cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35111/hmnj-ks39
title: KAFD: Arabic Font Database

description: <h3>Introduction</h3><br>
<p>KAFD: Arabic Font Database was developed by <a href="http://www.kfupm.edu.sa/default.aspx">King Fahd University of Petroleum & Minerals</a> and <a href="http://www.qu.edu.sa/en/Pages/Home.aspx">Qassim University</a>. It is comprised of approximately 2.5 million scanned Arabic printed pages in a variety of fonts, sizes and resolutions along with corresponding transcripts. KAFD was designed for research in Arabic text recognition.</p><br>
<h3>Data</h3><br>
<p>The scanned Arabic texts were collected from publications covering various subjects such as religion, medicine, science and history. Texts were printed in 40 different fonts, 10 sizes and four styles. Scans were made at 100, 200, 300 and 600 dpi (dots per inch).</p><br>
<p>The database is available in two formats: at the page level and at the line level. Images are presented as TIFF images and transcripts are in plain text format. Individual font folders are compressed into RAR archives.</p><br>
<p>The data is divided into training, validation and test sets.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2016T21.tif">image sample</a> and <a href="desc/addenda/LDC2016T21.txt">text sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2016 King Fahd University of Petroleum & Minerals, Trustees of the University of Pennsylvania
DOI: 10.35111/td6z-pv16
title: Chinese-English Parallel Sentences Extracted from Patents

description: <h3>Introduction</h3><br>
<p>Chinese-English Parallel Sentences Extracted from Patents was developed by Chilin (HK) Limited and contains 500,000 sentence pairs of Chinese-English parallel text. This resource is based on the training corpus and test sets developed for the Tokyo-based <a href="http://research.nii.ac.jp/ntcir/index-en.html">NTCIR</a> 2009 & 2010 tasks on <a href="http://ntcir.nii.ac.jp/PatentMTList/">Patent Machine Translation</a>.</p><br>
<h3>Data</h3><br>
<p>The sentences in this release were selected from a larger corpus of than 300,000 Chinese-English parallel patents in different fields according to a number of filtering parameters including word alignment, sentence length and language modeling. They were then automatically segmented and aligned. All text is encoded as UTF-8.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2016T22.cmn.txt">Chinese sample</a> and <a href="desc/addenda/LDC2016T22.eng.txt">English sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p><br>
<h3>Pricing</h3><br>
<p>Not-for-profit organizations may license this data set for US$25.00 under the LDC Not-for-Profit Membership Agreement or under the LDC User Agreement for Non-Members for use in linguistic research, education and non-commercial technology development. For-profit organizations may license this data for US$5000, discounted to US$4000 for LDC for-profit members, under the Commercial License Agreement for Chinese-English Parallel Sentences Extracted from Patents (LDC2016T22).</p><br>
<p>Current fees in this catalog entry reflect those pertaining to a for-profit organization license. Not-for-profit organizations should contact LDC's <a href="mailto:ldc@ldc.upenn.edu">Membership Office</a> to license this data set.</p></br>
Portions © 2016 Chilin (HK) Limited, © 2016 Trustees of the University of Pennsylvania
DOI: 10.35111/4ym6-k679
title: Richer Event Description

