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1,584 results for “introduction”
IPBES Data Management Tutorials - Session 1.4: Introduction to the IPBES data management tutorials
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>Introduction to the IPBES data management policy</em> chapter provides an overview on data management within the IPBES platform, and the series of the tutorials prepared by the task force on knowledge and data that will assist experts with the implementation of the IPBES data management policy.</p> <p>This session, <em>Introduction to the IPBES data management tutorials</em>, summarizes the purpose of the data management tutorials, what they cover, and where to find transcripts and documentation.</p>
IPBES Data Management Tutorials - Session 1.2: Introduction to IPBES tutorials and training
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>Introduction to the IPBES data management policy</em> chapter provides an overview on data management within the IPBES platform, and the series of the tutorials prepared by the task force on knowledge and data that will assist experts with the implementation of the IPBES data management policy.</p> <p>This session, <em>Introduction to the IPBES tutorials and training</em>, provides<strong> </strong>a short introduction detailing the objectives of these tutorials and the responsibilities of the IPBES secretariat regarding data management. </p>
IPBES Data Management Tutorials - Session 2.1: Introduction to the data management policy
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management Policy </em>chapter provides an introduction of the IPBES data management policy. It discusses why IPBES has a data management policy and who is responsible for what in the implementation and further development of this policy. </p> <p>This session,<em> Introduction to the data management policy</em><em>, </em>defines what a data management policy is and why it is important.</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): de novo Genome Assembly
<p>Data and conda software environment file for the chapter '<em>de novo</em> Genome Assembly' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Authentication
<div> <p>Data and conda software environment file for the chapter 'Authentication' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p> </div>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Introduction to Git(Hub)
<p>Data and conda software environment file for the chapter 'Introduction to Git(Hub)' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Introduction to R and the Tidyverse
<p>Data and conda software environment file for the chapter 'Introduction to R and the Tidyverse' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Introduction to the Command Line
<p>Data and conda software environment file for the chapter 'Introduction to the Command Line' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Introduction to Python and Pandas
<p>Data and conda software environment file for the chapter 'Introduction to Python and Pandas' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Phylogenomics
<p>Data and conda software environment file for the chapter 'Phylogenomics' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Genome Mapping
<p>Data and conda software environment file for the chapter 'Genome Mapping' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Taxonomic Profiling, OTU Tables, and Visualisation
<p>Data and conda software environment file for the chapter 'Taxonomic Profiling, OTU Tables, and Visualisation' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Ancient Metagenomic Pipelines
<p>Data and conda software environment file for the chapter 'Ancient Metagenomic Pipelines' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Contamination
<p>Data and conda software environment file for the chapter 'Contamination' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Introduction to bulk RNAseq analysis: supplementary material
