cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.15162197
title: Additional file 2 of The baculum affects paternity success of first but not second males in house mouse sperm competition

description: Additional file 2. Raw Data.
DOI: 10.6084/m9.figshare.15162197.v1
title: Additional file 2 of The baculum affects paternity success of first but not second males in house mouse sperm competition

description: Additional file 2. Raw Data.
DOI: 10.6084/m9.figshare.15162216
title: Additional file 2 of Influenza vaccination should have no border: cost-effectiveness of cross-border subsidy

description: Additional file 2. In-person survey raw data.
DOI: 10.6084/m9.figshare.15162216.v1
title: Additional file 2 of Influenza vaccination should have no border: cost-effectiveness of cross-border subsidy

description: Additional file 2. In-person survey raw data.
DOI: 10.25959/sd4n-9v54
title: SSR data from Yost et al  (2021) Forests paper on Californian landrace of 𝘌𝘶𝘤𝘢𝘭𝘺𝘱𝘵𝘶𝘴 𝘨𝘭𝘰𝘣𝘶𝘭𝘶𝘴

description: SSR data for native and Californian landrace samples of 𝘌𝘶𝘤𝘢𝘭𝘺𝘱𝘵𝘶𝘴 𝘨𝘭𝘰𝘣𝘶𝘭𝘶𝘴 used for the STRUCTURE analysis reported in Table 1 and Supplementary material Table S1 of:  

Yost JM, Wise SL, Love NLR, Steane DA, Jones RC, Ritter MK and Potts BM (2021) Origins, diversity and naturalization of 𝘌𝘶𝘤𝘢𝘭𝘺𝘱𝘵𝘶𝘴 𝘨𝘭𝘰𝘣𝘶𝘭𝘶𝘴 (Myrtaceae) in California. Forests.  

Data is in the format of a STRUCTURE input file with six SSR loci - EMB19 EMB30 CRC2 CRC7 CRC10 CRC11.  The tree grouping (second column) corresponds to Victoria (1), Furneaux (2), King Is./W Tas (3), Eastern Tasmania (4) and Californian Landrace (0). The third column identifies the native stand (1) and Californian landrace samples (0).   

The Californian landrace sample codes follow Table S1 in the above paper and the provenance of the Australian samples is listed in the attached supplementary file.
DOI: 10.6084/m9.figshare.15162306
title: Additional file 2 of CAZymes from the thermophilic fungus Thermoascus aurantiacus are induced by C5 and C6 sugars

description: Additional file 2. Tables for differential expression by T. aurantiacus induced with C5 and C6 sugars. Data is shown for genes whose differential expression has an adjusted pval
DOI: 10.6084/m9.figshare.15162306.v1
title: Additional file 2 of CAZymes from the thermophilic fungus Thermoascus aurantiacus are induced by C5 and C6 sugars

description: Additional file 2. Tables for differential expression by T. aurantiacus induced with C5 and C6 sugars. Data is shown for genes whose differential expression has an adjusted pval
DOI: 10.6084/m9.figshare.15162339
title: Selected English and Taiwanese Judicial Decisions on Tort Liability

description: This is a collection of 10 English and one Taiwanese court cases on tort liability.<br>
DOI: 10.5281/zenodo.5194135
title: Identifying contributors to PM2.5 simulation biases of chemical transport model using fully connected neural networks

description: The processed data and codes in the study are included. <strong>Source data:</strong> The training and testing dataset is composed of observed and simulated data of pollutants and meteorology in the BTH and YRD regions in the whole year of 2015. The processed datasets used for training are named as "dataset_BTH" and "dataset_YRD" in the folder. <em>The hourly observed pollution data</em> are from China National Urban Air Quality Real-time Release Platform of the National Environmental Monitoring Station <em>The hourly simulated pollutants data</em> comes from the output of WRF-CMAQv5.2 (spatial resolution of 27 km). <em>Meteorological observation data</em> is provided by China Meteorological Data Service Centre <em>The meteorological simulation data</em> comes from the simulation results of the WRF model <strong>Codes:</strong> preprocessing of raw CMAQ data, observed pollution data and meteorological data bulid and train process of fully connected neural networks calculation of correlation between variables feature selection method contribution analysis
DOI: 10.5281/zenodo.5194134
title: Identifying contributors to PM2.5 simulation biases of chemical transport model using fully connected neural networks