description: <h3>Introduction</h3><br>
<p>Richer Event Description was developed by the <a href="http://clear.colorado.edu/start/index.html">University of Colorado Boulder-CLEAR (Computational Language and Education Research</a>, <a href="http://www.cmu.edu/">Carnegie Mellon University</a> and LDC. It consists of coreference, bridging and event-event relations (temporal, causal, subevent and reporting relations) annotations over 95 English newswire, discussion forum and narrative text documents, covering all events, times and non-eventive entities within each document.</p><br>
<p>RED annotation is intended to join different annotation layers and to provide a rich representation of event phenomena.</p><br>
<h3>Data</h3><br>
<p>Documents were annotated twice -- in a markable pass and in an event annotation phase. In the markable pass, events, entities, TIMEX3 and section elements were annotated across the entire document and features were marked on each markable element. Event relations and event coreference were then annotated over the adjudicated markables. Further information about the annotation process is contained in the guidelines accompanying this release.</p><br>
<p>Annotation and source documents are divided into three partitions: (1) 20 newswire summarization documents, (2) 20 discussion forum documents and newswire annotations used in the original RED pilot annotations, and (3) 55 documents annotated by a range of DEFT (Deep Exploration and Filtering of Test) annotation formats. Data is presented as UTF-8 encoded xml and plain text.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2016T23.src.txt">source sample</a> and <a href="desc/addenda/LDC2016T23.anno.xml">annotation sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p><br>
<h3>Acknowledgement</h3><br>
<p>This material is based on research sponsored by Air Force Research Laboratory and Defense Advance Research Projects Agency under agreement number FA8750-13-2-0045. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory and Defense Advanced Research Projects Agency or the U.S. Government.</p></br>
Portions © 2002, 2010 Agence France Presse, © 2013 The New York Times, © 2010 Xinhua News Agency, © 2016 Colorado University - CLEAR, © 2016 Trustees of the University of Pennsylvania
DOI: 10.35111/r55z-6547
title: JANA: A Human-Human Dialogues Corpus for Egyptian Dialect

description: <h3>Introduction</h3><br>
<p>JANA: A Human-Human Dialogues Corpus for Egyptian Dialect was developed by researchers at <a href="https://cu.edu.eg/Home">Cairo University</a>. It consists of 82 transcribed dialogues from call center inquiries annotated for dialogue acts.</p><br>
<p>Data was collected from call centers for banks, airlines and mobile network providers as follows: (1) spontaneous spoken dialogues from inquiries to banks and airlines; and (2) instant messaging (chat) dialogues from a mobile network provider's online support system.</p><br>
<h3>Data</h3><br>
<p>The transcribed dialogues consist of 52 telephone calls and 30 instant messaging conversations, amounting to approximately 20,311 words. The data contains roughly 3,001 conversation turns, with an average of 6.7 words per turn, and 4,725 utterances, with an average of 4.3 words per utterance. The data was transcribed using <a href="http://transag.sourceforge.net/">Transcriber</a>.</p><br>
<p>All data is presented as UTF-8 XML.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2016T24.xml">sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p><br>
<h3>Pricing</h3><br>
<p>Not-for-profit organizations may license this data set for US$25.00 under the LDC Not-for-Profit Membership Agreement or under the LDC User Agreement for Non-Members for use in linguistic research, education and non-commercial technology development. For-profit organizations may license this data for US$1650 under the Commercial License Agreement for JANA: A Human-Human Dialogues Corpus for Egyptian Dialect (LDC2016T24).</p><br>
<p>Current fees in this catalog entry reflect those pertaining to a for-profit organization license. Not-for-profit organizations should contact LDC's Membership Office to license this data set.</p></br>
Portions © 2016 AbdelRahim AbdelSabour AbdelHalim Mohamed Elmadany, © 2016 Trustees of the University of Pennsylvania
DOI: 10.35111/qmag-6546
title: GALE Phase 3 and 4 Chinese Newswire Parallel Text

