cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15465690
title: TABLE 1 in A deeply divergent lineage of Walkerana (Anura: Ranixalidae) from the Western Ghats of Peninsular India

description: TABLE 1. Morphometric data (in mm) for the type series and reference collections of Walkerana muduga sp. nov.
Reg. No.ZSI/WRC/A/2211 (CESF1607)#ZSI/WRC/A/2212 (CESF1606)$CESF1608&SexMaleFemaleMaleSVL27.944.922.7HL10.315.49.2HW11.317.39.0ES4.77.64.3ED3.95.54.1TYH2.23.21.8UEW2.43.42.2EN2.53.61.9IN3.75.43.3IO3.03.92.5UAL4.77.74.3PAL7.611.55.8f13.75.32.7f24.25.93.0f34.67.03.5f43.04.42.4FEL14.924.812.1TBL18.228.414.0TAR8.113.06.5FOL14.925.612.8t12.53.82.0t23.95.62.6t35.79.64.1t48.815.27.0t54.87.43.5
# holotype; $ paratype; & other referred specimens
description: Published as part of Dinesh, K. P., Vijayakumar, S. P., Ramesh, Vijay, Jayarajan, Aditi, Chandramouli, S. R. & Shanker, Kartik, 2020, A deeply divergent lineage of Walkerana (Anura: Ranixalidae) from the Western Ghats of Peninsular India, pp. 272 in Zootaxa 4729 (2) on page 272, DOI: 10.11646/zootaxa.4729.2.7, http://zenodo.org/record/3632638
DOI: 10.5281/zenodo.15465691
title: TABLE 1 in A deeply divergent lineage of Walkerana (Anura: Ranixalidae) from the Western Ghats of Peninsular India

description: TABLE 1. Morphometric data (in mm) for the type series and reference collections of Walkerana muduga sp. nov.
Reg. No.ZSI/WRC/A/2211 (CESF1607)#ZSI/WRC/A/2212 (CESF1606)$CESF1608&SexMaleFemaleMaleSVL27.944.922.7HL10.315.49.2HW11.317.39.0ES4.77.64.3ED3.95.54.1TYH2.23.21.8UEW2.43.42.2EN2.53.61.9IN3.75.43.3IO3.03.92.5UAL4.77.74.3PAL7.611.55.8f13.75.32.7f24.25.93.0f34.67.03.5f43.04.42.4FEL14.924.812.1TBL18.228.414.0TAR8.113.06.5FOL14.925.612.8t12.53.82.0t23.95.62.6t35.79.64.1t48.815.27.0t54.87.43.5
# holotype; $ paratype; & other referred specimens
description: Published as part of Dinesh, K. P., Vijayakumar, S. P., Ramesh, Vijay, Jayarajan, Aditi, Chandramouli, S. R. & Shanker, Kartik, 2020, A deeply divergent lineage of Walkerana (Anura: Ranixalidae) from the Western Ghats of Peninsular India, pp. 272 in Zootaxa 4729 (2) on page 272, DOI: 10.11646/zootaxa.4729.2.7, http://zenodo.org/record/3632638
DOI: 10.5281/zenodo.15465694
title: TABLE 2 in A deeply divergent lineage of Walkerana (Anura: Ranixalidae) from the Western Ghats of Peninsular India

description: TABLE 2. Extant valid species of Walkerana in the Western Ghats and GenBank accession numbers for the Ranixalidae taxa used in the construction of Bayesian tree
Sl. No.SpeciesGenBank accession numbersVoucher NumbersBroad hill range distribution in the WG1Walkerana muduga sp. nov.KX098622 and KX098637CESF 1606 and CESF 1607Elivalmalai2Walkerana diplosticta (Günther, 1876)KX098610CESF 2045Agasthyamalai3Walkerana leptodactyla (Boulenger, 1882)KX098603 and KX098628CESF 698 and CESF 282Anaimalai4Walkerana phrynoderma (Boulenger, 1882)KX098604CESF 720Anaimalai
description: Published as part of Dinesh, K. P., Vijayakumar, S. P., Ramesh, Vijay, Jayarajan, Aditi, Chandramouli, S. R. & Shanker, Kartik, 2020, A deeply divergent lineage of Walkerana (Anura: Ranixalidae) from the Western Ghats of Peninsular India, pp. 272 in Zootaxa 4729 (2) on page 272, DOI: 10.11646/zootaxa.4729.2.7, http://zenodo.org/record/3632638
DOI: 10.5281/zenodo.15465695
title: TABLE 2 in A deeply divergent lineage of Walkerana (Anura: Ranixalidae) from the Western Ghats of Peninsular India