description: The processed data and codes in the study are included. <strong>Source data:</strong> The training and testing dataset is composed of observed and simulated data of pollutants and meteorology in the BTH and YRD regions in the whole year of 2015. The processed datasets used for training are named as "dataset_BTH" and "dataset_YRD" in the folder. <em>The hourly observed pollution data</em> are from China National Urban Air Quality Real-time Release Platform of the National Environmental Monitoring Station <em>The hourly simulated pollutants data</em> comes from the output of WRF-CMAQv5.2 (spatial resolution of 27 km). <em>Meteorological observation data</em> is provided by China Meteorological Data Service Centre <em>The meteorological simulation data</em> comes from the simulation results of the WRF model <strong>Codes:</strong> preprocessing of raw CMAQ data, observed pollution data and meteorological data bulid and train process of fully connected neural networks calculation of correlation between variables feature selection method contribution analysis
DOI: 10.6084/m9.figshare.15162360.v1
title: Data and Code for STICC

description: Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated clusters with spatial contiguity maintained. In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering. Different from the traditional clustering methods that treat each geographic object independently, a subregion of each object is created serving as the basic unit when performing clustering. A Markov random field (MRF) is constructed to characterize the attribute dependencies of subregions. Using a spatial consistency strategy, nearby objects are encouraged to belong to the same cluster. To test the performance of the proposed STICC algorithm, we apply it in three use cases. The comparison results with several baseline methods show that the STICC outperforms others significantly in terms of adjusted rand index and macro-F1. Moran's I index is also calculated and shows the spatial dependency is well preserved by STICC. Such a spatial clustering method may benefit various applications in the fields of geography, remote sensing, transportation, and urban planning, etc.
DOI: 10.6084/m9.figshare.15162360
title: Data and Code for STICC

description: Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated clusters with spatial contiguity maintained. In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering. Different from the traditional clustering methods that treat each geographic object independently, a subregion of each object is created serving as the basic unit when performing clustering. A Markov random field (MRF) is constructed to characterize the attribute dependencies of subregions. Using a spatial consistency strategy, nearby objects are encouraged to belong to the same cluster. To test the performance of the proposed STICC algorithm, we apply it in three use cases. The comparison results with several baseline methods show that the STICC outperforms others significantly in terms of adjusted rand index and macro-F1. Moran's I index is also calculated and shows the spatial dependency is well preserved by STICC. Such a spatial clustering method may benefit various applications in the fields of geography, remote sensing, transportation, and urban planning, etc.
DOI: 10.5281/zenodo.5189261
title: Wikidata Dump P856

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.5189260
title: Wikidata Dump P856

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5517/ccdc.csd.cc24cj79
title: CCDC 1977280: Experimental Crystal Structure Determination

description: Related Article: Pathan Mosim Amin, Qi Cui, Shaozhong Wang, Zhi-Xiang Yu|2021|Org.Lett.|23|5669|doi:10.1021/acs.orglett.1c01782
DOI: 10.5517/ccdc.csd.cc23g1xk
title: CCDC 1949928: Experimental Crystal Structure Determination

description: Related Article: Abhishek Mondal, Javid Ahmad Malla, Harshad Paithankar, Shilpy Sharma, Jeetender Chugh, Pinaki Talukdar|2021|Org.Lett.|23|6131|doi:10.1021/acs.orglett.1c02235
DOI: 10.5517/ccdc.csd.cc25wtrn
title: CCDC 2022742: Experimental Crystal Structure Determination

description: Related Article: Abhishek Mondal, Javid Ahmad Malla, Harshad Paithankar, Shilpy Sharma, Jeetender Chugh, Pinaki Talukdar|2021|Org.Lett.|23|6131|doi:10.1021/acs.orglett.1c02235
DOI: 10.5517/ccdc.csd.cc26pj4l
title: CCDC 2046469: Experimental Crystal Structure Determination

description: Related Article: María Gómez-Pantoja, Juan I. González-Pérez, Avelino Martín, Miguel Mena, Cristina Santamaría, Manuel Temprado|2021|Organometallics|40|2610|doi:10.1021/acs.organomet.1c00300
DOI: 10.5517/ccdc.csd.cc26pj5m
title: CCDC 2046470: Experimental Crystal Structure Determination