description: <h3>Introduction</h3><br>
<p>GALE Phase 3 and 4 Chinese Newswire Parallel Text was developed by the Linguistic Data Consortium (LDC). Along with other corpora, the parallel text in this release comprised training data for Phases 3 and 4 of the DARPA GALE (Global Autonomous Language Exploitation) Program. This corpus contains Chinese source text and corresponding English translations selected from newswire data collected by LDC between 2007 and 2008 and translated by LDC or under its direction.</p><br>
<h3>Data</h3><br>
<p>GALE Phase 3 and 4 Chinese Newswire Parallel Text includes 367 source-translation document pairs, comprising 210,048 tokens of Chinese source text and its English translation. Data is drawn from five distinct Chinese newswire sources.</p><br>
<p>Data was manually selected for translation according to several criteria, including linguistic features and topic features. The files were formatted into a human-readable translation format and assigned to translation vendors. Translators followed LDC's Chinese to English translation guidelines. Bilingual LDC staff performed quality control procedures on the completed translations.</p><br>
<p>Source data and translations are distributed in TDF format. TDF files are tab-delimited files containing one segment of text along with meta information about that segment. Each field in the TDF file is described in TDF_format.txt. All data are encoded in UTF-8.</p><br>
<h3>Acknowledgement</h3><br>
<p>This work was supported in part by the Defense Advanced Research Projects Agency, GALE Program Grant No. HR0011-06-1-0003. The content of this publication does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2016T25.txt">source sample</a> and <a href="desc/addenda/LDC2016T25.eng.txt">translation sample.</a></p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2008 Agence France Presse, © 2007-2008 China Military Online, © 2007-2008 Chinanews.com, © 2007-2008 Guangming Daily, © 2007-2008 People's Daily, © 2007, 2008, 2011, 2016 Trustees of the University of Pennsylvania
DOI: 10.35111/nfwt-9367
title: GALE Phase 4 Arabic Newswire Parallel Sentences

description: <h3>Introduction</h3><br>
<p>GALE Phase 4 Arabic Newswire Parallel Sentences was developed by the Linguistic Data Consortium (LDC). Along with other corpora, the parallel text in this release comprised training data for Phase 4 of the DARPA GALE (Global Autonomous Language Exploitation) Program. This corpus contains Modern Standard Arabic source sentences and corresponding English translations selected from newswire data collected by LDC in 2008 and translated by LDC or under its direction.</p><br>
<h3>Data</h3><br>
<p>GALE Phase 4 Arabic Newswire Parallel Sentences includes 393 source-translation document pairs, comprising 62,669 words (Arabic source) of translated data. Data is drawn from six distinct Arabic newswire sources.</p><br>
<p>Sentences were selected for translation in two steps. First, files were chosen using sentence selection scripts provided by GALE program participants <a href="http://www.sri.com/">SRI International</a> and <a href="http://www.ibm.com/us/en/">IBM</a>. The output was then manually reviewed by LDC staff to eliminate problematic sentences. Selected files were reformatted into a human-readable translation format and assigned to translation vendors. Translators followed LDC's Arabic to English translation guidelines and were provided with the full source documents containing the target sentences for their reference. Bilingual LDC staff performed quality control procedures on the completed translations.</p><br>
<p>Source data and translations are distributed in TDF format. TDF files are tab-delimited files containing one segment of text along with meta information about that segment. Each field in the TDF file is described in TDF_format.txt. All data are encoded in UTF-8.</p><br>
<h3>Acknowledgement</h3><br>
<p>This work was supported in part by the Defense Advanced Research Projects Agency, GALE Program Grant No. HR0011-06-1-0003. The content of this publication does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.</p><br>
<h3>Samples</h3><br>
<p>Please view the following samples:</p><br>
<ul><br>
<li><a href="desc/addenda/LDC2016T27.txt">Source Sample</a></li><br>
<li><a href="desc/addenda/LDC2016T27.sel.txt">Selection Sample</a></li><br>
<li><a href="desc/addenda/LDC2016T27.sel.eng.txt">Translated Selection Sample</a></li><br>
</ul><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2008 Agence France Presse, Al-Ahram, Al Hayat, Al-Quds Al-Arabi, An Nahar, Asharq Al-Awsat, © 2008, 2016 Trustees of the University of Pennsylvania
DOI: 10.35111/cn82-n503
title: HAVIC Pilot Transcription