description: TABLE 2. Extant valid species of Walkerana in the Western Ghats and GenBank accession numbers for the Ranixalidae taxa used in the construction of Bayesian tree
Sl. No.SpeciesGenBank accession numbersVoucher NumbersBroad hill range distribution in the WG1Walkerana muduga sp. nov.KX098622 and KX098637CESF 1606 and CESF 1607Elivalmalai2Walkerana diplosticta (Günther, 1876)KX098610CESF 2045Agasthyamalai3Walkerana leptodactyla (Boulenger, 1882)KX098603 and KX098628CESF 698 and CESF 282Anaimalai4Walkerana phrynoderma (Boulenger, 1882)KX098604CESF 720Anaimalai
description: Published as part of Dinesh, K. P., Vijayakumar, S. P., Ramesh, Vijay, Jayarajan, Aditi, Chandramouli, S. R. & Shanker, Kartik, 2020, A deeply divergent lineage of Walkerana (Anura: Ranixalidae) from the Western Ghats of Peninsular India, pp. 272 in Zootaxa 4729 (2) on page 272, DOI: 10.11646/zootaxa.4729.2.7, http://zenodo.org/record/3632638
DOI: 10.5281/zenodo.15420093
title: Data from: Multi-modal, interrelated navigation in migratory birds: a data mining study

description: This dataset contains processed GPS tracking data from 117 greater white-fronted geese (Anser albifrons) used in the study "Multi-modal, interrelated navigation in migratory birds: a data mining study" (Moayedi et al., 2025, in review). The data, collected between 2014 and 2023, cover spring and autumn migrations between Arctic breeding grounds and wintering sites in central Europe. Data have been cleaned, filtered, and annotated with environmental (wind, elevation, distance to coast) and geomagnetic (inclination, intensity, declination) variables.

The data are derived from five original studies in the Movebank Data Repository:- Disturbance of GWFG by IFV and IWWR- Disturbance of GWFG by IFV and IWWR 2017-2018- Geese_MPIAB_IFV_IWWR_Kolguev_HUN2018- Geese_MPIAB_Kolguev2016- LifeTrack Geese IG-RAS MPIAB ICARUS 2

 

For further data use or details about the data background, please contact the data creators :  Andrea Kölzsch (akoelzsch@ab.mpg.de) Helmut Kruckenberg (helmut.kruckenberg@blessgans.de).

 

Funding AcknowledgementsData collection was funded by:





The Lower Saxony Ministry of Food, Agriculture and Consumer Protection




The “ICARUS” grant from the German Aerospace Center (DLR)
DOI: 10.5517/ccdc.csd.cc2lpp77
title: CCDC 2404119: Experimental Crystal Structure Determination

description: Related Article: Chi Song, Ze-Yu Zhao, Yi Zang, Jia Li, Yeun-Mun Choo, Haibo Wang, Guangyao Mei, Juan Xiong, Jin-Feng Hu|2025|Phytochemistry|237|114533|doi:10.1016/j.phytochem.2025.114533
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5066/p9py1ulv
title: Allometrics of Baltic Tellin (Macoma balthica) Bivalves from Cook Inlet, AK, and Baie de Somme, France, 2010-2011

description: This dataset contains two tables of sampling information and size and mass measurements of the Baltic Tellin (Macoma balthica) from sites in Cook Inlet, Alaska, USA, and the Baie de Somme estuary, France.
DOI: 10.15472/qsskss
title: Colección de Reptiles (Reptilia: Squamata) del Centro de Colecciones Biológicas de la Universidad del Magdalena

description: El Centro de Colecciones Biológicas de la Universidad del Magdalena (CBUMAG) se constituyó formalmente en julio del año 2013 y obtuvo su registro ante el Instituto Humboldt en febrero de 2014 (Registro Nacional de Colecciones - RNC: 207). El CBUMAG se encuentra en una fase de consolidación, con un gran número de ejemplares catalogados y organizados en las diferentes colecciones, al que se suma el constante crecimiento con los depósitos de ejemplares hechos principalmente por los estudiantes de la Universidad del Magdalena a partir de sus trabajos de investigación, la misión es promover la preservación y curaduría de los ejemplares depositados en el mismo, desarrollar trabajos de investigación en taxonomía, sistemática, biogeografía y bioprospección con el fin de dar a conocer y promover el conocimiento sobre la biodiversidad de la región Caribe y sus usos potenciales.
description: Este set de datos contiene 112 registros de anfibios recolectados en el año 2022 en la Sierra Nevada de Santa Marta, en las cuencas de los ríos Don Diego y Fundación, se encuentran depositados en la Colección Biológica de la Universidad del Magdalena preservados en frascos con formaldehído 10%.
DOI: 10.5519/qd.d97cyxv9
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (83 records)
DOI: 10.25574/29607
title: Chandra X-ray Observatory ObsId 29607

