cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6815838
title: TV Series - Networks of characters

description: Description. This repository contains three sets of files related to the social networks of characters in the following episodes of 3 TV series:



Breaking Bad (BB): S01--S03;

Game of Thrones (GoT): S01--05;

House of Cards (HoC): S01--S02.


The three separate Graphml files (`.graphml` extension) contain the static, cumulative, conversational networks at the end of the set of episodes considered. Three images (`.jpg` extension) of the resulting static, cumulative, conversational networks at the end of the first two seasons are also provided.The compressed archives (`.tgz` extension) provide snapshots of the conversational network of characters (Graphml format) in every scene. These files come in three flavors:



Narrative smoothing based networks (denoted `ns`).

Time-slice based networks, where all interactions are agglomerated every 10 scenes (denoted `'ts10`).

Time-slice based networks, where all interactions are agglomerated every 40 scenes (denoted `ts40`).


The video files (`.mp4` extension) contain short animations of the dynamic networks of characters as they evolve over the whole set of episodes considered:



The nodes are represented by the names of the corresponding characters and the distance between two character names is inversely proportional to the weight of the corresponding edge: the closer they are at some point of the story, the more they interact then.

The size of each character name is proportional to the local strength of the corresponding character at the moment considered.

The color of each character name corresponds to his community at any moment.


Each snapshot in these animations is based on our narrative smoothing approach.

This dataset was used in the following articles:



X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Narrative smoothing: dynamic conversational network for the analysis of TV Series plots,” in 2nd International Workshop on Dynamics in Networks (DyNo/ASONAM), 2016, pp. 1111–1118. ⟨hal-01276708⟩ DOI: 10.1109/ASONAM.2016.7752379

X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Extraction de réseaux dynamiques conversationnels par lissage narratif,” in 7ème Conférence sur les modèles et lánalyse de réseaux : approches mathématiques et informatiques, 2016. ⟨hal-01385215⟩

X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Extraction and analysis of dynamic conversational networks from TV series,” in Social Network Based Big Data Analysis and Applications, Springer, 2018, pp. 55–84. ⟨hal-01543938⟩ DOI: 10.1007/978-3-319-78196-9_3


Citation. If you use this dataset, please cite the article [1]

@InProceedings{Bost2016,  author    = {Bost, Xavier and Labatut, Vincent and Gueye, Serigne and Linarès, Georges},  title     = {Narrative smoothing: dynamic conversational network for the analysis of {TV} Series plots},  booktitle = {2nd ASONAM International Workshop on Dynamics in Networks},  year      = {2016},  pages     = {1111-1118},  address   = {San Francisco, US},  publisher = {IEEE Publishing},  doi       = {10.1109/ASONAM.2016.7752379},}
DOI: 10.5281/zenodo.6815839
title: TV Series - Networks of characters

description: Description. This repository contains three sets of files related to the social networks of characters in the following episodes of 3 TV series:



Breaking Bad (BB): S01--S03;

Game of Thrones (GoT): S01--05;

House of Cards (HoC): S01--S02.


The three separate Graphml files (`.graphml` extension) contain the static, cumulative, conversational networks at the end of the set of episodes considered. Three images (`.jpg` extension) of the resulting static, cumulative, conversational networks at the end of the first two seasons are also provided.The compressed archives (`.tgz` extension) provide snapshots of the conversational network of characters (Graphml format) in every scene. These files come in three flavors:



Narrative smoothing based networks (denoted `ns`).

Time-slice based networks, where all interactions are agglomerated every 10 scenes (denoted `'ts10`).

Time-slice based networks, where all interactions are agglomerated every 40 scenes (denoted `ts40`).


The video files (`.mp4` extension) contain short animations of the dynamic networks of characters as they evolve over the whole set of episodes considered:



The nodes are represented by the names of the corresponding characters and the distance between two character names is inversely proportional to the weight of the corresponding edge: the closer they are at some point of the story, the more they interact then.

The size of each character name is proportional to the local strength of the corresponding character at the moment considered.

The color of each character name corresponds to his community at any moment.


Each snapshot in these animations is based on our narrative smoothing approach.