description: Related Article: María Gómez-Pantoja, Juan I. González-Pérez, Avelino Martín, Miguel Mena, Cristina Santamaría, Manuel Temprado|2021|Organometallics|40|2610|doi:10.1021/acs.organomet.1c00300
DOI: 10.5517/ccdc.csd.cc26pj6n
title: CCDC 2046471: Experimental Crystal Structure Determination

description: Related Article: María Gómez-Pantoja, Juan I. González-Pérez, Avelino Martín, Miguel Mena, Cristina Santamaría, Manuel Temprado|2021|Organometallics|40|2610|doi:10.1021/acs.organomet.1c00300
DOI: 10.5517/ccdc.csd.cc26pj7p
title: CCDC 2046472: Experimental Crystal Structure Determination

description: Related Article: María Gómez-Pantoja, Juan I. González-Pérez, Avelino Martín, Miguel Mena, Cristina Santamaría, Manuel Temprado|2021|Organometallics|40|2610|doi:10.1021/acs.organomet.1c00300
DOI: 10.5517/ccdc.csd.cc26pj8q
title: CCDC 2046473: Experimental Crystal Structure Determination

description: Related Article: María Gómez-Pantoja, Juan I. González-Pérez, Avelino Martín, Miguel Mena, Cristina Santamaría, Manuel Temprado|2021|Organometallics|40|2610|doi:10.1021/acs.organomet.1c00300
DOI: 10.5517/ccdc.csd.cc26pj9r
title: CCDC 2046474: Experimental Crystal Structure Determination

description: Related Article: María Gómez-Pantoja, Juan I. González-Pérez, Avelino Martín, Miguel Mena, Cristina Santamaría, Manuel Temprado|2021|Organometallics|40|2610|doi:10.1021/acs.organomet.1c00300
DOI: 10.5517/ccdc.csd.cc26pjbs
title: CCDC 2046475: Experimental Crystal Structure Determination

description: Related Article: María Gómez-Pantoja, Juan I. González-Pérez, Avelino Martín, Miguel Mena, Cristina Santamaría, Manuel Temprado|2021|Organometallics|40|2610|doi:10.1021/acs.organomet.1c00300
DOI: 10.5517/ccdc.csd.cc26pjct
title: CCDC 2046476: Experimental Crystal Structure Determination

description: Related Article: María Gómez-Pantoja, Juan I. González-Pérez, Avelino Martín, Miguel Mena, Cristina Santamaría, Manuel Temprado|2021|Organometallics|40|2610|doi:10.1021/acs.organomet.1c00300
DOI: 10.5517/ccdc.csd.cc26pjdv
title: CCDC 2046477: Experimental Crystal Structure Determination

description: Related Article: María Gómez-Pantoja, Juan I. González-Pérez, Avelino Martín, Miguel Mena, Cristina Santamaría, Manuel Temprado|2021|Organometallics|40|2610|doi:10.1021/acs.organomet.1c00300
DOI: 10.5517/ccdc.csd.cc26zpc8
title: CCDC 2055280: Experimental Crystal Structure Determination

description: Related Article: B. Sathish Kumar, Brijesh Chandra, K. V. Jovan Jose, Pradeepta K. Panda|2021|J.Org.Chem.|86|10536|doi:10.1021/acs.joc.1c01179
DOI: 10.5517/ccdc.csd.cc27940q
title: CCDC 2064352: Experimental Crystal Structure Determination

description: Related Article: Minghao Zhang, Wenbo Deng, Mingjie Sun, Liwei Zhou, Guobo Deng, Yun Liang, Yuan Yang|2021|Org.Lett.|23|5744|doi:10.1021/acs.orglett.1c01888
DOI: 10.5517/ccdc.csd.cc27r326
title: CCDC 2077777: Experimental Crystal Structure Determination

description: Related Article: Yongan Feng, Jie Wang, Zhimin Li|2021|Cryst.Growth Des.|21|4725|doi:10.1021/acs.cgd.1c00537
DOI: 10.5517/ccdc.csd.cc27r337
title: CCDC 2077778: Experimental Crystal Structure Determination