description: AO-26 Calibration Observations of SNR G21.5-0.9
DOI: 10.60520/ieda/113811
title: Arc-Wide Exhumation of the Aleutian Islands at ~5-7 Ma Revealed By Apatite (U-Th-Sm)/He

description: We applied apatite (U-Th-Sm)/He (AHe) thermochronology to a total of 171 apatite grains from 43 plutonic samples from six of the Aleutian Islands, covering over 1400 km of the arc. We obtained AHe mean ages (calculated using 3-8 AHe grain dates) from twelve samples from the island of Unalaska, six from Umnak, six from Atka (plus two AHe grain dates from two samples each), two from Kagalaska, nine from Adak, and four from Attu. Nine plutonic samples from Unalaska (UNAK), six samples from Umnak (UMK15), and eight samples from Atka (ATK15) were collected during the NSF funded 2015 GeoPRISMS Aleutian Islands field campaign. All samples from Attu (AT-80), Adak (CC76, BW8), and Kagalaska (KAG76) and three samples from Unalaska (SED) were provided by the Cornell University Kay group collection collected in the 1970’s and 1980’s. The goal of this study was to constrain the timing of Aleutian arc exhumation, test the potential role of varying subduction dynamics along the arc, and investigate potential island arc exhumation mechanisms.
DOI: 10.25574/30735
title: Chandra X-ray Observatory ObsId 30735

description: Elucidating the Progenitors of Fast Radio Bursts with Chandra
DOI: 10.5281/zenodo.15397892
title: Deconvoluting the Effects of Substituents on Reaction Barriers through Machine Learning: The Case of Brønsted Acid-Mediated Nazarov Cyclizations
DOI: 10.5281/zenodo.15397893
title: Deconvoluting the Effects of Substituents on Reaction Barriers through Machine Learning: The Case of Brønsted Acid-Mediated Nazarov Cyclizations
DOI: 10.5281/zenodo.11213783
title: Code4ML: a Large-scale Dataset of annotated Machine Learning Code

description: This is an enriched version of Code4ML: a Large-scale Dataset of annotated Machine Learning Code, a corpus of Python code snippets, competition summaries, and data descriptions from Kaggle. The initial corpus consists of ≈ 2.5 million snippets of ML code collected from ≈ 100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. 

The data is organized as a set of tables in CSV format. It includes several central entities: raw code blocks collected from Kaggle (code_blocks.csv), kernels (kernels_meta.csv) and competitions meta information (competitions_meta.csv). Manually annotated code blocks are presented as a separate table (murkup_data.csv). As this table contains the numeric id of the code block semantic type, we also provide a mapping from the id to semantic class and subclass (vertices.csv).

Snippets information (code_blocks.csv) can be mapped with kernels meta-data via kernel_id. Kernels metadata is linked to Kaggle competitions information through comp_name. To ensure the quality of the data kernels_meta.csv includes only notebooks with an available Kaggle score.

Automatic classification of code_blocks are stored in data_with_preds.csv. The mapping of this table with code_blocks.csv can be doe through code_blocks_index column, which corresponds to code_blocks indices.

The updated Code4ML 2.0 corpus includes kernels retrieved from Code Kaggle Meta. These kernels correspond to the kaggle competitions launched since 2020. The natural descriptions of the competitions are retrieved with the aim of LLM. 

kernels_meta2.csv may contain kernels without Kaggle score, but with the place in the leader board (rank).

Code4ML 2.0 dataset can be used for various purposes, including training and evaluating models for code generation, code understanding, and natural language processing tasks.
DOI: 10.5281/zenodo.11213871
title: Code4ML 2.0: a Large-scale Dataset of annotated Machine Learning Code

description: This is an enriched version of Code4ML: a Large-scale Dataset of annotated Machine Learning Code, a corpus of Python code snippets, competition summaries, and data descriptions from Kaggle. The initial corpus consists of ≈ 2.5 million snippets of ML code collected from ≈ 100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. 