<p><strong>Vampirium setup</strong></p><p>This archive contains materials (datasets, exercises and slides, etc) used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course repo can be found on <a href="https://github.com/hds-sandbox/bulk_RNAseq_course">Github</a>:</p><p>Assignments.zip contains exercises for the preprocessing part of the course, like fastqc and multiqc examples of bulk RNAseq experiments</p><p>Data.zip contains count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata (samplesheet.csv) and backup results from the preprocessing pipeline.</p><p>Notes.zip contains supplementary materials such as extra pdfs for more information on bulk RNAseq technology.</p><p>Slides.zip contains all the slides used in the workshop.</p><p>raw_reads.zip contains the raw reads from the bulk RNAseq experiment (<a href="https://doi.org/10.1016/j.celrep.2014.10.054">10.1016/j.celrep.2014.10.054</a>) used in this course.</p>
Videos, audio transcriptions and closed captions for the workshop: Introduction to Wikidata for Maastricht University, Theory and Pratice - 15 October 2024
<h1><strong>Videos, audio transcriptions and closed captions for the workshop: Introduction to Wikidata for Maastricht University, Theory and Pratice - 15 October 2024</strong></h1> <h3><a href="https://theplant.maastrichtuniversity.nl/event/navigating-the-world-of-wikidata-for-research-science-and-cultural-heritage-2"><em>Navigating the World of Wikidata for Research, Science and Cultural Heritage</em></a></h3> <h3><em><a href="https://www.wikidata.org/wiki/Wikidata:Twelfth_Birthday/Workshop_in_Maastricht" target="_blank" rel="noopener">Wikidata's Twelfth Birthday: Workshop in Maastricht </a></em></h3> <p>Wikidata is a free, collaborative, multilingual database, collecting structured open data for anyone in the world to use. It also plays a crucial role in supporting Wikimedia projects, such as Wikipedia and Wikimedia Commons. Over the last 12 years it has strongly increased in popularity among the scientific and cultural heritage communities.</p> <p>In this 2,5 hours workshop you will learn the basics of working with Wikidata, both in theory and practice. You will learn</p> <ol> <li>The basics of Wikidata: A first look at what Wikidata is and how it works, both technically and socially (Wikidata community)</li> <li>How Wikidata can be relevant for research, science and cultural heritage (GLAM), and</li> <li>First steps in contributing to Wikidata yourself, with a focus on the topic of UM professors from past and present.</li> </ol> <p>As part of the <a title="Wikidata:Twelfth Birthday" href="https://www.wikidata.org/wiki/Wikidata:Twelfth_Birthday">Wikidata 12th Birthday celebrations</a> this workshop is open to academics, researchers, students, and professionals interested in working with Wikidata in the intersection of open data, research, and science. Whether you are new to Wikidata or looking to deepen your understanding, this session will provide valuable insights for improving your work.</p> <h2><strong>Workshop outline</strong></h2> <h3><strong>Part 1: Theory, Wikidata basics (45-60 minutes) </strong></h3> <ul> <li><strong><a href="https://zenodo.org/records/13984149/files/Wikidata%20Workshop%20-%20Theoretical%20part%20-%20Maastricht%20University%20-%2015%20October%202024.webm" target="_blank" rel="noopener">Video</a> (.webm) including <a href="https://zenodo.org/records/13984149/files/WikidataWorkshop_MaastrichtUniversity_15October2024_TheoreticalPart.txt?download=1" target="_blank" rel="noopener">audio transcription </a>(.txt) and <a href="https://zenodo.org/records/13984149/files/Wikidata%20Workshop%20-%20Theoretical%20part%20-%20Maastricht%20University%20-%2015%20October%202024.webm.en.srt?download=1" target="_blank" rel="noopener">closed captions</a> (.srt) are available below</strong></li> </ul> <p><strong>Additional materials</strong></p> <ul> <li><strong>Slides in <a href="https://zenodo.org/records/13837957/files/WikidataWorkshop_MaastrichtUniversity_15October2024_TheoreticalPart.pptx?download=1" rel="nofollow">PowerPoint</a> or <a href="https://zenodo.org/records/13837957/files/Wikidata%20Workshop%20-%20Theoretical%20part%20-%20Maastricht%20University%20-%2015%20October%202024.pdf?download=1" rel="nofollow">PDF</a> are available from <a href="https://zenodo.org/records/13837957" target="_blank" rel="noopener">https://zenodo.org/records/13837957</a></strong></li> </ul> <p><em>1) Wikidata basics</em></p> <ul> <li>What is Wikidata?