description: <h3>Introduction</h3><br>
<p>HAVIC Pilot Transcription was developed by the Linguistic Data Consortium (LDC) and is comprised of approximately 72 hours of user-generated videos with transcripts based on the English speech audio extracted from the videos. This data set was created in collaboration with <a href="http://www.nist.gov/">NIST</a> (the National Institute of Standards and Technology) as part of the <a href="https://www.ldc.upenn.edu/collaborations/past-projects/havic">HAVIC</a> (the Heterogeneous Audio Visual Internet Collection) project, the goal of which was to advance multimodal event detection and related technologies.</p><br>
<p>LDC has developed a large, heterogeneous, annotated multimodal corpus for HAVIC that has been used in the NIST-sponsored <a href="http://nist.gov/itl/iad/mig/med.cfm">MED</a> (Multimedia Event Detection) task for several years. HAVIC Pilot Transcription supported an experiment to produce a verbatim transcript (quick and rich transcription) based on audio extracted from user-generated videos. It contains the pilot transcripts for selected MED 2011 video files as well as the associated videos.</p><br>
<h3>Data</h3><br>
<p>NIST designated the videos to be transcribed. Annotators generated the transcripts using <a href="https://www.ldc.upenn.edu/language-resources/tools/xtrans">XTrans</a>, which supports manual transcription across multiple channels, languages and platforms. HAVIC transcription guidelines are included in the documentation for this release.</p><br>
<p>Each file was transcribed by a single annotator with no corpus-wide second pass. File samples from each annotator were checked for various errors, including missing transcription, improper mark-up, poor segmentation and missing/added words.</p><br>
<p>All transcription files are in .tdf format, a plain-text, flat-table format with 13 tab-delimited fields. All video files are in .mp4 format (h264), with varying bit-rates and levels of audio fidelity and video resolution.</p><br>
<h3>Samples</h3><br>
<p>Please view these <a href="desc/addenda/LDC2016V01.mp4">video</a> and <a href="desc/addenda/LDC2016V01.txt">transcript</a> samples.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2011-2016 YouTube, LLC, © 2011-2016 Trustees of the University of Pennsylvania
DOI: 10.35111/9abp-k222
title: Arabic Speech Recognition Pronunciation Dictionary

description: <h3>Introduction</h3><br>
<p>Arabic Speech Recognition Pronunciation Dictionary was developed by the <a href="http://qcri.qa/">Qatar Computing Research Institute</a>. It contains approximately two million pronunciation entries for 526,000 Modern Standard Arabic words, for an average of 3.84 pronunciations for each grapheme word.</p><br>
<h3>Data</h3><br>
<p>The dictionary was developed from news archive resources, including the Arabic news website <a href="http://www.aljazeera.net/">Aljazeera.net</a>. The selected words were those that occurred more than once in the news collection. The text was processed using <a href="http://www.ccls.columbia.edu/project/madatokan/">MADA</a>.</p><br>
<p>The dictionary is presented in a single UTF-8 plain text file.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2017L01.txt">sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2017 Qatar Computing Research Institute, © 2017 Trustees of the University of Pennsylvania
DOI: 10.6073/pasta/d85a1a17d13c48abeba076509265cb80
title: Soil Bacteria and Archaea in Macrosystems Biodiversity Project at Harvard Forest 2012

description: Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This field experiment focused on soil microbes. DNA was extracted and purified from soil cores from an array of 21 1m2 subplots. The V4 region of the 16S rRNA genes for bacteria and archaea were amplified and sequenced using Illumina MiSeq by the University of Oklahoma Institute for Environmental Genomics as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
DOI: 10.35111/fjsq-a117
title: 2010 NIST Speaker Recognition Evaluation Test Set

