cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15496084
title: Transposable elements create distinct genomic niches for effector evolution among Magnaporthe oryzae lineages

description: Draft genome assemblies: De novo draft assemblies were generated using the software SPAdes v3.15.5 (Bankevich et al., 2012) with the “careful” method and automated k-mer selection. Assemblies are identified by their NCBI SRA accession ID.

Effectors gain and loss dynamics across Magnaporthe spp. Maximum likelihood phylogenomic tree of 405 Magnaporthe genomes from the most frequently sampled pathotypes based on 300 protein sequence alignments of single-copy genes. Bootstrap confidence values >80% are displayed. AVRs are organized based on hierarchical clustering of presence/absence patterns. AVRs colored in green belong to the MoO AVR pathotype and in blue belong to the MoT AVR pathotype.

 

Abstract: Plant-pathogen interactions are characterized by evolutionary arms races. At the molecular level, fungal effectors can target important plant functions, while plants evolve to improve effector recognition. Rapid evolution in genes encoding effectors can be facilitated by transposable elements (TEs). In Magnaporthe oryzae, the causal agent of blast disease in several cereals and grasses, TEs play important roles in chromosomal evolution as well as the gain or loss of effector genes in host specialized lineages. However, a global understanding of TE dynamics driving effector evolution at population scale and across lineages is lacking. Here, we focus on 16 AVR effector loci assessed across a global sampling of 11 reference genomes and 447 newly generated draft genome assemblies across all major M. oryzae lineages and outgroups. We classified each effector based on evidence for duplication, deletion and translocation processes among M. oryzae lineages. Next, we determined AVR gain and loss dynamics across lineages allowing for a broad categorization of effector dynamics. Each AVR was integrated in a distinct genomic niche determined by the TE activity profile contributing to the diversification at the locus. We quantified TE contributions to effector niches and found that TE identity helped diversify AVR loci. We used the large genomic dataset to recapitulate the evolution of the rice blast AVR1-CO39 locus. Taken together, our work demonstrates how TE dynamics are an integral component of M. oryzae effector evolution, likely facilitating escape from host recognition. In-depth tracking of effector loci is a valuable tool to predict the durability of host resistance.
DOI: 10.5281/zenodo.15875042
title: Transposable elements create distinct genomic niches for effector evolution among Magnaporthe oryzae lineages

description: Draft genome assemblies: De novo draft assemblies were generated using the software SPAdes v3.15.5 (Bankevich et al., 2012) with the “careful” method and automated k-mer selection. Assemblies are identified by their NCBI SRA accession ID.

Effectors gain and loss dynamics across Magnaporthe spp. Maximum likelihood phylogenomic tree of 405 Magnaporthe genomes from the most frequently sampled pathotypes based on 300 protein sequence alignments of single-copy genes. Bootstrap confidence values >80% are displayed. AVRs are organized based on hierarchical clustering of presence/absence patterns. AVRs colored in green belong to the MoO AVR pathotype and in blue belong to the MoT AVR pathotype.

 

Abstract: Plant-pathogen interactions are characterized by evolutionary arms races. At the molecular level, fungal effectors can target important plant functions, while plants evolve to improve effector recognition. Rapid evolution in genes encoding effectors can be facilitated by transposable elements (TEs). In Magnaporthe oryzae, the causal agent of blast disease in several cereals and grasses, TEs play important roles in chromosomal evolution as well as the gain or loss of effector genes in host specialized lineages. However, a global understanding of TE dynamics driving effector evolution at population scale and across lineages is lacking. Here, we focus on 16 AVR effector loci assessed across a global sampling of 11 reference genomes and 447 newly generated draft genome assemblies across all major M. oryzae lineages and outgroups. We classified each effector based on evidence for duplication, deletion and translocation processes among M. oryzae lineages. Next, we determined AVR gain and loss dynamics across lineages allowing for a broad categorization of effector dynamics. Each AVR was integrated in a distinct genomic niche determined by the TE activity profile contributing to the diversification at the locus. We quantified TE contributions to effector niches and found that TE identity helped diversify AVR loci. We used the large genomic dataset to recapitulate the evolution of the rice blast AVR1-CO39 locus. Taken together, our work demonstrates how TE dynamics are an integral component of M. oryzae effector evolution, likely facilitating escape from host recognition. In-depth tracking of effector loci is a valuable tool to predict the durability of host resistance.
DOI: 10.15468/dl.4q3bhj
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bidens pilosa",
 "Country is Cambodia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008436-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.js2ycq
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAC1514"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008437-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8p4dh8
title: Occurrence Download