</li> <li>What are the principles of Wikidata?</li> <li>How are things described in Wikidata?</li> <li>Who builds Wikidata? - The Wikidata community</li> </ul> <p><em>2) Wikidata for research, science and cultural heritage</em></p> <ul> <li>To what extent is Wikidata used throughout science, research and GLAM?</li> <li>Six anecd<em>a</em>tic cases <ol> <li>Scientometrics - Scholia</li> <li>Life and biomedical sciences</li> <li>Astronomy</li> <li>Language technology / AI / LLMs</li> <li>GLAM – KB collection highlights</li> <li>Representation of (female) scientists</li> </ol> </li> </ul> <h3><strong>Break (15 minutes)</strong></h3> <h3><strong>Part 2: Practice, contributing to Wikidata (75-90 minutes) </strong></h3> <ul> <li><strong><a href="https://zenodo.org/records/13984149/files/Wikidata%20Workshop%20-%20Practical%20part,%20UM%20professors%20-%20Maastricht%20University%20-%2015%20October%202024.webm?download=1" target="_blank" rel="noopener">Video</a> (.webm) including <a href="https://zenodo.org/records/13984149/files/WikidataWorkshop_MaastrichtUniversity_15October2024_PracticalPart_UMprofessors.txt?download=1" target="_blank" rel="noopener">audio transcription </a>(.txt) and <a href="https://zenodo.org/records/13984149/files/Wikidata%20Workshop%20-%20Practical%20part,%20UM%20professors%20-%20Maastricht%20University%20-%2015%20October%202024.webm.en.srt?download=1" target="_blank" rel="noopener">closed captions</a> (.srt) are available below</strong></li> </ul> <p><strong>Additional materials</strong></p> <ul> <li><strong>Slides in <a href="https://zenodo.org/records/13837957/files/WikidataWorkshop_MaastrichtUniversity_15October2024_PracticalPart_UMprofessors.pptx?download=1" rel="nofollow">PowerPoint</a> or <a href="https://zenodo.org/records/13837957/files/Wikidata%20Workshop%20-%20Practical%20part,%20UM%20professors%20-%20Maastricht%20University%20-%2015%20October%202024.pdf?download=1" rel="nofollow">PDF</a> are available from <a href="https://zenodo.org/records/13837957" target="_blank" rel="noopener">https://zenodo.org/records/13837957</a></strong></li> <li><strong>Handout for participants in <a href="https://zenodo.org/records/13837957/files/WikidataWorkshop_MaastrichtUniversity_15October2024_PracticalPart_HandoutForParticipants.docx?download=1" rel="nofollow">Word</a> or <a href="https://zenodo.org/records/13837957/files/WikidataWorkshop_MaastrichtUniversity_15October2024_PracticalPart_HandoutForParticipants.pdf?download=1" rel="nofollow">PDF</a></strong> <strong>are available from <a href="https://zenodo.org/records/13837957" target="_blank" rel="noopener">https://zenodo.org/records/13837957</a></strong></li> </ul> <p><strong> </strong>The goals of this hands-on part are:</p> <ul> <li>Get familiar with basic data editing via the Wikidata interface</li> <li>Understand WD data models and structures related to professors (of Maastricht University)</li> <li>Extend existing <a title="Wikidata:Wiki-wetenschappers/Universiteit Maastricht/hoogleraren" href="https://www.wikidata.org/wiki/Wikidata:Wiki-wetenschappers/Universiteit_Maastricht/hoogleraren">Wikidata items about UM professors</a>, based on information in public sources.</li> <li>If time allows: Create new Wikidata items about UM professors</li> </ul> <p>The visual slides and the textual handout explain the same content, blocks and exercises, albeit in a slightly different order.<strong> </strong></p> <h2><strong>Required preparation</strong></h2> <p>To make optimal use of our time, participants must create a Wikidata account in the weeks before the workshop. See <a href="https://www.wikidata.org/w/index.php?title=Special:CreateAccount" target="_blank" rel="noopener">https://www.wikidata.org/w/index.php?title=Special:CreateAccount</a>.