cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bghkw
title: CCDC 2158888: Experimental Crystal Structure Determination

description: Related Article: Steffen Pospiech, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgjp1
title: CCDC 2158923: Experimental Crystal Structure Determination

description: Related Article: Deniz Güclü, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgjq2
title: CCDC 2158924: Experimental Crystal Structure Determination

description: Related Article: Deniz Güclü, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgjr3
title: CCDC 2158925: Experimental Crystal Structure Determination

description: Related Article: Deniz Güclü, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgk9p
title: CCDC 2158942: Experimental Crystal Structure Determination

description: Related Article: Felicitas von Rekowski, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgkbq
title: CCDC 2158943: Experimental Crystal Structure Determination

description: Related Article: Felicitas von Rekowski, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgkcr
title: CCDC 2158944: Experimental Crystal Structure Determination

description: Related Article: Felicitas von Rekowski, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgkds
title: CCDC 2158945: Experimental Crystal Structure Determination

description: Related Article: Felicitas von Rekowski, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgkft
title: CCDC 2158946: Experimental Crystal Structure Determination

description: Related Article: Felicitas von Rekowski, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.48370/ofd/1taoan/rbwzza
title: Betula pendula_pędy z defoliacją 30 procent i zainfekowane Phytophthora plurivora - ekstrakt eterowy.pdf

description: :unav
DOI: 10.5517/ccdc.csd.cc2bgkr4
title: CCDC 2158956: Experimental Crystal Structure Determination

description: Related Article: Estera Januszewski, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgks5
title: CCDC 2158957: Experimental Crystal Structure Determination

description: Related Article: Estera Januszewski, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgkt6
title: CCDC 2158958: Experimental Crystal Structure Determination

description: Related Article: Estera Januszewski, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.48370/ofd/1taoan
title: Betula pendula_pędy z defoliacją 30 procent i zainfekowane Phytophthora plurivora - ekstrakt eterowy

description: ekstrakt eterowy
DOI: 10.48370/ofd/abmcvm/lskzc3
title: Betula pendula_pędy z defoliacją 60 procent i zainfekowane Phytophthora plurivora - ekstrakt eterowy.pdf

description: :unav
DOI: 10.48370/ofd/abmcvm
title: Betula pendula_pędy z defoliacją 60 procent i zainfekowane Phytophthora plurivora - ekstrakt eterowy

description: ekstrakt eterowy
DOI: 10.48596/pgn.rsus1p1-8.mexicocity-vallejo.p157s1
title: Pandora 157s1 direct sun SO2 total vertical column amount at MexicoCity-Vallejo; Processing Code: sus1; Processor Version: 1-8
DOI: 10.7910/dvn/4121kv
title: Estudio Longitudinal Social de Chile 2016-2021

description: ELSOC has been designed to evaluate how Chileans think, feel, and behave regarding a set of social issues related to conflict and social cohesion in Chile. Because of its nature, this study seeks to analyze, in a national representative sample, the stability or change in several social dimensions attending factors that can moderate or explain it through the years. These datasets contain 5 waves of the study merged in wide and long format (2016-2021).
DOI: 10.5281/zenodo.4630206
title: Supplementary Information for "Diversification of 4ʹ-Methylated Nucleosides by Nucleoside Phosphorylases"

description: This is the external Supplementary Information for our publication "Diversification of 4ʹ-Methylated Nucleosides by Nucleoside Phosphorylases". <strong>IMPORTANT: This is only the zero version. To access the dataset, see the most recent version</strong> (either via the banner above or the version navigator to the right). The .pdf file contains the Supplementary Information: author contributions, experimental procedures and supplementary items, among others. The .zip file contains the raw data and metadata for all items (supplementary and main text) as well as the calculations. This includes UV, HPLC, HRMS and NMR data. The Python code used for spectral unmixing is available on this platform (https://doi.org/10.5281/zenodo.3243376) and has been described in our previous publications (https://doi.org/10.3390/mps2030060, https://doi.org/10.5281/zenodo.3333469, https://doi.org/10.1002/cbic.202000204, https://doi.org/10.5281/zenodo.3723806). Further applications of this method can be found in our recent articles in <em>Adv. Synth. Catal.</em> (https://doi.org/10.1002/adsc.201901230) and its supporting material (https://doi.org/10.5281/zenodo.3568858) and in <em>ChemBioChem</em> (https://doi.org/10.1002/cbic.202000679) and its supporting material on this platform (https://doi.org/10.5281/zenodo.4284188). The equilibrium shift method employed for the half-life determination of the methylated sugar phosphate is described in detail in our article in ChemPhysChem (https://doi.org/10.1002/cphc.202000901).
description: This is the zero version, solely containing a placeholder. To access the dataset, see the most recent version (either via the banner above or the version navigator to the right).
DOI: 10.5281/zenodo.6321987
title: TROPOMI SIF high resolution data at 0.005° for CONUS as estimated by the convolutional neural network SIFnet