description: A dataset containing 4087121 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Mareca strepera, Marmaronetta angustirostris, Melanitta nigra, Melanocorypha bimaculata, Melanocorypha calandra, Melanocorypha yeltoniensis, Meleagris gallopavo, Melierax metabates, Mergellus albellus, Mergus merganser, Mergus serrator, Merops viridissimus, Microcarbo pygmaeus, Milvus aegyptius, Milvus migrans, Milvus milvus, Monticola saxatilis, Monticola solitarius, Montifringilla nivalis, Morus bassanus, Motacilla cinerea, Motacilla citreola, Motacilla flava, Myiopsitta monachus, Neophron percnopterus, Neotis nuba, Netta rufina, Numenius arquata, Numenius phaeopus, Numenius tenuirostris, Numida meleagris, Nycticorax nycticorax)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "DecimalLatitude is greater than 0",
 "DecimalLatitude is less than 75",
 "DecimalLongitude is greater than -50",
 "DecimalLongitude is less than 50",
 "CoordinateUncertaintyInMeters is less than 5000",
 "Year is greater than or equal to 1990",
 "Year is less than or equal to 2015"
 ]
}

 The dataset includes 4087121 records from 1101 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008421-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14885156
title: Improved River Slope Datasets for the United States Hydrofabrics

description: The CONtiguous United States (CONUS) “Flood Inundation Mapping Hydrofabric - ICESat-2 River Surface Slope” (FIM HF IRIS) dataset integrates river slopes from the global IRIS dataset for 117,357 spatially corresponding main-stream reaches within NOAA’s Office of Water Prediction operational FIM forecasting system, which utilizes the Height Above Nearest Drainage approach (OWP HAND-FIM) to help warn communities of floods. To achieve this, a spatial joining approach was developed to align FIM HF reaches with IRIS reaches, accounting for differences in reach flowline sources. When applied to OWP HAND-FIM, FIM HF IRIS improved flood map accuracy by an average of 31% (CSI) across eight flood events compared to the original FIM HF slopes. Using a common attribute, IRIS data were also transferred from FIM HF IRIS to the CONUS-scale Next Generation Water Resources Modeling Framework Hydrofabric (NextGen HF), creating the NextGen HF IRIS dataset. By referencing another common attribute, SWOT vector data (e.g., water surface elevation, slope, discharge) can be leveraged by OWP HAND-FIM and NextGen through the two resulting datasets. The spatial joining approach, which enables the integration of FIM HF with other hydrologic datasets via flowlines, is provided alongside the two resulting datasets.

1. Variable Description

1.1 FIM HF IRIS v1.0

HydroID:

Unique identifier for FIM HF stream reaches

From_Node:

Upstream node ID

To_Node:

Downstream node ID

reach_id:

The SWORD reach identifier

slope_iris_sword:

The IRIS slope joined to the corresponding FIM HF flowline, while the SWORD slopes were used for the flowlines where the IRIS slopes were unavailable [mm/mm]

slope_source_iris_sword:

Value indicating the slope of slope_iris_sword is from IRIS (1) or SWORD (2)

1.2 NextGen HF IRIS v1.0

id:

Unique identifier for NextGen HF flowline (flowpath)

to_id:

NextGen HF flowline id where water flows

reach_id:

The SWORD reach identifier

slope_iris_sword:

The IRIS slope joined to the corresponding NextGen HF flowline, while the SWORD slopes were used for the flowlines where the IRIS slopes were unavailable [mm/mm]

slope_source_iris_sword:

Value indicating the slope of slope_iris_sword is from IRIS (1) or SWORD (2)
DOI: 10.5281/zenodo.14984550
title: Improved River Slope Datasets for the United States Hydrofabrics

description: The CONtiguous United States (CONUS) “Flood Inundation Mapping Hydrofabric - ICESat-2 River Surface Slope” (FIM HF IRIS) dataset integrates river slopes from the global IRIS dataset for 117,357 spatially corresponding main-stream reaches within NOAA’s Office of Water Prediction operational FIM forecasting system, which utilizes the Height Above Nearest Drainage approach (OWP HAND-FIM) to help warn communities of floods. To achieve this, a spatial joining approach was developed to align FIM HF reaches with IRIS reaches, accounting for differences in reach flowline sources. When applied to OWP HAND-FIM, FIM HF IRIS improved flood map accuracy by an average of 31% (CSI) across eight flood events compared to the original FIM HF slopes. Using a common attribute, IRIS data were also transferred from FIM HF IRIS to the CONUS-scale Next Generation Water Resources Modeling Framework Hydrofabric (NextGen HF), creating the NextGen HF IRIS dataset. By referencing another common attribute, SWOT vector data (e.g., water surface elevation, slope, discharge) can be leveraged by OWP HAND-FIM and NextGen through the two resulting datasets. The spatial joining approach, which enables the integration of FIM HF with other hydrologic datasets via flowlines, is provided alongside the two resulting datasets.

1. Variable Description

1.1 FIM HF IRIS v1.0

HydroID:

Unique identifier for FIM HF stream reaches

From_Node:

Upstream node ID

To_Node:

Downstream node ID

reach_id:

The SWORD reach identifier

slope_iris_sword:

The IRIS slope joined to the corresponding FIM HF flowline, while the SWORD slopes were used for the flowlines where the IRIS slopes were unavailable [mm/mm]

slope_source_iris_sword:

Value indicating the slope of slope_iris_sword is from IRIS (1) or SWORD (2)

1.2 NextGen HF IRIS v1.0

id:

Unique identifier for NextGen HF flowline (flowpath)

to_id:

NextGen HF flowline id where water flows

reach_id:

The SWORD reach identifier

slope_iris_sword:

The IRIS slope joined to the corresponding NextGen HF flowline, while the SWORD slopes were used for the flowlines where the IRIS slopes were unavailable [mm/mm]

slope_source_iris_sword:

Value indicating the slope of slope_iris_sword is from IRIS (1) or SWORD (2)

2. Notes

The slope_iris_sword in FIM HF IRIS can be used with the Recalculate_Discharge_in_Hydrotable_useFIMHFIRIS.py script to regenerate the hydrotable for OWP HAND-FIM, where the discharge will be recalculated using slope_iris_sword. Consequently, the synthetic rating curves (SRCs) will be updated based on the new discharges (see more details here). The script can also be used to regenerate hydrotables using river slopes from other sources, such as NextGen HF, provided they are linked to the FIM HF flowlines.

The shapefiles for FIMHF_IRIS and NextGenHF_IRIS are provided in two geodatabases (.gdb), which can be accessed using ArcGIS, QGIS, or relevant Python packages for inspection, visualization, or spatial analysis of slope_iris_sword.
DOI: 10.5281/zenodo.15490790
title: Computer science student test results

description: 📊 Dataset Title:

Student Performance Interaction Logs for Programming Assignments



📝 Description:

This dataset contains anonymized interaction data collected from 486 computer science students working on online programming tasks. Each row represents a unique student session with aggregated behavioral and performance metrics. The data was collected as part of an educational analytics study aiming to cluster students based on usage patterns and performance.



📁 Columns:





user_id: Anonymized identifier for the student session (UUID)




clicks: Number of interactions (clicks) during the session




total_time: Total time spent (in seconds) on the task




avg_time_per_question: Average time per question (in seconds)




correct_answers: Number of correctly answered questions




total_questions: Total number of questions attempted




language: Programming language of the task (e.g., Java, SQL, Python)




level: Task difficulty level (e.g., Beginner, Advanced, Expert)




distribution: Statistical distribution of time allocation (e.g., uniform, exponential, Poisson)





⚠️ Notes:





All personally identifiable information has been removed.