</p> <p>This is important because very fresh accounts may have limited editing rights. Furthermore only 6 Wikidata accounts can be created per day from UM IP addresses, so creating a lot of new accounts during the workshop might overstretch this limit.</p> <h2><strong>Workshop leader</strong></h2> <p>This workshop was given by <a href="https://www.kb.nl/over-ons/experts/olaf-janssen">Olaf Janssen</a>, the Wikimedia coordinator of the <a href="https://www.kb.nl/over-ons/experts/olaf-janssen">Koninklijke Bibliotheek</a>, the national library of the Netherlands.</p> <p>In this role he stimulates and facilitates collaboration between the collections, knowledge, open data and staff of the KB on the one hand, and the projects of the Wikimedia movement, such as Wikipedia, Wikimedia Commons and Wikidata on the other. He is also active as a volunteer within the community. Feel free to contact Olaf via olaf.janssen(at)<a href="http://kb.nl">kb.nl</a></p> <h2><strong>Materials on Wikimedia Commons</strong></h2> <p>Photos , videos and presentations related to this event can be found on Wikimedia Commons: <a title="c:Category:Wikidata Workshop at Maastricht University, 15 October 2024" href="https://commons.wikimedia.org/wiki/Category:Wikidata_Workshop_at_Maastricht_University,_15_October_2024">Category:Wikidata Workshop at Maastricht University, 15 October 2024</a></p> <h2>Relevant URLs </h2> <ul> <li><a href="https://www.wikidata.org/wiki/Wikidata:Twelfth_Birthday/Workshop_in_Maastricht">https://www.wikidata.org/wiki/Wikidata:Twelfth_Birthday/Workshop_in_Maastricht </a></li> <li><a href="https://theplant.maastrichtuniversity.nl/event/navigating-the-world-of-wikidata-for-research-science-and-cultural-heritage-2" target="_blank" rel="noopener">https://theplant.maastrichtuniversity.nl/event/navigating-the-world-of-wikidata-for-research-science-and-cultural-heritage-2</a> + <a href="https://web.archive.org/web/20240926154021/https://theplant.maastrichtuniversity.nl/event/navigating-the-world-of-wikidata-for-research-science-and-cultural-heritage-2/">archived version</a></li> <li><a href="https://www.linkedin.com/feed/update/urn:li:activity:7244614063102529536/">https://www.linkedin.com/feed/update/urn:li:activity:7244614063102529536/</a></li> <li><a href="https://www.linkedin.com/feed/update/urn:li:activity:7245329234385059843/" target="_blank" rel="noopener">https://www.linkedin.com/feed/update/urn:li:activity:7245329234385059843/</a></li> </ul> <p>Earlier LinkedIn posts (April-May 2024, before rescheduling the worlshop to October)</p> <ul> <li><a href="https://www.linkedin.com/feed/update/urn:li:activity:7188470390858428416/" target="_blank" rel="noopener">https://www.linkedin.com/feed/update/urn:li:activity:7188470390858428416/</a></li> <li><a href="https://www.linkedin.com/feed/update/urn:li:activity:7188180802021543936/" target="_blank" rel="noopener">https://www.linkedin.com/feed/update/urn:li:activity:7188180802021543936/</a></li> <li><a href="https://www.linkedin.com/feed/update/urn:li:activity:7189276985267826691/" target="_blank" rel="noopener">https://www.linkedin.com/feed/update/urn:li:activity:7189276985267826691/</a></li> </ul> <h3> </h3>
Invasive pneumococcal diseases in children and adults before and after introduction of the 10-valent pneumococcal conjugate vaccine into the Austrian national immunization program
<p>The dataset contains case-based data on invasive pneumococcal disease in Austria, 2009/01 to 2017/02, by year and month of diagnosis, serotype and clinical presentation. Cases are anonymised by using a random ID.</p>
Course Materials for Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730)
In today's world, understanding environmental data and making informed decisions based on it is crucial for addressing complex environmental challenges. Yale School of the Environment's Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730) course serves as an introduction to the integration of environmental data using R programming language, coupled with machine learning techniques. This dataset contains a zip file with all the data files used in this course, along with a README that has the metadata for those files.