description: We develop a Convolutional Neural Network, named SIFnet, that increases the spatial resolution of SIF from the TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations together with a broad range high resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis. Temporal coverage: April 2018 until March 2021, 16 day time steps Data for other regions can be requested and produced by the authors. Please refer for further information to: Gensheimer, J., Turner, A. J., Köhler, P., Frankenberg, C., & Chen, J. (2022). A Convolutional Neural Network for Spatial Downscaling of Satellite-Based Solar-Induced Chlorophyll Fluorescence (SIFnet). A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet). <em>Biogeosciences</em>, <em>19</em>(6), 1777-1793. DOI: https://doi.org/10.5194/bg-19-1777-2022
DOI: 10.5281/zenodo.6321986
title: TROPOMI SIF high resolution data at 0.005° for CONUS as estimated by the convolutional neural network SIFnet

description: We develop a Convolutional Neural Network, named SIFnet, that increases the spatial resolution of SIF from the TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations together with a broad range high resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis. Temporal coverage: April 2018 until March 2021, 16 day time steps Data for other regions can be requested and produced by the authors. Please refer for further information to: Gensheimer, J., Turner, A. J., Köhler, P., Frankenberg, C., & Chen, J. (2022). A Convolutional Neural Network for Spatial Downscaling of Satellite-Based Solar-Induced Chlorophyll Fluorescence (SIFnet). A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet). <em>Biogeosciences</em>, <em>19</em>(6), 1777-1793. DOI: https://doi.org/10.5194/bg-19-1777-2022
DOI: 10.6084/m9.figshare.19404020
title: PRISMA_2020_checklist.docx

description: This is PRISMA Check-list of systematic review of retinoid acid
DOI: 10.6084/m9.figshare.19404020.v3
title: PRISMA_2020_checklist.docx

description: This is PRISMA Check-list of systematic review of retinoid acid
DOI: 10.6084/m9.figshare.19404020.v2
title: PRISMA_2020_checklist.docx

description: This is PRISMA Check-list of systematic review of retinoid acid
DOI: 10.34740/kaggle/dsv/3411219
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.6411819
title: Preparatory Slip in Laboratory Faults: Effect of Roughness and Loading Rate

description: Mechanical and acoustic emission data presented in the GRL manuscript: 'Preparatory Slip in Laboratory Faults: Effect of Roughness and Loading Rate', 2022
DOI: 10.5281/zenodo.6411818
title: Preparatory Slip in Laboratory Faults: Effect of Roughness and Loading Rate

description: Mechanical and acoustic emission data presented in the GRL manuscript: 'Preparatory Slip in Laboratory Faults: Effect of Roughness and Loading Rate', 2022
DOI: 10.34740/kaggle/dsv/3411256
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.3239032
title: ICDAR 2019 Competition on Table Detection and Recognition (cTDaR)

description: The aim of this competition is to evaluate the performance of state of the art methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done. The Ground Truth is provided in a similar format as for the ICDAR 2013 competition (see [2]): &lt;?xml version="1.0" encoding="UTF-8"?&gt; &lt;<strong>document</strong> filename='filename.jpg'&gt; &lt;<strong>table</strong> id='Table_1540517170416_3'&gt; <strong> &lt;Coords points="180,160 4354,160 4354,3287 180,3287"/&gt;</strong> &lt;<strong>cell</strong> id='TableCell_1540517477147_58' <strong>start-row</strong>='0' <strong>start-col</strong>='0' <strong>end-row</strong>='1' <strong>end-col</strong>='2'&gt; &lt;<strong>Coords</strong> <strong>points</strong>="180,160 177,456 614,456 615,163"/&gt; &lt;/cell&gt; ... &lt;/table&gt; ... &lt;/document&gt; The difference to Gobel et al. [2] is the Coords tag which defines a table/cell as a polygon specified by a list of coordinates. For B.1 the table and its coordinates is given together with the input image. Important Note: For the modern dataset, the convex hull of the content describes a cell region. For the historical dataset, it is requested that the output region of a cell is the cell boundary. This is necessary due to the characteristics of handwritten text, which is often overlapping with different cells. See also: http://sac.founderit.com/tasks.html The evaluation tool is available at github: https://github.com/cndplab-founder/ctdar_measurement_tool
description: http://sac.founderit.com/
DOI: 10.5281/zenodo.2649216
title: ICDAR 2019 Competition on Table Detection and Recognition (cTDaR)