description: Related Article: Yongan Feng, Jie Wang, Zhimin Li|2021|Cryst.Growth Des.|21|4725|doi:10.1021/acs.cgd.1c00537
DOI: 10.5517/ccdc.csd.cc280rfh
title: CCDC 2086096: Experimental Crystal Structure Determination

description: Related Article: Yongan Feng, Jie Wang, Zhimin Li|2021|Cryst.Growth Des.|21|4725|doi:10.1021/acs.cgd.1c00537
DOI: 10.5517/ccdc.csd.cc27r315
title: CCDC 2077776: Experimental Crystal Structure Determination

description: Related Article: Yongan Feng, Jie Wang, Zhimin Li|2021|Cryst.Growth Des.|21|4725|doi:10.1021/acs.cgd.1c00537
DOI: 10.15121/1812884
title: Utah FORGE Well 16A(78)-32 Core Photos

description: Images of core samples collected from Utah FORGE well 16A(78)-32. These images were created by stitching together multiple photographs resulting in a circumferential view of the cores exterior in two dimensions. Core footages (measured depths) are indicated in the file names, and are annotated on each image. The images, of which there are 30 in the .zip file, are in a .jpg format.
DOI: 10.6084/m9.figshare.15162549.v1
title: Figure 6B.xlsx

description: PF-07304814 and PF-00835231 predicted human PK parameters
DOI: 10.6084/m9.figshare.15162549
title: Figure 6B.xlsx

description: PF-07304814 and PF-00835231 predicted human PK parameters
DOI: 10.6084/m9.figshare.15162780
title: Figure S2A.xlsx

description: PF-07304814 Substrate saturation plot in human liver S9
DOI: 10.6084/m9.figshare.15162768
title: Table S7.xlsx

description: In vitro transporter assessment by PF-07304814 or PF-00835231
DOI: 10.6084/m9.figshare.15162768.v1
title: Table S7.xlsx

description: In vitro transporter assessment by PF-07304814 or PF-00835231
DOI: 10.6084/m9.figshare.15162774
title: Table S5.xlsx

description: Reversible CYP inhibition by PF-07304814 and PF-00835231 in human liver microsomes
DOI: 10.6084/m9.figshare.15162792.v1
title: Table S10.xlsx

description: Predicted PK parameters of PF-00835231 with and without coadministration of itraconazole
DOI: 10.6084/m9.figshare.15162792
title: Table S10.xlsx

description: Predicted PK parameters of PF-00835231 with and without coadministration of itraconazole
DOI: 10.14457/su.res.2016.8
title: การศึกษาความเป็นไปได้ทางเศรษฐกิจครัวเรือนในการผลิตทานตะวันของเกษตรกรที่อำเภอหัวหิน จังหวัดประจวบคีรีขันธ์
DOI: 10.12755/nrct.res.2016.11
title: การตรวจสอบกลไกของเชื้อไรโซเบียมที่ใช้ส่งเสริมการเจริญของถั่วลิสงภายใต้สภาวะน้ำท่วม (Investigation of rhizobial mechanisms associated with enhancing peanut growth under flooding condition)
DOI: 10.6084/m9.figshare.15162771
title: Table S4.xlsx

description: Evaluation of fractional metabolism of PF-00835231 in human liver microsomes
DOI: 10.6084/m9.figshare.15162771.v1
title: Table S4.xlsx

description: Evaluation of fractional metabolism of PF-00835231 in human liver microsomes
DOI: 10.6084/m9.figshare.15162777
title: Table S11.xlsx

description: Metabolic stability and enzyme kinetics of PF-07304814 in liver S9
DOI: 10.6084/m9.figshare.15162777.v1
title: Table S11.xlsx

description: Metabolic stability and enzyme kinetics of PF-07304814 in liver S9
DOI: 10.6084/m9.figshare.15162783
title: Table S9.xlsx

description: Preclinical PK of PF-07304814 or PF-00835231 in rats, dogs and monkeys following IV or oral administration.
DOI: 10.6084/m9.figshare.15162789
title: Table S6.xlsx

description: Time dependent inhibition of CYP3A4/5 by PF-00835231 in human liver microsomes
DOI: 10.6084/m9.figshare.15162786
title: Table S8.xlsx

description: Plasma protein binding of PF-07304814 or PF-00835231 in plasma, liver microsomes and S9.
DOI: 10.5281/zenodo.5166790
title: WtaGraph: Web Tracking and Advertising Detection using Graph Neural Networks