The dataset is suitable for educational data mining, clustering, and behavioral modeling research.




Ethical approval was obtained (if applicable), and all data complies with GDPR anonymization standards.





Would you like a markdown version or Zenodo metadata JSON file to go along with it?
DOI: 10.5281/zenodo.15490791
title: Computer science student test results

description: 📊 Dataset Title:

Student Performance Interaction Logs for Programming Assignments



📝 Description:

This dataset contains anonymized interaction data collected from 486 computer science students working on online programming tasks. Each row represents a unique student session with aggregated behavioral and performance metrics. The data was collected as part of an educational analytics study aiming to cluster students based on usage patterns and performance.



📁 Columns:





user_id: Anonymized identifier for the student session (UUID)




clicks: Number of interactions (clicks) during the session




total_time: Total time spent (in seconds) on the task




avg_time_per_question: Average time per question (in seconds)




correct_answers: Number of correctly answered questions




total_questions: Total number of questions attempted




language: Programming language of the task (e.g., Java, SQL, Python)




level: Task difficulty level (e.g., Beginner, Advanced, Expert)




distribution: Statistical distribution of time allocation (e.g., uniform, exponential, Poisson)





⚠️ Notes:





All personally identifiable information has been removed.




The dataset is suitable for educational data mining, clustering, and behavioral modeling research.




Ethical approval was obtained (if applicable), and all data complies with GDPR anonymization standards.





Would you like a markdown version or Zenodo metadata JSON file to go along with it?
DOI: 10.25345/c5h98zs25
title: MassIVE MSV000098654 - High-performance native separation of mAb proteoforms by CZE-MS under native and denaturing nanoESI conditions for ultrasensitive charge variant characterization
title: This dataset includes mass spectrometry data from CZE-MS analysis of NISTmAb and trastuzumab on the intact level (triplicate measurements). In addition, it contains CZE-MS data of PNGase F digested NISTmAb.
DOI: 10.5281/zenodo.16532528
title: Experimental data of Adoption and Evolution of Code Style and Best Programming Practices in Open-Source Projects - ICSME 2025

description: Adoption and Evolution of Code Style and Best Programming Practices in Open-Source Projects - ICSME 2025

This is the experimental data of the paper

Adoption and Evolution of Code Style and Best Programming Practices in Open-Source Projects

assepted at the main research track of the IEEE International Conference on Software Maintenance and Evolution (ICSME) 2025

preprint: https://valerio-terragni.github.io/assets/pdf/kupari-icsme-2025.pdf 

To cite this work:



@inproceedings{kupari2025adoption, 

author = {Kupari, Alvari and Giacaman, Nasser and Terragni, Valerio},

title = {Adoption and Evolution of Code Style and Best Programming Practices in Open-Source Projects},

booktitle = {Proceedings of the IEEE International Conference on Software Maintenance and Evolution (ICSME)},

year = {2025},

publisher = {IEEE}

}
DOI: 10.5281/zenodo.16532529
title: Experimental data of Adoption and Evolution of Code Style and Best Programming Practices in Open-Source Projects - ICSME 2025

description: Adoption and Evolution of Code Style and Best Programming Practices in Open-Source Projects - ICSME 2025

This is the experimental data of the paper

Adoption and Evolution of Code Style and Best Programming Practices in Open-Source Projects

assepted at the main research track of the IEEE International Conference on Software Maintenance and Evolution (ICSME) 2025

preprint: https://valerio-terragni.github.io/assets/pdf/kupari-icsme-2025.pdf 

To cite this work:



@inproceedings{kupari2025adoption, 

author = {Kupari, Alvari and Giacaman, Nasser and Terragni, Valerio},

title = {Adoption and Evolution of Code Style and Best Programming Practices in Open-Source Projects},

booktitle = {Proceedings of the IEEE International Conference on Software Maintenance and Evolution (ICSME)},

year = {2025},

publisher = {IEEE}

}
DOI: 10.15468/dl.6fd8yv
title: Occurrence Download

description: A dataset containing 170 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Armoracia rusticana",
 "Country is Lithuania",
 "HasCoordinate is true"
 ]
}