description: The aim of this competition is to evaluate the performance of state of the art methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done. The Ground Truth is provided in a similar format as for the ICDAR 2013 competition (see [2]): &lt;?xml version="1.0" encoding="UTF-8"?&gt; &lt;<strong>document</strong> filename='filename.jpg'&gt; &lt;<strong>table</strong> id='Table_1540517170416_3'&gt; <strong> &lt;Coords points="180,160 4354,160 4354,3287 180,3287"/&gt;</strong> &lt;<strong>cell</strong> id='TableCell_1540517477147_58' <strong>start-row</strong>='0' <strong>start-col</strong>='0' <strong>end-row</strong>='1' <strong>end-col</strong>='2'&gt; &lt;<strong>Coords</strong> <strong>points</strong>="180,160 177,456 614,456 615,163"/&gt; &lt;/cell&gt; ... &lt;/table&gt; ... &lt;/document&gt; The difference to Gobel et al. [2] is the Coords tag which defines a table/cell as a polygon specified by a list of coordinates. For B.1 the table and its coordinates is given together with the input image. Important Note: For the modern dataset, the convex hull of the content describes a cell region. For the historical dataset, it is requested that the output region of a cell is the cell boundary. This is necessary due to the characteristics of handwritten text, which is often overlapping with different cells. See also: http://sac.founderit.com/tasks.html The evaluation tool is available at github: https://github.com/cndplab-founder/ctdar_measurement_tool
description: http://sac.founderit.com/
DOI: 10.5281/zenodo.1492267
title: CVL Database - An Off-line Database for Writer Retrieval, Writer Identification and Word Spotting

description: The CVL Database is a public database for writer retrieval, writer identification and word spotting. The database consists of 7 different handwritten texts (1 German and 6 Englisch Texts). In total 310 writers participated in the dataset. 27 of which wrote 7 texts and 283 writers had to write 5 texts. For each text a rgb color image (300 dpi) comprising the handwritten text and the printed text sample is available as well as a cropped version (only handwritten). An unique id identifies the writer, whereas the Bounding Boxes for each single word are stored in an XML file. The CVL-database consists of images with cursively handwritten german and english texts which has been choosen from literary works. All pages have a unique writer id and the text number (separated by a dash) at the upper right corner, followed by the printed sample text. The text is placed between two horizontal separatores. Beneath the printed text individuals have been asked to write the text using a ruled undersheet to prevent curled text lines. The layout follows the style of the IAM database. The database was updated on 12/09/2013 since one writer ID (265/266) was wrong. The version number was changed to 1.1. Samples of the following texts have been used: Edwin A. Abbot – Flatland: A Romance of Many Dimension (92 words). William Shakespeare – Mac Beth (49 words). Wikipedia – Mailüfterl (73 words, under CC Attribution-ShareALike License). Charles Darwin – Origin of Species (52 words). Johann Wolfgang von Goethe – Faust. Eine Tragödie (50 words). Oscar Wilde – The Picture of Dorian Gray (66 words). Edgar Allan Poe – The Fall of the House of Usher (78 words). This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to: Florian Kleber, Stefan Fiel, Markus Diem and Robert Sablatnig, <em>CVL-Database: An Off-line Database for Writer Retrieval, Writer Identification and Word Spotting</em>, In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 560-564, 2013.
description: {"references": ["Florian Kleber, Stefan Fiel, Markus Diem and Robert Sablatnig, CVL-Database: An Off-line Database for Writer Retrieval, Writer Identification and Word Spotting, In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 560-564, 2013."]}
DOI: 10.5281/zenodo.1492266
title: CVL Database - An Off-line Database for Writer Retrieval, Writer Identification and Word Spotting

description: The CVL Database is a public database for writer retrieval, writer identification and word spotting. The database consists of 7 different handwritten texts (1 German and 6 Englisch Texts). In total 310 writers participated in the dataset. 27 of which wrote 7 texts and 283 writers had to write 5 texts. For each text a rgb color image (300 dpi) comprising the handwritten text and the printed text sample is available as well as a cropped version (only handwritten). An unique id identifies the writer, whereas the Bounding Boxes for each single word are stored in an XML file. The CVL-database consists of images with cursively handwritten german and english texts which has been choosen from literary works. All pages have a unique writer id and the text number (separated by a dash) at the upper right corner, followed by the printed sample text. The text is placed between two horizontal separatores. Beneath the printed text individuals have been asked to write the text using a ruled undersheet to prevent curled text lines. The layout follows the style of the IAM database. The database was updated on 12/09/2013 since one writer ID (265/266) was wrong. The version number was changed to 1.1. Samples of the following texts have been used: Edwin A. Abbot – Flatland: A Romance of Many Dimension (92 words). William Shakespeare – Mac Beth (49 words). Wikipedia – Mailüfterl (73 words, under CC Attribution-ShareALike License). Charles Darwin – Origin of Species (52 words). Johann Wolfgang von Goethe – Faust. Eine Tragödie (50 words). Oscar Wilde – The Picture of Dorian Gray (66 words). Edgar Allan Poe – The Fall of the House of Usher (78 words). This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to: Florian Kleber, Stefan Fiel, Markus Diem and Robert Sablatnig, <em>CVL-Database: An Off-line Database for Writer Retrieval, Writer Identification and Word Spotting</em>, In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 560-564, 2013.
description: {"references": ["Florian Kleber, Stefan Fiel, Markus Diem and Robert Sablatnig, CVL-Database: An Off-line Database for Writer Retrieval, Writer Identification and Word Spotting, In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 560-564, 2013."]}
DOI: 10.5281/zenodo.1492173
title: ICDAR2013 – Handwritten Digit and Digit String Recognition Competition