INTRODUCTION OF COVID-NEWS-US-NNK AND COVID-NEWS-BD-NNK DATASET
<p>Introduction</p> <p>There are several works based on Natural Language Processing on newspaper reports. Mining opinions from headlines [ 1 ] using Standford NLP and SVM by Rameshbhaiet. Al.compared several algorithms on a small and large dataset. Rubinet. al., in their paper [ 2 ], created a mechanism to differentiate fake news from real ones by building a set of characteristics of news according to their types. The purpose was to contribute to the low resource data available for training machine learning algorithms. Doumitet. al.in [ 3 ] have implemented LDA, a topic modeling approach to study bias present in online news media.</p> <p>However, there are not many NLP research invested in studying COVID-19. Most applications include classification of chest X-rays and CT-scans to detect presence of pneumonia in lungs [ 4 ], a consequence of the virus. Other research areas include studying the genome sequence of the virus[ 5 ][ 6 ][ 7 ] and replicating its structure to fight and find a vaccine. This research is crucial in battling the pandemic. The few NLP based research publications are sentiment classification of online tweets by Samuel et el [ 8 ] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodaret. al.[ 9 ]. NKK dataset is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus’s awareness to the best of our knowledge.</p> <p> </p> <p>2 Data-set Introduction</p> <p>2.1 Data Collection</p> <p>We accumulated 1000 online newspaper report from United States of America (USA) on COVID-19. The newspaper includes The Washington Post (USA) and StarTribune (USA). We have named it as “Covid-News-USA-NNK”. We also accumulated 50 online newspaper report from Bangladesh on the issue and named it “Covid-News-BD-NNK”. The newspaper includes The Daily Star (BD) and Prothom Alo (BD). All these newspapers are from the top provider and top read in the respective countries. The collection was done manually by 10 human data-collectors of age group 23- with university degrees. This approach was suitable compared to automation to ensure the news were highly relevant to the subject. The newspaper online sites had dynamic content with advertisements in no particular order. Therefore there were high chances of online scrappers to collect inaccurate news reports. One of the challenges while collecting the data is the requirement of subscription. Each newspaper required $1 per subscriptions. Some criteria in collecting the news reports provided as guideline to the human data-collectors were as follows:</p> <ul> <li>The headline must have one or more words directly or indirectly related to COVID-19.</li> <li>The content of each news must have 5 or more keywords directly or indirectly related to COVID-19.</li> <li>The genre of the news can be anything as long as it is relevant to the topic. Political, social, economical genres are to be more prioritized.</li> <li>Avoid taking duplicate reports.</li> <li>Maintain a time frame for the above mentioned newspapers.</li> </ul> <p>To collect these data we used a google form for USA and BD. We have two human editor to go through each entry to check any spam or troll entry.</p> <p>2.2 Data Pre-processing and Statistics</p> <p>Some pre-processing steps performed on the newspaper report dataset are as follows:</p> <ul> <li>Remove hyperlinks.</li> <li>Remove non-English alphanumeric characters.</li> <li>Remove stop words.</li> <li>Lemmatize text.</li> </ul> <p>While more pre-processing could have been applied, we tried to keep the data as much unchanged as possible since changing sentence structures could result us in valuable information loss. While this was done with help of a script, we also assigned same human collectors to cross check for any presence of the above mentioned criteria.</p> <p>The primary data statistics of the two dataset are shown in Table 1 and 2.</p> <pre><code>Table 1: Covid-News-USA-NNK data statistics </code></pre> <pre><code>No of words per headline </code></pre> <pre><code>7 to 20 </code></pre> <pre><code>No of words per body content </code></pre> <pre><code>150 to 2100 </code></pre> <pre><code>Table 2: Covid-News-BD-NNK data statistics No of words per headline </code></pre> <pre><code>10 to 20 </code></pre> <pre><code>No of words per body content </code></pre> <pre><code>100 to 1500 </code></pre> <p>2.3 Dataset Repository</p> <p>We used GitHub as our primary data repository in account name NKK^1. Here, we created two repositories USA-NKK^2 and BD-NNK^3. The dataset is available in both CSV and JSON format. We are regularly updating the CSV files and regenerating JSON using a py script. We provided a python script file for essential operation. We welcome all outside collaboration to enrich the dataset.</p> <p> </p> <p> </p> <p>3 Literature Review</p> <p>Natural Language Processing (NLP) deals with text (also known as categorical) data in computer science, utilizing numerous diverse methods like one-hot encoding, word embedding, etc., that transform text to machine language, which can be fed to multiple machine learning and deep learning algorithms.