 The dataset includes 170 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060742-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rjq5ey
title: Occurrence Download

description: A dataset containing 2356 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Armoracia rusticana",
 "Country is Romania",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2356 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060744-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w8ymv2
title: Occurrence Download

description: A dataset containing 662 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Armoracia rusticana",
 "Country is Norway",
 "HasCoordinate is true"
 ]
}

 The dataset includes 662 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060743-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14536852
title: Dataset for assessing the economic risks of overshoot pathways using an emulator based modelling chain

description: Dataset accompanying our research article titled "The Costs of Overshoot: Exceeding 1.5°C Burdens Vulnerable Countries and Widens Inequality". This dataset contains all files required to reproduce our results and to run the code uploaded to https://github.com/sarasita/econ.git.

The files are related to the following modelling steps: 



scen_{scenario_name}.csv: contain the original output from the simple climate model FaIR (about ~2000 trajectories each)

ids_reconstructed.csv: we sub-select 100 of the 2000 FaIR runs that adequately approximate uncertainty ranges from AR6. this file is a maps the 100 sub-selected FaIR runs back to the indices of the original 2000 trajectories

{scenario_name}.nc: contains the MESMER output for each scenario, i.e., netCDF file containing 100 local temperature trajectories at 2652 grid-cells

temperature_countrylevel_total.csv: contains the MESMER output aggregated to country-level and represents the input dataset for running the economic damage function

GDP_<model>_Result.csv: contains 100 GDP estimates per scenario for each of the SSP1-SSP5 baseline growth rates for the specified damage function


We suggest to generarte a folder with a subfolder 'Code' containing the code from https://github.com/sarasita/econ; and a second subfolder called 'Data' containing the files from this archive structured as follows: 



subfolder FaIR containing all ten scen_{scenario_name}.csv files and ids_reconstructed.csv

subfolder MESMER containing all ten {scenario_name}.nc files

subfolder GDP containing temperature_countrylevel_total.csv and the files GDP_<model>_Result.csv
DOI: 10.5281/zenodo.16529682
title: Dataset for assessing the economic risks of overshoot pathways using an emulator based modelling chain

description: Dataset accompanying our research article titled "The Costs of Overshoot: Exceeding 1.5°C Burdens Vulnerable Countries and Widens Inequality". This dataset contains all files required to reproduce our results and to run the code uploaded to https://github.com/sarasita/econ.git.

The files are related to the following modelling steps: 



scen_{scenario_name}.csv: contain the original output from the simple climate model FaIR (about ~2000 trajectories each)

ids_reconstructed.csv: we sub-select 100 of the 2000 FaIR runs that adequately approximate uncertainty ranges from AR6. this file is a maps the 100 sub-selected FaIR runs back to the indices of the original 2000 trajectories

{scenario_name}.nc: contains the MESMER output for each scenario, i.e., netCDF file containing 100 local temperature trajectories at 2652 grid-cells

temperature_countrylevel_total.csv: contains the MESMER output aggregated to country-level and represents the input dataset for running the economic damage function

GDP_<model>_Result.csv: contains 100 GDP estimates per scenario for each of the SSP1-SSP5 baseline growth rates for the specified damage function


We suggest to generarte a folder with a subfolder 'Code' containing the code from https://github.com/sarasita/econ; and a second subfolder called 'Data' containing the files from this archive structured as follows: 



subfolder FaIR containing all ten scen_{scenario_name}.csv files and ids_reconstructed.csv

subfolder MESMER containing all ten {scenario_name}.nc files

subfolder GDP containing temperature_countrylevel_total.csv and the files GDP_<model>_Result.csv
DOI: 10.15468/dl.srdnjk
title: Occurrence Download

description: A dataset containing 970 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aptenodytes forsteri",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLatitude is less than 10",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2015"
 ]
}

 The dataset includes 970 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060745-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.