 2023, including station information. Radiocarbon data from UC Irvine are included.
DOI: 10.17632/48x9xm7rkf.1
title: IDTheftCase-JudgmentCorpus: Indonesian Theft Case Judgment Corpus - Levels of Court

description: IDTheftCase-JudgmentCorpus: Indonesian Theft Case Judgment Corpus – Levels of Court is a dataset containing the full-text documents of written judgments handed down by Indonesian courts in criminal theft cases at three levels: the court of first instance, the appellate court, and the cassation court. The dataset was created to support research and development activities in information extraction and natural language processing, specifically about the processing and understanding the legal texts and court documents.

The dataset includes the full text of judgments with information about defendants, judges, types of punishment, hearing dates, and other relevant data for analysis. The dataset is organized into several files:
1. Manually Annotated Judgment Documents
 • 1-pertama.json: Annotated judgments from the court of first instance.
 • 2-banding.json: Annotated judgments from the appellate court.
 • 3-kasasi.json: Annotated judgments from the cassation court.
2. Non-Annotated Judgment Documents
 • 1-pertama-not-annotated.json: Non-annotated judgments from the court of first instance.
3. Metadata File metadata.csv : Contains contextual and hierarchical information about the judgment documents, structured into the following columns:
 • Id: Unique case identifier.
 • Id Putusan: Original ID of the document, distinguishing records from the Supreme Court’s website.
 • Tingkat Proses: Indicates the court level (First Instance, Appellate, Cassation).
 • Jenis Lembaga Pengadilan: Type of judicial institution handling the case (e.g., PN, PT, MA).
 • Lembaga Pengadilan: Name of the judicial institution.
 • Tahun: The year the judgment was issued.
 • Inckrah: Indicates the case’s final resolution level (First Instance, Appellate, or Cassation).
 • Amar: Type of decision issued (e.g., acquittal, conviction).
 • Id Pertama: Related first-instance document ID.
 • Id Banding: Related appellate document ID.
 • Id Kasasi: Related cassation document ID.

All documents in this dataset were obtained from public records on the official website of the Supreme Court of the Republic of Indonesia (https://putusan3.mahkamahagung.go.id/). As such, the dataset represents real-world cases and reflects the legal form of Indonesian court documents.

IDTheftCase-JudgmentCorpus is an essential dataset for research in named entity recognition and extraction, punishment imposition pattern analysis, and automatic document classification in the Indonesian legal context. Moreover, the dataset is useful for developers and researchers who aim to build and implement machine learning-based models to extract, group, and analyze judgment documents at different court levels.
DOI: 10.17632/48x9xm7rkf.2
title: IDTheftCase-JudgmentCorpus: Indonesian Theft Case Judgment Corpus - Levels of Court

description: IDTheftCase-JudgmentCorpus: Indonesian Theft Case Judgment Corpus – Levels of Court is a dataset containing the full-text documents of written judgments handed down by Indonesian courts in criminal theft cases at three levels: the court of first instance, the appellate court, and the cassation court. The dataset was created to support research and development activities in information extraction and natural language processing, specifically about the processing and understanding the legal texts and court documents.

The dataset includes the full text of judgments with information about defendants, judges, types of punishment, hearing dates, and other relevant data for analysis. The dataset is organized into several files:
1. Manually Annotated Judgment Documents (annotated)
 • Json Files:
 o 1-pertama.json: Annotated judgments from the court of first instance.
 o 2-banding.json: Annotated judgments from the appellate court.
 o 3-kasasi.json: Annotated judgments from the cassation court.
 • CSV Files:
 o 1-pertama.csv: Tokenized version of the first-instance court judgments from the related JSON file, with corresponding BIO tagging for each token.
 o 2-banding.csv: Tokenized version of the appellate court judgments from the related JSON file, with corresponding BIO tagging for each token.
 o 3-kasasi.csv: Tokenized version of the cassation court judgments from the related JSON file, with corresponding BIO tagging for each token.

2. Non-Annotated Judgment Documents
 • 1-pertama-not-annotated.json: Non-annotated judgments from the court of first instance.
3. Metadata File metadata.csv : Contains contextual and hierarchical information about the judgment documents, structured into the following columns:
 • Id: Unique case identifier.
 • Id Putusan (Decision ID): Original ID of the document, distinguishing records from the Supreme Court’s website.
 • Tingkat Proses (Process Level): Indicates the court level (First Instance, Appellate, Cassation).
 • Id Pertama (First-Instance ID): Related first-instance document ID.
 • Id Banding (Appeal ID): Related appellate document ID.
 • Id Kasasi (Cassation ID): Related cassation document ID.