This dataset was used in the following articles:



X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Narrative smoothing: dynamic conversational network for the analysis of TV Series plots,” in 2nd International Workshop on Dynamics in Networks (DyNo/ASONAM), 2016, pp. 1111–1118. ⟨hal-01276708⟩ DOI: 10.1109/ASONAM.2016.7752379

X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Extraction de réseaux dynamiques conversationnels par lissage narratif,” in 7ème Conférence sur les modèles et lánalyse de réseaux : approches mathématiques et informatiques, 2016. ⟨hal-01385215⟩

X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Extraction and analysis of dynamic conversational networks from TV series,” in Social Network Based Big Data Analysis and Applications, Springer, 2018, pp. 55–84. ⟨hal-01543938⟩ DOI: 10.1007/978-3-319-78196-9_3


Citation. If you use this dataset, please cite the article [1]

@InProceedings{Bost2016,  author    = {Bost, Xavier and Labatut, Vincent and Gueye, Serigne and Linarès, Georges},  title     = {Narrative smoothing: dynamic conversational network for the analysis of {TV} Series plots},  booktitle = {2nd ASONAM International Workshop on Dynamics in Networks},  year      = {2016},  pages     = {1111-1118},  address   = {San Francisco, US},  publisher = {IEEE Publishing},  doi       = {10.1109/ASONAM.2016.7752379},}
DOI: 10.6084/m9.figshare.27143499.v1
title: Supplementary Material for: Spotlighting turnover costs for pediatric ocular oncology exams under anesthesia

description: Introduction: While pediatric intraocular malignancies are rare, management requires frequent examinations under anesthesia (EUAs) which are financial costly. The purpose of the study was to measure operating room (OR) turnover costs and to estimate potential cost savings of reducing turnover time.

Methods: Turnover time data for EUAs with a single ocular oncology provider at the University of Michigan were analyzed from 2021-2023. Direct cost of OR turnover was set at $25.73 per minute, and turnover time after business hours utilized a cost multiplier for overtime costs. A sensitivity analysis for turnover reduction savings was based on reducing turnover times to a goal turnover time. Primary outcomes were total and average turnover time and costs for pediatric ocular oncology EUAs. Secondary outcomes included the potential cost savings associated with reduced turnover time goals.

Results: 158 EUAs with valid turnover times were included. Total turnover time and costs were 5034 minutes and $134,104. Average (standard deviation) turnover time and costs were 31.9 minutes (19.0) and $848.76 ($487.48). A majority of cases (65%) had turnover time less than 30 minutes, though a minority (23%) had a turnover time less than 20 minutes. By reducing maximum turnover time to 20 minutes, a total of $52,057 could be saved across these cases, or $329 per case.

Conclusions: Turnover time accounts for a notable proportion of OR costs, particularly for shorter cases. The average pediatric ocular oncology case had nearly $850 of turnover costs, much of which may be avoidable. Actions aimed at reducing turnover time are likely to have high return on investments if they are successful.
DOI: 10.5281/zenodo.13866321
title: QCA-2024-SCC-XZ40900 R&D data

description: Project Code: QCA-2024-SCC-XZ40900.This project is classified as CONFIDENTIAL. All information, data, and results associated with this project are strictly restricted to authorized personnel only. Any dissemination, distribution, or copying of the project details is strictly prohibited without prior approval from the project lead and the university's Office of Research Compliance.
DOI: 10.5281/zenodo.13866322
title: QCA-2024-SCC-XZ40900 R&D data

description: Project Code: QCA-2024-SCC-XZ40900.This project is classified as CONFIDENTIAL. All information, data, and results associated with this project are strictly restricted to authorized personnel only. Any dissemination, distribution, or copying of the project details is strictly prohibited without prior approval from the project lead and the university's Office of Research Compliance.
DOI: 10.5281/zenodo.6815775
title: Serial Speakers: a Dataset of TV Series

description: Description. This dataset consists of 3 TV series with manual annotations:



Breaking Bad: S01--S05 (file 'bb.json');

Game of Thrones: S01--08 (file 'got.json');

House of Cards: S01--S02 (file 'hoc.json').


All three files are in `.json` format and contain TV Series annotated data.