</p> <p>Some well-known applications of NLP includes fraud detection on online media sites[ 10 ], using authorship attribution in fallback authentication systems[ 11 ], intelligent conversational agents or chatbots[ 12 ] and machine translations used by Google Translate[ 13 ]. While these are all downstream tasks, several exciting developments have been made in the algorithm solely for Natural Language Processing tasks. The two most trending ones are BERT[ 14 ], which uses bidirectional encoder-decoder architecture to create the transformer model, that can do near-perfect classification tasks and next-word predictions for next generations, and GPT-3 models released by OpenAI[ 15 ] that can generate texts almost human-like. However, these are all pre-trained models since they carry huge computation cost. Information Extraction is a generalized concept of retrieving information from a dataset. Information extraction from an image could be retrieving vital feature spaces or targeted portions of an image; information extraction from speech could be retrieving information about names, places, etc[ 16 ]. Information extraction in texts could be identifying named entities and locations or essential data. Topic modeling is a sub-task of NLP and also a process of information extraction. It clusters words and phrases of the same context together into groups. Topic modeling is an unsupervised learning method that gives us a brief idea about a set of text. One commonly used topic modeling is Latent Dirichlet Allocation or LDA[17].</p> <p>Keyword extraction is a process of information extraction and sub-task of NLP to extract essential words and phrases from a text. TextRank [ 18 ] is an efficient keyword extraction technique that uses graphs to calculate the weight of each word and pick the words with more weight to it.</p> <p>Word clouds are a great visualization technique to understand the overall ’talk of the topic’. The clustered words give us a quick understanding of the content.</p> <p> </p> <p> </p> <p>4 Our experiments and Result analysis</p> <p>We used the wordcloud library^4 to create the word clouds. Figure 1 and 3 presents the word cloud of Covid-News-USA- NNK dataset by month from February to May. From the figures 1,2,3, we can point few information:</p> <ul> <li>In February, both the news paper have talked about China and source of the outbreak.</li> <li>StarTribune emphasized on Minnesota as the most concerned state. In April, it seemed to have been concerned more.</li> <li>Both the newspaper talked about the virus impacting the economy, i.e, bank, elections, administrations, markets.</li> <li>Washington Post discussed global issues more than StarTribune.</li> <li>StarTribune in February mentioned the first precautionary measurement: wearing masks, and the uncontrollable spread of the virus throughout the nation.</li> <li>While both the newspaper mentioned the outbreak in China in February, the weight of the spread in the United States are more highlighted through out March till May, displaying the critical impact caused by the virus.</li> </ul> <p>We used a script to extract all numbers related to certain keywords like ’Deaths’, ’Infected’, ’Died’ , ’Infections’, ’Quarantined’, Lock-down’, ’Diagnosed’ etc from the news reports and created a number of cases for both the newspaper. Figure 4 shows the statistics of this series. From this extraction technique, we can observe that April was the peak month for the covid cases as it gradually rose from February. Both the newspaper clearly shows us that the rise in covid cases from February to March was slower than the rise from March to April. This is an important indicator of possible recklessness in preparations to battle the virus. However, the steep fall from April to May also shows the positive response against the attack. We used Vader Sentiment Analysis to extract sentiment of the headlines and the body. On average, the sentiments were from -0.5 to -0.9. Vader Sentiment scale ranges from -1(highly negative to 1(highly positive). There were some cases</p> <p>where the sentiment scores of the headline and body contradicted each other,i.e., the sentiment of the headline was negative but the sentiment of the body was slightly positive. Overall, sentiment analysis can assist us sort the most concerning (most negative) news from the positive ones, from which we can learn more about the indicators related to COVID-19 and the serious impact caused by it. Moreover, sentiment analysis can also provide us information about how a state or country is reacting to the pandemic. We used PageRank algorithm to extract keywords from headlines as well as the body content. PageRank efficiently highlights important relevant keywords in the text. Some frequently occurring important keywords extracted from both the datasets are: ’China’, Government’, ’Masks’, ’Economy’, ’Crisis’, ’Theft’ , ’Stock market’ , ’Jobs’ , ’Election’, ’Missteps’, ’Health’, ’Response’. Keywords extraction acts as a filter allowing quick searches for indicators in case of locating situations of the economy, how states are defending against the pandemic, the condition of the health care system etc.