All documents in this dataset were obtained from public records on the official website of the Supreme Court of the Republic of Indonesia (https://putusan3.mahkamahagung.go.id/). As such, the dataset represents real-world cases and reflects the legal form of Indonesian court documents.

IDTheftCase-JudgmentCorpus is an essential dataset for research in named entity recognition and extraction, punishment imposition pattern analysis, and automatic document classification in the Indonesian legal context. Moreover, the dataset is useful for developers and researchers who aim to build and implement machine learning-based models to extract, group, and analyze judgment documents at different court levels.
DOI: 10.17632/48x9xm7rkf.3
title: IDTheftCase-JudgmentCorpus: Indonesian Theft Case Judgment Corpus - Levels of Court

description: IDTheftCase-JudgmentCorpus: Indonesian Theft Case Judgment Corpus – Levels of Court is a dataset containing the full-text documents of written judgments handed down by Indonesian courts in criminal theft cases at three levels: the court of first instance, the appellate court, and the cassation court. The dataset was created to support research and development activities in information extraction and Natural Language Processing (NLP), specifically about the processing and understanding the legal texts and court documents.

All annotated entity names have been standardized and translated into English, making the dataset more suitable for international NLP research and development of multilingual or cross-lingual models.

Available Annotated Files:
• 1-first-instance.csv – Contains tokenized and BIO-tagged court decisions from the district courts (Pengadilan Negeri).
• 2-appellate.csv – Contains tokenized and BIO-tagged decisions from the appellate courts (Pengadilan Tinggi).
• 3-cassation.csv – Contains tokenized and BIO-tagged decisions from the cassation level (Mahkamah Agung).
• metadata.csv – Contains contextual and hierarchical information about the judgment documents, structured into the following columns:
 o decision_id,
 o original_id, 
 o court_level, 
 o court_name, 
 o year,
 o verdict_type, and
 o cross-referenced case identifiers (first_id, appellate_id, cassation_id).

Entity Annotations:
The dataset is annotated using a BIO tagging format, identifying over 56 legal entities that appear in court documents. All entity labels are expressed in English, covering information such as:
• Parties and roles: Defendant, Lawyer, Prosecutor, Witness, PresidingJudge
• Legal process: ProsecutionDate, DecisionDate, ArrestDate, CassationReason
• Legal references: ChargeArticles, ProsecutionArticles, CourtRuling, DecisionCosts
• Case identifiers and metadata: DecisionNumber, ChargeType, CaseLevel, IncidentLocation

All documents in this dataset were obtained from public records on the official website of the Supreme Court of the Republic of Indonesia (https://putusan3.mahkamahagung.go.id/). As such, the dataset represents real-world cases and reflects the legal form of Indonesian court documents.

IDTheftCase-JudgmentCorpus is an essential dataset for research in named entity recognition and extraction, punishment imposition pattern analysis, and automatic document classification in the Indonesian legal context. Moreover, the dataset is useful for developers and researchers who aim to build and implement machine learning-based models to extract, group, and analyze judgment documents at different court levels.
DOI: 10.17632/z2zd4sxnvb.1
title: Evidence for substrate control of Cdk phosphorylation during the budding yeast cell cycle. Takacs et al

description: A series of sequential events orchestrate cell growth and division, set in motion by cyclin-dependent kinases (Cdks). In the ‘qualitative model’ for Cdk control, order is achieved by cell cycle stage-specific cyclins. However, single-cyclin cells retain cell cycle order. In an alternative ‘quantitative model’, increasing Cdk activity triggers substrate phosphorylation at sequential thresholds. Here, we test a key prediction from the quantitative model: the best Cdk substrates should be first to be phosphorylated. Phosphoproteome analysis of synchronous budding yeast cultures, against expectations, reveals little correlation between known in vitro Cdk phosphorylation rates and observed in vivo phosphorylation timings. Incorporating Cdk-counteracting phosphatases that impose phosphorylation thresholds does not improve the correlation. Instead of kinase-phosphatase control, i.e. ‘regulator control’, our phosphoproteome patterns reveal signatures of ‘substrate control’, including substrate-defined phosphorylation waves. The changing behavior of the substrates themselves therefore contributes to ordering their Cdk phosphorylation during the budding yeast cell cycle.
DOI: 10.17632/z2zd4sxnvb.2
title: Evidence for substrate control of Cdk phosphorylation during the budding yeast cell cycle. Takacs et al