The data is organized as a set of tables in CSV format. It includes several central entities: raw code blocks collected from Kaggle (code_blocks.csv), kernels (kernels_meta.csv) and competitions meta information (competitions_meta.csv). Manually annotated code blocks are presented as a separate table (murkup_data.csv). As this table contains the numeric id of the code block semantic type, we also provide a mapping from the id to semantic class and subclass (vertices.csv).

Snippets information (code_blocks.csv) can be mapped with kernels meta-data via kernel_id. Kernels metadata is linked to Kaggle competitions information through comp_name. To ensure the quality of the data kernels_meta.csv includes only notebooks with an available Kaggle score.

Automatic classification of code_blocks are stored in data_with_preds.csv. The mapping of this table with code_blocks.csv can be doe through code_blocks_index column, which corresponds to code_blocks indices.

The updated Code4ML 2.0 corpus includes kernels retrieved from Code Kaggle Meta. These kernels correspond to the kaggle competitions launched since 2020. The natural descriptions of the competitions are retrieved with the aim of LLM. 

kernels_meta2.csv may contain kernels without Kaggle score, but with the place in the leader board (rank).

Code4ML 2.0 dataset can be used for various purposes, including training and evaluating models for code generation, code understanding, and natural language processing tasks.
DOI: 10.5281/zenodo.6607065
title: Code4ML: a Large-scale Dataset of annotated Machine Learning Code

description: We present Code4ML: a Large-scale Dataset of annotated Machine Learning Code, a corpus of Python code snippets, competition summaries, and data descriptions from Kaggle. The data is organized in a table structure. Code4ML includes several main objects: competitions information, raw code blocks collected form Kaggle and manually marked up snippets. Each table has a .csv format. Each competition has the text description and metadata, reflecting competition and used dataset characteristics as well as evaluation metrics (competitions.csv). The corresponding datasets can be loaded using Kaggle API and data sources. The code blocks themselves and their metadata are collected to the data frames concerning the publishing year of the initial kernels. The current version of the corpus includes two code blocks files: snippets from kernels up to the 2020 year (сode_blocks_upto_20.csv) and those from the 2021 year (сode_blocks_21.csv) with corresponding metadata. The corpus consists of 2 743 615 ML code blocks collected from 107 524 Jupyter notebooks. Marked up code blocks have the following metadata: anonymized id, the format of the used data (for example, <em>table</em> or <em>audio</em>), the id of the semantic type, a flag for the code errors, the estimated relevance to the semantic class (from 1 to 5), the id of the parent notebook, and the name of the competition. The current version of the corpus has ~12 000 labeled snippets (markup_data_20220415.csv). As marked up code blocks data contains the numeric id of the code block semantic type, we also provide a mapping from this number to semantic type and subclass (actual_graph_2022-06-01.csv). The dataset can help solve various problems, including code synthesis from a prompt in natural language, code autocompletion, and semantic code classification.
DOI: 10.5281/zenodo.7023415
title: Code4ML: a Large-scale Dataset of annotated Machine Learning Code

description: We present Code4ML: a Large-scale Dataset of annotated Machine Learning Code, a corpus of Python code snippets, competition summaries, and data descriptions from Kaggle. The corpus consists of ≈ 2.5 million snippets of ML code collected from ≈ 100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. The data is organized as a set of tables in CSV format. It includes several central entities: raw code blocks collected from Kaggle (code_blocks.csv), kernels (kernels_meta.csv) and competitions meta information (competitions_meta.csv). Manually annotated code blocks are presented as a separate table (murkup_data.csv). As this table contains the numeric id of the code block semantic type, we also provide a mapping from the id to semantic class and subclass (vertices.csv). Snippets information (code_blocks.csv) can be mapped with kernels meta-data via kernel_id. Kernels metadata is linked to Kaggle competitions information through comp_name. To ensure the quality of the data kernels_meta.csv includes only notebooks with an available Kaggle score. The corpus can help solve various problems, including code synthesis from a prompt in natural language, code autocompletion, and semantic code classification.
DOI: 10.5281/zenodo.7312771
title: Code4ML: a Large-scale Dataset of annotated Machine Learning Code