description: <h3>Introduction</h3><br>
<p>2010 NIST Speaker Recognition Evaluation Test Set was developed by the Linguistic Data Consortium (LDC) and NIST (National Institute of Standards and Technology). It contains 2,255 hours of American English telephone speech and speech recorded over a microphone channel involving an interview scenario used as test data in the NIST-sponsored 2010 Speaker Recognition Evaluation (SRE).</p><br>
<p>The ongoing series of SRE yearly evaluations conducted by NIST are intended to be of interest to researchers working on the general problem of text independent speaker recognition. To this end the evaluations are designed to be simple, to focus on core technology issues, to be fully supported and to be accessible to those wishing to participate.</p><br>
<p>The 2010 evaluation was similar to the 2008 evaluation by including in the training and test conditions for the core test not only conversational telephone speech (CTS) recorded over ordinary telephone channels, but also CTS and conversational interview speech recorded over a room microphone channel. Unlike prior evaluations, some of the conversational telephone style speech was collected in a manner to produce particularly high, or particularly low, vocal effort on the part of the speaker of interest.</p><br>
<h3>Data</h3><br>
<p>The speech recordings in this release were collected in 2009 and 2010 by LDC at its <a href="https://www.ldc.upenn.edu/about/facilities/human-subjects-collection">Human Subjects Collection</a> facility in Philadelphia. This collection was part of the Mixer 6 project, which was designed to support the development of robust speaker recognition technology by providing carefully collected and audited speech from a large pool of speakers recorded simultaneously across numerous microphones.</p><br>
<p>The telephone speech segments include two-channel excerpts of approximately 10 seconds and 5 minutes. There are also summed-channel excerpts in the range of 5 minutes. The microphone excerpts are 3-15 minutes in duration. As in prior evaluations, intervals of silence were not removed. The data included in this release is 8 bit ulaw with a sample rate of 8000.</p><br>
<p>In addition to evaluation data, this package also consists of answer keys, trial and train files, development data and evaluation documentation.</p><br>
<h3>Samples</h3><br>
<p>Please listen to this <a href="desc/addenda/LDC2017S06.sph">audio sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2009, 2010, 2017 Trustees of the University of Pennsylvania
DOI: 10.35111/g9fy-kd36
title: CHiME2 Grid

description: <h3>Introduction</h3><br>
<p>CHiME2 Grid was developed as part of <a href="http://spandh.dcs.shef.ac.uk/chime_challenge/chime2013/">The 2nd CHiME Speech Separation and Recognition Challenge</a> and contains approximately 120 hours of English speech from a noisy living room environment. The CHiME Challenges focus on distant-microphone automatic speech recognition (ASR) in real-world environments.</p><br>
<p>CHiME2 Grid reflects the <a href="http://spandh.dcs.shef.ac.uk/chime_challenge/chime2013/chime2_task1.html">small vocabulary track</a> of the CHiME2 Challenge. The target utterances were taken from the <a href="http://spandh.dcs.shef.ac.uk/gridcorpus/">Grid corpus</a> and consist of 34 speakers reading simple 6-word sequences.</p><br>
<p>LDC also released CHiME2 WSJ0 (<a href="../../../LDC2017S10">LDC2017S10</a>) and CHiME3 (<a href="../../../LDC2017S24">LDC2017S24</a>).</p><br>
<h3>Data</h3><br>
<p>Data is divided into training, development and test sets. All data is provided as 16 bit WAV files sampled at 16 kHz. The noisy utterances are provided both in isolated form and in embedded form. The latter either involve five seconds of background noise before and after the utterance (in the training set) or they are mixed in continuous five minute noise background recordings (in the development and test sets). Seven hours of noise background not part of the training set are also included. The data is accompanied by one annotation file per speaker that includes additional technical information.</p><br>
<p>Also included is a baseline Hidden Markov Model (HMM)-based speech recogniser and a scoring tool designed for the 2nd CHiME Challenge to allow users to obtain keyword recognition scores from formatted result files, perform recognition and score the challenge data, and estimate parameters of speaker dependent HMMs.</p><br>
<h3>Samples</h3><br>
<p>Please listen to the following samples:</p><br>
<ul><br>
<li><a href="desc/addenda/LDC2017S07.clean.wav">Clean</a></li><br>
<li><a href="desc/addenda/LDC2017S07.embed.wav">Embedded</a></li><br>
<li><a href="desc/addenda/LDC2017S07.isolate.wav">Isolated</a></li><br>
<li><a href="desc/addenda/LDC2017S07.reverb.wav">Reverberated</a></li><br>
</ul><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2017 Inria Nancy - Grand Est, University of Sheffield, Mitsubishi Electric Research Labs, Fondazione Bruno Kessler, © 2017 Trustees of the University of Pennsylvania