description: Dataset release for our IEEE Symposium on Security and Privacy 2022 paper entitled "WtaGraph: Web Tracking and Advertising Detection using Graph Neural Networks" These files are pretty self-explanatory: there are node/edge features of a specified (full graph or random 5K graph). To use these files, make sure to check out GitHub here. Basically, you need put each of these files in a correct folder as detailed on GitHub.
DOI: 10.5281/zenodo.5166789
title: WtaGraph: Web Tracking and Advertising Detection using Graph Neural Networks

description: Dataset release for our IEEE Symposium on Security and Privacy 2022 paper entitled "WtaGraph: Web Tracking and Advertising Detection using Graph Neural Networks" These files are pretty self-explanatory: there are node/edge features of a specified (full graph or random 5K graph). To use these files, make sure to check out GitHub here. Basically, you need put each of these files in a correct folder as detailed on GitHub.
DOI: 10.5281/zenodo.5189275
title: Multi-channel Surface EMG Dataset for Fatigue analysis

description: This is the data used in paper "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG" DOI: 10.1109/NILES50944.2020.9257909 Data can be found as a txt files for each subject separately or can be found as .mat file with all subjects included. Data details: Sampling Frequency= 200 Hz 8-Bit resolution 15 Healthy Subjects 6 Kg Load with elbow flexed to a 90 angle 120 Seconds Duration 8 channels sEMG 50 Hz Notch Filtered For more details and citation: A. Ebied, A. M. Awadallah, M. A. Abbass and Y. El-Sharkawy, "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG," 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 423-427, doi: 10.1109/NILES50944.2020.9257909.
description: {"references": ["A. Ebied, A. M. Awadallah, M. A. Abbass and Y. El-Sharkawy, \"Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG,\" 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 423-427, doi: 10.1109/NILES50944.2020.9257909."]}
DOI: 10.5281/zenodo.5189274
title: Multi-channel Surface EMG Dataset for Fatigue analysis

description: This is the data used in paper "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG" DOI: 10.1109/NILES50944.2020.9257909 Data can be found as a txt files for each subject separately or can be found as .mat file with all subjects included. Data details: Sampling Frequency= 200 Hz 8-Bit resolution 15 Healthy Subjects 6 Kg Load with elbow flexed to a 90 angle 120 Seconds Duration 8 channels sEMG 50 Hz Notch Filtered For more details and citation: A. Ebied, A. M. Awadallah, M. A. Abbass and Y. El-Sharkawy, "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG," 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 423-427, doi: 10.1109/NILES50944.2020.9257909.
description: {"references": ["A. Ebied, A. M. Awadallah, M. A. Abbass and Y. El-Sharkawy, \"Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG,\" 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 423-427, doi: 10.1109/NILES50944.2020.9257909."]}
DOI: 10.25388/nwu.15000393
title: Code-switching among Afrikaans-Dutch bilingual children_Addendum D_MR20210718.xlsx

description: To describe the code-switching in the speech of pre-school, bilingual Afrikaans-Dutch children, novel language data of eight preschool children between the ages of 2;3 and 6;4 who are raised bilingually in Afrikaans and Dutch in South Africa were collected and analysed. The parent-report method was employed to collect the data, and the data collection period lasted a total of approximately two months.The NWU HSSEC ethics number for the research is: NWU-02043-20-S7
DOI: 10.20375/0000-000e-6729-1
title: Klassifikation von Tragödien und Komödien bei Calderón de la Barca

description: Datenpublikation zum Artikel "Klassifikation von Tragödien und Komödien bei Calderón de la Barca": Gesprochener Text von 64 Dramen Pedro Calderón de la Barcas, R-Code-Dokumentation, Quellennachweise
DOI: 10.20375/0000-000e-672a-0
title: 00 Skip-Gram-Calderon.R

description: R Code: Skip-Gram-Matrix auf 64 Calderón-Dramen. R-Datei, UTF-8 codiert
DOI: 10.20375/0000-000e-672b-f
title: 01 Volltext-TFNorm-WardD2-Calderon.R

description: R-Code: Unüberwachtes document embedding-Verfahren No. 1; R-Datei, UTF-8 codiert
DOI: 10.20375/0000-000e-672c-e
title: 02 Volltext-Sparsity-WardD2-Calderon.R