description: A series of sequential events orchestrate cell growth and division, set in motion by cyclin-dependent kinases (Cdks). In the ‘qualitative model’ for Cdk control, order is achieved by cell cycle stage-specific cyclins. However, single-cyclin cells retain cell cycle order. In an alternative ‘quantitative model’, increasing Cdk activity triggers substrate phosphorylation at sequential thresholds. Here, we test a key prediction from the quantitative model: the best Cdk substrates should be first to be phosphorylated. Phosphoproteome analysis of synchronous budding yeast cultures, against expectations, reveals little correlation between known in vitro Cdk phosphorylation rates and observed in vivo phosphorylation timings. Incorporating Cdk-counteracting phosphatases that impose phosphorylation thresholds does not improve the correlation. Instead of kinase-phosphatase control, i.e. ‘regulator control’, our phosphoproteome patterns reveal signatures of ‘substrate control’, including substrate-defined phosphorylation waves. The changing behavior of the substrates themselves therefore contributes to ordering their Cdk phosphorylation during the budding yeast cell cycle.
DOI: 10.17632/z2zd4sxnvb.3
title: Evidence of substrate control of Cdk phosphorylation during the budding yeast cell cycle. Takacs et al

description: A series of sequential events orchestrate cell growth and division, set in motion by cyclin-dependent kinases (Cdks). In the ‘qualitative model’ for Cdk control, order is achieved by cell cycle stage-specific cyclins. However, single-cyclin cells retain cell cycle order. In an alternative ‘quantitative model’, increasing Cdk activity triggers substrate phosphorylation at sequential thresholds. Here, we test a key prediction from the quantitative model: the best Cdk substrates should be first to be phosphorylated. Phosphoproteome analysis of synchronous budding yeast cultures, against expectations, reveals little correlation between known in vitro Cdk phosphorylation rates and observed in vivo phosphorylation timings. Incorporating Cdk-counteracting phosphatases that impose phosphorylation thresholds does not improve the correlation. Instead of kinase-phosphatase control, i.e. ‘regulator control’, our phosphoproteome patterns reveal signatures of ‘substrate control’, including substrate-defined phosphorylation waves. The changing behavior of the substrates themselves therefore contributes to ordering their Cdk phosphorylation during the budding yeast cell cycle.
DOI: 10.17632/rwr75f68jc.1
title: Crockford Proterozoic Triple Oxygen Isotopes

description: Triple oxygen isotope compositions of sedimentary sulfate (∆’17O) have been applied as a tracer of past primary productivity, pO2 and pCO2. However, large intraformational variability of ∆’17O signatures and debate surrounding how they are produced and eventually preserved has limited the understanding of this important geochemical record. Here we explore depositional controls on ∆’17O signatures of mid-Proterozoic (≈2.0-1.0 Ga) sulfates. We identify a clear depositional control on the magnitude of preserved ∆’17O sulfate signatures, with Proterozoic terrestrial environments preserving the most negative and variable signatures and marine environments displaying values near modern marine sulfate with less overall variability. Our results strongly suggest that local depositional settings influence the magnitude of preserved ∆’17O signals, and the processes within those environments drive much of the observed intraformational variability. Finally, this analysis suggests that ∆’17O signatures in concert with sedimentological observations may offer a powerful tool to identify basin restriction and non-marine settings throughout the geologic record.
DOI: 10.5281/zenodo.15175590
title: Co-UDlabs_WP6_T61_INSA_002. Lidar testing data set

description: This data set produced by the Co-UDlabs project includes raw data collected for the preliminary testing of a Lidar sensor used to evaluate its ability to measure sediments accumulation in a stormwater settling tank. The data set corresponds to the Lidar data acquisition during two drone flights above the Chassieu stormwater infiltration tank in Lyon, France in May 2023. The PDF file describes the sensor, the testing site and protocol, and the data set. The zip file contains two files in the .LAS format as described in the PDF file. This data set is linked to the Co-UDlabs Deliverable D6.2 “Report on emerging monitoring technologies for urban drainage systems”.
DOI: 10.5281/zenodo.15175591
title: Co-UDlabs_WP6_T61_INSA_002. Lidar testing data set

description: This data set produced by the Co-UDlabs project includes raw data collected for the preliminary testing of a Lidar sensor used to evaluate its ability to measure sediments accumulation in a stormwater settling tank. The data set corresponds to the Lidar data acquisition during two drone flights above the Chassieu stormwater infiltration tank in Lyon, France in May 2023. The PDF file describes the sensor, the testing site and protocol, and the data set. The zip file contains two files in the .LAS format as described in the PDF file. This data set is linked to the Co-UDlabs Deliverable D6.2 “Report on emerging monitoring technologies for urban drainage systems”.