Each TV Series is defined by its name, A TV Series contains seasons, defined by their ids. Every season is made of episodes, defined by their ids, titles, duration and fps. Each episode contains two basic kinds of data: scenes and speech segments. Scenes are defined by starting points and are made of shots (Seasons 1 only).A shot is defined by:



Starting and ending positions.

Recurring shot ids.


The speech segments are defined by their:



Starting and ending points.

Textual content (here encrypted for copyright reasons).

Speaker.

Possible interlocutors (for the following episodes only: bb: S01E04, S01E06, S02E03, S02E04; got: S01E03, S01E07, S01E08; hoc: S01E01, S01E07, S01E11).


All timestamps are expressed in seconds and are valid for the video files extracted from the commercial DVDs (PAL 25 FPS), with recaps (unannotated) included at the beginning of the House of Cards episodes.

A comprehensive description of the dataset can be found in the following paper:



X. Bost, V. Labatut, and G. Linarès, “Serial Speakers: a Dataset of TV Series,” in 12th Language Resources and Evaluation Conference, Marseille, FR, 2020, pp. 4249–4257. ⟨hal-02477736⟩


Source code. If you are interested in the textual content of the dataset, please consider using our text recovering tool on GitHub: https://github.com/bostxavier/Serial-Speakers

Citation. If you use these data or source code, please cite the above article.

@InProceedings{Bost2020,  author    = {Bost, Xavier and Labatut, Vincent and Linarès, Georges},  title     = {Serial Speakers: a Dataset of {TV} Series},  booktitle = {12\textsuperscript{th} Language Resources and Evaluation Conference},  year      = {2020},  pages     = {4249-4257},  address   = {Marseille, FR},  url= {http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.524.pdf},}
DOI: 10.5281/zenodo.6815776
title: Serial Speakers: a Dataset of TV Series

description: Description. This dataset consists of 3 TV series with manual annotations:



Breaking Bad: S01--S05 (file 'bb.json');

Game of Thrones: S01--08 (file 'got.json');

House of Cards: S01--S02 (file 'hoc.json').


All three files are in `.json` format and contain TV Series annotated data.

Each TV Series is defined by its name, A TV Series contains seasons, defined by their ids. Every season is made of episodes, defined by their ids, titles, duration and fps. Each episode contains two basic kinds of data: scenes and speech segments. Scenes are defined by starting points and are made of shots (Seasons 1 only).A shot is defined by:



Starting and ending positions.

Recurring shot ids.


The speech segments are defined by their:



Starting and ending points.

Textual content (here encrypted for copyright reasons).

Speaker.

Possible interlocutors (for the following episodes only: bb: S01E04, S01E06, S02E03, S02E04; got: S01E03, S01E07, S01E08; hoc: S01E01, S01E07, S01E11).


All timestamps are expressed in seconds and are valid for the video files extracted from the commercial DVDs (PAL 25 FPS), with recaps (unannotated) included at the beginning of the House of Cards episodes.

A comprehensive description of the dataset can be found in the following paper:



X. Bost, V. Labatut, and G. Linarès, “Serial Speakers: a Dataset of TV Series,” in 12th Language Resources and Evaluation Conference, Marseille, FR, 2020, pp. 4249–4257. ⟨hal-02477736⟩


Source code. If you are interested in the textual content of the dataset, please consider using our text recovering tool on GitHub: https://github.com/bostxavier/Serial-Speakers

Citation. If you use these data or source code, please cite the above article.

@InProceedings{Bost2020,  author    = {Bost, Xavier and Labatut, Vincent and Linarès, Georges},  title     = {Serial Speakers: a Dataset of {TV} Series},  booktitle = {12\textsuperscript{th} Language Resources and Evaluation Conference},  year      = {2020},  pages     = {4249-4257},  address   = {Marseille, FR},  url= {http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.524.pdf},}
DOI: 10.5281/zenodo.6815708
title: Remembering Winter Was Coming - Figures

description: Description. Figures used in the following article:



X. Bost, S. Gueye, V. Labatut, M. Larson, G. Linarès, D. Malinas & R. Roth, Remembering winter was coming: Character-oriented video summaries of TV series,” Multimedia Tools and Applications 78(24):35373–35399, 2019. ⟨hal-02278188⟩ DOI: 10.1007/s11042-019-07969-4


Citation. If you use these data, please cite the above article.