description: R-Code: Unüberwachtes document embedding-Verfahren No. 2, R-Datei, UTF-8 codiert
DOI: 10.20375/0000-000e-672d-d
title: 03a POS-Tagging-Calderon.R

description: R-Code: Part-of-Speech-tagging einzelner spanischer Dramen, R-Datei, UTF-codiert
DOI: 10.20375/0000-000e-672e-c
title: 03b POS-Clustering-Calderon.R

description: R-Code: Unüberwachtes document embedding-Verfahren No. 3, R-Datei, UTF-8 codiert
DOI: 10.20375/0000-000e-672f-b
title: 04 TFIDF-Calderon.R

description: R-Code: Unüberwachtes document embedding-Verfahren No. 4, R-Datei, UTF-8 codiert
DOI: 10.20375/0000-000e-6730-8
title: 05a Korpusvorbereitung-Fuer-fastText-Calderon.R

description: R-Code: Korpusvorbereitung für fastText, R-Datei, UTF-8 codiert, Herstellen des Inputs für fastText
DOI: 10.20375/0000-000e-6731-7
title: 05b Vokabularanalyse-fastText-Calderon.R

description: R-Code: Analyse mit fastText, R-Datei, UTF-8 codiert
DOI: 10.20375/0000-000e-6732-6
title: 06 KNN-Textklassifikation-Calderon.R

description: R-Code: Textklassifikation mit K-Nearest Neighbors, R-Datei, UTF-8 codiert
DOI: 10.20375/0000-000e-6733-5
title: 330LLwords-Komoedien+Tragoedien.ods

description: Tabelle enthält 330 Worte mit dem höchsten Log-likelihood-Wert; tabellarische Übersicht von je 130 Worten je Komödien- und Tragödiensubkorpus sowie 70 Worten, die in beiden Subkorpora identisch auftauchen
DOI: 10.20375/0000-000e-6734-4
title: ComediaNueva.csv

description: Quellennachweis für die 64 verwendeten Dramen Pedro Calderón de la Barcas; UTF-8 codierte csv-Datei (comma separated values - keine Semikolons!)
DOI: 10.20375/0000-000e-6736-2
title: C5.txt

description: 64 Dramen Calderón de la Barcas, txt-Dateien, UTF-8 codiert, 15 davon Komödien (Dateiname mit C beginnend), 15 Tragödien (mit T beginnend), 35 Dramen unbekannter Klassifikation (mit "Test" beginnend)
DOI: 10.20375/0000-000e-6737-1
title: C7.txt

description: 64 Dramen Calderón de la Barcas, txt-Dateien, UTF-8 codiert, 15 davon Komödien (Dateiname mit C beginnend), 15 Tragödien (mit T beginnend), 35 Dramen unbekannter Klassifikation (mit "Test" beginnend)
DOI: 10.20375/0000-000e-6738-0
title: C4.txt

description: 64 Dramen Calderón de la Barcas, txt-Dateien, UTF-8 codiert, 15 davon Komödien (Dateiname mit C beginnend), 15 Tragödien (mit T beginnend), 35 Dramen unbekannter Klassifikation (mit "Test" beginnend)
DOI: 10.20375/0000-000e-6739-f
title: C6.txt

description: 64 Dramen Calderón de la Barcas, txt-Dateien, UTF-8 codiert, 15 davon Komödien (Dateiname mit C beginnend), 15 Tragödien (mit T beginnend), 35 Dramen unbekannter Klassifikation (mit "Test" beginnend)
DOI: 10.20375/0000-000e-673a-e
title: C8.txt

description: 64 Dramen Calderón de la Barcas, txt-Dateien, UTF-8 codiert, 15 davon Komödien (Dateiname mit C beginnend), 15 Tragödien (mit T beginnend), 35 Dramen unbekannter Klassifikation (mit "Test" beginnend)
DOI: 10.20375/0000-000e-673b-d
title: C11.txt

description: 64 Dramen Calderón de la Barcas, txt-Dateien, UTF-8 codiert, 15 davon Komödien (Dateiname mit C beginnend), 15 Tragödien (mit T beginnend), 35 Dramen unbekannter Klassifikation (mit "Test" beginnend)