</p> <p> </p> <p>5 Conclusion</p> <p>This dataset can demonstrate how news reports could speculate the situation differently based on the news source. The different types of experiments are possible to assert the importance of Natural Language Processing in newspaper report analysis. We are looking for more collaborators in GitHub to enrich the dataset, which will make it possible to run extensive deep learning experiments.</p> <p> </p> <p>References</p> <p>[1] Chaudhary Jashubhai Rameshbhai and Joy Paulose. Opinion mining on newspaper headlines using svm and nlp. International Journal of Electrical & Computer Engineering (2088-8708), 9(3), 2019. [2]Victoria L Rubin, Yimin Chen, and Nadia K Conroy. Deception detection for news: three types of fakes. Proceedings of the Association for Information Science and Technology, 52(1):1–4, 2015. [3]Sarjoun Doumit and Ali Minai. Online news media bias analysis using an lda-nlp approach. InInternational Conference on Complex Systems, 2011. [4]Md Manjurul Ahsan, Kishor Datta Gupta, Mohammad Maminur Islam, Sajib Sen, Md Rahman, Moham- mad Shakhawat Hossain, et al. Study of different deep learning approach with explainable ai for screening patients with covid-19 symptoms: Using ct scan and chest x-ray image dataset.arXiv preprint arXiv:2007.12525, 2020. [5]Gurjit S Randhawa, Maximillian PM Soltysiak, Hadi El Roz, Camila PE de Souza, Kathleen A Hill, and Lila Kari. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: Covid-19 case study.Plos one, 15(4):e0232391, 2020.</p> <p>[6]Ahmad Alimadadi, Sachin Aryal, Ishan Manandhar, Patricia B Munroe, Bina Joe, and Xi Cheng. Artificial intelligence and machine learning to fight covid-19, 2020. [7]Shreshth Tuli, Shikhar Tuli, Rakesh Tuli, and Sukhpal Singh Gill. Predicting the growth and trend of covid- pandemic using machine learning and cloud computing.Internet of Things, page 100222, 2020. [8]Jim Samuel, GG Ali, Md Rahman, Ek Esawi, Yana Samuel, et al. Covid-19 public sentiment insights and machine learning for tweets classification.Information, 11(6):314, 2020. [9] Hamed Jelodar, Yongli Wang, Rita Orji, and Hucheng Huang. Deep sentiment classification and topic discovery on novel coronavirus or covid-19 online discussions: Nlp using lstm recurrent neural network approach.arXiv preprint arXiv:2004.11695, 2020.</p> <p>[10]Nafiz Sadman, Kishor Datta Gupta, Ariful Haque, Subash Poudyal, and Sajib Sen. Detect review manipulation by leveraging reviewer historical stylometrics in amazon, yelp, facebook and google reviews. InProceedings of the 2020 The 6th International Conference on E-Business and Applications, pages 42–47, 2020.</p> <p>[11]Nafiz Sadman, Kishor Datta Gupta, Ariful Haque, Subash Poudyal, and Sajib Sen. Stylometry as a reliable method for fallback authentication. InProceedings of the 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2020.</p> <p>[12]Ethan Fast, Binbin Chen, Julia Mendelsohn, Jonathan Bassen, and Michael S Bernstein. Iris: A conversational agent for complex tasks. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pages 1–12, 2018.</p> <p>[13] Philipp Koehn.Statistical machine translation. Cambridge University Press, 2009.</p> <p>[14]Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding.arXiv preprint arXiv:1810.04805, 2018.</p> <p>[15]Will Douglas Heavenarchive page. Openai’s new language generator gpt-3 is shockingly good—and completely mindless <a href="https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-">https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-</a> gpt-3-nlp/. Technical report.</p> <p>[16]Chin-Hui Lee and Sabato Marco Siniscalchi. An information-extraction approach to speech processing: Analysis, detection, verification, and recognition.Proceedings of the IEEE, 101(5):1089–1115, 2013.</p> <p>[17]Hamed Jelodar, Yongli Wang, Chi Yuan, Xia Feng, Xiahui Jiang, Yanchao Li, and Liang Zhao. Latent dirich- let allocation (lda) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78(11):15169–15211, 2019.</p> <p>[18]Monica Bianchini, Marco Gori, and Franco Scarselli. Inside pagerank.ACM Transactions on Internet Technology (TOIT), 5(1):92–128, 2005.</p>
Dataset of "Introduction to neuromorphic functions of memristors: The inductive nature of synapse potentiation"
<p>This dataset supports the article "Introduction to Neuromorphic Functions of Memristors: The Inductive Nature of Synapse Potentiation," published in the Journal of Applied Physics.</p> <p>Raw data for the article "Introduction to neuromorphic functions of memristors: The inductive nature of synapse potentiation". For further details see the Readme.txt file.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.