description: We present Code4ML: a Large-scale Dataset of annotated Machine Learning Code, a corpus of Python code snippets, competition summaries, and data descriptions from Kaggle. The corpus consists of ≈ 2.5 million snippets of ML code collected from ≈ 100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. The data is organized as a set of tables in CSV format. It includes several central entities: raw code blocks collected from Kaggle (code_blocks.csv), kernels (kernels_meta.csv) and competitions meta information (competitions_meta.csv). Manually annotated code blocks are presented as a separate table (murkup_data.csv). As this table contains the numeric id of the code block semantic type, we also provide a mapping from the id to semantic class and subclass (vertices.csv). Snippets information (code_blocks.csv) can be mapped with kernels meta-data via kernel_id. Kernels metadata is linked to Kaggle competitions information through comp_name. <em>To ensure the quality of the data kernels_meta.csv includes only notebooks with an available Kaggle score.</em> The corpus can help solve various problems, including code synthesis from a prompt in natural language, code autocompletion, and semantic code classification.
DOI: 10.5281/zenodo.7312803
title: Code4ML: a Large-scale Dataset of annotated Machine Learning Code

description: We present Code4ML: a Large-scale Dataset of annotated Machine Learning Code, a corpus of Python code snippets, competition summaries, and data descriptions from Kaggle. The corpus consists of ≈ 2.5 million snippets of ML code collected from ≈ 100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. The data is organized as a set of tables in CSV format. It includes several central entities: raw code blocks collected from Kaggle (code_blocks.csv), kernels (kernels_meta.csv) and competitions meta information (competitions_meta.csv). Manually annotated code blocks are presented as a separate table (murkup_data.csv). As this table contains the numeric id of the code block semantic type, we also provide a mapping from the id to semantic class and subclass (vertices.csv). Snippets information (code_blocks.csv) can be mapped with kernels meta-data via kernel_id. Kernels metadata is linked to Kaggle competitions information through comp_name. <em>To ensure the quality of the data kernels_meta.csv includes only notebooks with an available Kaggle score.</em> The corpus can help solve various problems, including code synthesis from a prompt in natural language, code autocompletion, and semantic code classification.
DOI: 10.5281/zenodo.7494924
title: Code4ML: a Large-scale Dataset of annotated Machine Learning Code

description: We present Code4ML: a Large-scale Dataset of annotated Machine Learning Code, a corpus of Python code snippets, competition summaries, and data descriptions from Kaggle. The corpus consists of ≈ 2.5 million snippets of ML code collected from ≈ 100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. The data is organized as a set of tables in CSV format. It includes several central entities: raw code blocks collected from Kaggle (code_blocks.csv), kernels (kernels_meta.csv) and competitions meta information (competitions_meta.csv). Manually annotated code blocks are presented as a separate table (murkup_data.csv). As this table contains the numeric id of the code block semantic type, we also provide a mapping from the id to semantic class and subclass (vertices.csv). Snippets information (code_blocks.csv) can be mapped with kernels meta-data via kernel_id. Kernels metadata is linked to Kaggle competitions information through comp_name. <em>To ensure the quality of the data kernels_meta.csv includes only notebooks with an available Kaggle score.</em> <em>Automatic </em>classification of code_blocks are stored in data_with_preds.csv. The mapping of this table with code_blocks.csv can be doe through <em>code_blocks_index</em> column, which corresponds to code_blocks indices. The corpus can help solve various problems, including code synthesis from a prompt in natural language, code autocompletion, and semantic code classification.
DOI: 10.5281/zenodo.7704743
title: Code4ML: a Large-scale Dataset of annotated Machine Learning Code

description: We present Code4ML: a Large-scale Dataset of annotated Machine Learning Code, a corpus of Python code snippets, competition summaries, and data descriptions from Kaggle. The corpus consists of ≈ 2.5 million snippets of ML code collected from ≈ 100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. The data is organized as a set of tables in CSV format. It includes several central entities: raw code blocks collected from Kaggle (code_blocks.csv), kernels (kernels_meta.csv) and competitions meta information (competitions_meta.csv). Manually annotated code blocks are presented as a separate table (murkup_data.csv). As this table contains the numeric id of the code block semantic type, we also provide a mapping from the id to semantic class and subclass (vertices.csv). Snippets information (code_blocks.csv) can be mapped with kernels meta-data via kernel_id. Kernels metadata is linked to Kaggle competitions information through comp_name. <em>To ensure the quality of the data kernels_meta.csv includes only notebooks with an available Kaggle score.</em> <em>Automatic </em>classification of code_blocks are stored in data_with_preds.csv. The mapping of this table with code_blocks.csv can be doe through <em>code_blocks_index</em> column, which corresponds to code_blocks indices. The corpus can help solve various problems, including code synthesis from a prompt in natural language, code autocompletion, and semantic code classification.