@Article{Bost2019,  author    = {Bost, Xavier and Gueye, Serigne and Labatut, Vincent and Larson, Martha and Linarès, Georges and Malinas, Damien and Roth, Raphaël},  title     = {Remembering Winter Was Coming: Character-oriented Video Summaries of {TV} Series},  journal   = {Multimedia Tools and Applications},  year      = {2019},  volume    = {78},  number    = {24},  pages     = {35373-35399},  doi       = {10.1007/s11042-019-07969-4},}
DOI: 10.5281/zenodo.6815709
title: Remembering Winter Was Coming - Figures

description: Description. Figures used in the following article:



X. Bost, S. Gueye, V. Labatut, M. Larson, G. Linarès, D. Malinas & R. Roth, Remembering winter was coming: Character-oriented video summaries of TV series,” Multimedia Tools and Applications 78(24):35373–35399, 2019. ⟨hal-02278188⟩ DOI: 10.1007/s11042-019-07969-4


Citation. If you use these data, please cite the above article.

@Article{Bost2019,  author    = {Bost, Xavier and Gueye, Serigne and Labatut, Vincent and Larson, Martha and Linarès, Georges and Malinas, Damien and Roth, Raphaël},  title     = {Remembering Winter Was Coming: Character-oriented Video Summaries of {TV} Series},  journal   = {Multimedia Tools and Applications},  year      = {2019},  volume    = {78},  number    = {24},  pages     = {35373-35399},  doi       = {10.1007/s11042-019-07969-4},}
DOI: 10.6084/m9.figshare.27143505
title: Data used in "Individually Addressed Entangling Gates in a Two-Dimensional Ion Crystal"

description: Data of 5 figures in the main text and supplementary information for Nature Communications manuscript "Individually Addressed Entangling Gates in a Two-Dimensional Ion Crystal"
DOI: 10.5281/zenodo.13866037
title: Data Set available for Liquid Biopsy of CML Diagnosis

description: Chronic Myeloid Leukemia, CML, is caused in patients with myeloproliferative disorders by the presence of BCR::ABL1.  The research discussed the integration of microfluidic chips with biosensors for the purpose of detecting biomarkers in peripheral blood thereby showing the raw experimental data set avaialble, liquid bipsy sample set available and Calculation for methods and data Simulation along with graphs, which could obviate the need for painful biopsies, thereby improving diagnostic accuracy and accessibility.
DOI: 10.5281/zenodo.13866038
title: Data Set available for Liquid Biopsy of CML Diagnosis

description: Chronic Myeloid Leukemia, CML, is caused in patients with myeloproliferative disorders by the presence of BCR::ABL1.  The research discussed the integration of microfluidic chips with biosensors for the purpose of detecting biomarkers in peripheral blood thereby showing the raw experimental data set avaialble, liquid bipsy sample set available and Calculation for methods and data Simulation along with graphs, which could obviate the need for painful biopsies, thereby improving diagnostic accuracy and accessibility.
DOI: 10.6084/m9.figshare.27141441
title: Nucleocentric-Profiling

description: Test data associated with the ELife publication 'Unbiased identification of cell identity in dense mixed neural cultures'.
DOI: 10.5281/zenodo.13865920
title: MoMA Collection - Automatic Update

description: Temporary Description
DOI: 10.5281/zenodo.13865921
title: MoMA Collection - Automatic Update

description: Temporary Description
DOI: 10.6084/m9.figshare.24264544.v5
title: 在当前研究期间生成和分析的数据集的补充材料。