description: The CVL Single Digit dataset consists of 7000 single digits (700 digits per class) written by approximately 60 different writers. The validation set has the same size but different writers. The validation set may be used for parameter estimation and validation but not for supervised training. The CVL Digit Strings dataset uses 10 different digit strings from a total of about 120 writers resulting in 1262 training images. The digits from the CVL Single Digit dataset were extracted from these strings. This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to: Markus Diem, Stefan Fiel, Angelika Garz, Manuel Keglevic, Florian Kleber and Robert Sablatnig, <em>ICDAR 2013 Competition on Handwritten Digit Recognition (HDRC 2013)</em>, In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 1454-1459, 2013.
description: File Naming The numbers before the first minus are the respective class labels succeeded by an unique ID. 2-0202-21-04.png is an image that contains a single digit with groundtruth 2 135579-0001-10.png is an image that contains the digit string 135579
description: {"references": ["Markus Diem, Stefan Fiel, Angelika Garz, Manuel Keglevic, Florian Kleber and Robert Sablatnig, ICDAR 2013 Competition on Handwritten Digit Recognition (HDRC 2013), In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 1454-1459, 2013."]}
DOI: 10.5281/zenodo.1492172
title: ICDAR2013 – Handwritten Digit and Digit String Recognition Competition

description: The CVL Single Digit dataset consists of 7000 single digits (700 digits per class) written by approximately 60 different writers. The validation set has the same size but different writers. The validation set may be used for parameter estimation and validation but not for supervised training. The CVL Digit Strings dataset uses 10 different digit strings from a total of about 120 writers resulting in 1262 training images. The digits from the CVL Single Digit dataset were extracted from these strings. This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to: Markus Diem, Stefan Fiel, Angelika Garz, Manuel Keglevic, Florian Kleber and Robert Sablatnig, <em>ICDAR 2013 Competition on Handwritten Digit Recognition (HDRC 2013)</em>, In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 1454-1459, 2013.
description: File Naming The numbers before the first minus are the respective class labels succeeded by an unique ID. 2-0202-21-04.png is an image that contains a single digit with groundtruth 2 135579-0001-10.png is an image that contains the digit string 135579
description: {"references": ["Markus Diem, Stefan Fiel, Angelika Garz, Manuel Keglevic, Florian Kleber and Robert Sablatnig, ICDAR 2013 Competition on Handwritten Digit Recognition (HDRC 2013), In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 1454-1459, 2013."]}
DOI: 10.5281/zenodo.1421600
title: READ ABP WI Dataset - Writer Identification over decades

description: A hand is usually considered as a unique characteristic of a person. However, it may slightly change over their whole lifespan. This change might be due to some physical or mental issues. To the best of our knowledge, there is no dataset available, which covers this aspect of evolvement of handwriting of a single person. When dealing with archival documents, it is important to show that methods are invariant against these changes or investigate how much of these changes are covered. Thus, a new dataset was created with data of the Passau Diocesan Archives (ABP, https://www.bistum-passau.de/bistum/archiv ). The documents originate from death records of different villages or towns in the Diocese of Passau. Usually the writer of these records (mostly the priest) remains the same over several years. In total, the dataset consists of 1766 pages, which originate from 28 different writers. The number of pages per writer varies from 7 up to 311. For some writers, we only have data from 3 different years, whereas the largest time span between two documents of the same writer is 31 years. The dataset is organized as follows: [ID]_[Name]\[YEAR]\[ID]_filename.png The corresponding PAGE XML file is provided along with the dataset and contains the regions of the image where text is included. This file can be used to calculate features of the writer solely on the handwriting and not on the table lines. Currently no research tasks are defined on the dataset; we leave this up to the community. Drop us a note how you are using this dataset.
description: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 674943