description: Supplementary Material S1: The primer sequences.Supplementary Material S2: Summary of transcriptome sequencing data, transcriptome assembly, and functional annotation of unigenes.Supplementary Material S3: Analysis of unigenes differential expression and a summary table of the data. Corresponding to the data from Fig. 2E–R.Supplementary Material S4: GO annotations analysis of DEGs. Corresponding to the data from Fig. 3C and D.Supplementary Material S5: eggNOG annotations analysis of DEGs. Corresponding to the data from Fig. 3E and F.Supplementary Material S6: KEGG enrichment analysis of DEGs classified by eggNOG for RNA processing and modification, cell motility, and nuclear structure in Sf21 cells.Supplementary Material S7: DEGs present in RIRI-PA1 and Sf21 cells across all time points.Supplementary Material S8: KEGG enrichment analysis of DEGs for the expression of recombinant protein in cells. Corresponding to the data from Fig. 3I–N.Supplementary Material S9: The protein expression-related DEGs annotated by KEGG enrichment in cells. Corresponding to the data from Fig. 3I–N, O and P.Supplementary Material S10: The intracellular environment regulation-related DEGs annotated by KEGG enrichment in cells. Corresponding to the data from Fig. 3I–N, Q and R.Supplementary Material S11: Overall analysis of the KEGG enrichment of DEGs in two cell lines.Supplementary Material S12: Analysis of DEGs significantly enriched only in Sf21 cells.Supplementary Material S13: Annotation information of selected genes in the PCR experiment. Corresponding to the information in Fig. 4.
DOI: 10.15468/dl.6y9xnd
title: Occurrence Download

description: A dataset containing 1512208412 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is EOD – eBird Observation Dataset"
 ]
}

 The dataset includes 1512208412 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036492-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13818617
title: Early Members of the Leopoldina (1652-1818): Biographical Data

description: This data set encompasses a collection of detailed biographical data about 850 of the first members of the German National Academy of Sciences Leopoldina (Deutsche Akademie der Naturforscher Leopoldina – Nationale Akademie der Wissenschaften) from 1652 to 1818. The data includes information about the members themselves, their family, their membership in the Leopoldina, academic and professional positions held, as well as works, portraits, and associated sources.
DOI: 10.5281/zenodo.13818618
title: Early Members of the Leopoldina (1652-1818): Biographical Data

description: This data set encompasses a collection of detailed biographical data about 850 of the first members of the German National Academy of Sciences Leopoldina (Deutsche Akademie der Naturforscher Leopoldina – Nationale Akademie der Wissenschaften) from 1652 to 1818. The data includes information about the members themselves, their family, their membership in the Leopoldina, academic and professional positions held, as well as works, portraits, and associated sources.
DOI: 10.5281/zenodo.6815495
title: NetVotes Dataset

description: The NetVote dataset contains the outputs of the NetVote program when applied to voting data coming from VoteWatch (http://www.votewatch.eu/). The tool itself is available on GitHub (https://github.com/CompNet/NetVotes). These results were used in the following conference papers: I. Mendonça, R. Figueiredo, V. Labatut, and P. Michelon, “Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the European Parliament,” in <em>2nd European Network Intelligence Conference</em>, 2015, pp. 122–129. 〈hal-01176090〉 DOI: 10.1109/ENIC.2015.25 I. Mendonça, R. Figueiredo, V. Labatut, and P. Michelon, “Informative Value of Negative Links for Graph Partitioning, with an application to European Parliament Votes,” in <em>6ème Conférence sur les modèles et lánalyse de réseaux : approches mathématiques et informatiques</em>, 2015, p. 12p. 〈hal-02055158〉 If you use our dataset or tool, please cite the first article. Contact: Vincent Labatut &lt;vincent.labatut@univ-avignon.fr&gt; & Rosa Figueiredo &lt;rosa.figueiredo@univ-avignon.fr&gt;
DOI: 10.15468/dl.r37nu2
title: Occurrence Download

description: A dataset containing 2627106 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-76.82746 39.19302, -76.42736 39.19302, -76.42736 39.46043, -76.82746 39.46043, -76.82746 39.19302))",
 "Year is greater than or equal to 2010",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 2627106 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036514-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3ezx22
title: Occurrence Download

description: A dataset containing 222283 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ClassKey is one of (Pezizomycetes)",
 "Continent is Europe",
 "Year is greater than or equal to 1960",
 "Year is less than or equal to 2020",
 "OccurrenceStatus is Present",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Fossil, Human Observation, Living Specimen, Observation, Occurrence evidence, Specimen)"
 ]
}

 The dataset includes 222283 records from 487 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036508-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ey6ybf
title: Occurrence Download

description: A dataset containing 1380 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Fungal communities in decaying poplar pins in Northeastern USA discovered by MycoPins method"
 ]
}

 The dataset includes 1380 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036522-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.