Computational journalism projects
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Presentation to Duke University computer science students, February 2012, by Sarah Cohen, Knight Professor of the Practice
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Introduction to Scholarly Communication and the CSCDC
Introduction to Scholarly Communication and the CSCDCCenter for Scholarly Communication & Digital Curation
A presentation by Claire Stewart introducing scholarly communication and the Center for Scholarly Communication & Digital Curation (CSCDC). Presented to the Library Board of Governors, September 2011.Challenges in Enabling Mixed Media Scholarly Research with Multi-Media Data i...
Presentation at the Digital Humanities 2018 Conference, Mexico City, on the development of the Media Suite, an online research environment that facilitates scholarly research using large multimedia collections maintained at archives, libraries and knowledge institutions. The Media Suite unlocks the data on the collection level, item level, and segment level, provides tools that are aligned with the scholarly primitives (discovery, annotation, comparison, linking), and has a 'workspace' for storing personal mixed media collections and annotations, and to do advanced analysis using Jupyter Notebooks and NLP tools.
See the notes for the narrative that goes with the slides.
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Di d dlf_handout
This document discusses four key lessons learned from the Digging Into Data Challenge, which funded computational research projects in the humanities and social sciences.
Lesson 1 is that these projects require open sharing of resources like hardware, software, data, and communication tools. Information professionals can facilitate partnerships and resource sharing agreements. Lesson 2 is that these projects rely on diverse expertise in domains, analytics, data management, and project management that information professionals can provide. Lesson 3 is that computational tools need to be adapted to evolving research questions through close and distant readings. Lesson 4 is that humanities and social science research now deals with "big data" and produces large datasets that information professionals can help curate and preserve long-
Media Suite: Unlocking Archives for Mixed Media Scholarly Research
This document summarizes the CLARIAH Media Suite, which aims to unlock institutional multimedia collections for mixed media scholarly research. It provides access to over 1.8 million radio/TV items, 60 million newspaper pages, and other collections. The Media Suite connects these collections through a shared virtual workspace that allows scholars to discover, access, annotate, analyze, and export data. It supports scholarly primitives like distant and close reading. However, the document notes some issues like complex interfaces, lack of robust analysis pipelines, and need for manual metadata work. Overall, the Media Suite brings analytical tools to archived data and enables new forms of mixed media research, though further development is still needed.
One Discovery Layer, Eight Front Doors: Implementing Blacklight @ IU
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Recommended
Introduction to Scholarly Communication and the CSCDC
Introduction to Scholarly Communication and the CSCDCCenter for Scholarly Communication & Digital Curation
A presentation by Claire Stewart introducing scholarly communication and the Center for Scholarly Communication & Digital Curation (CSCDC). Presented to the Library Board of Governors, September 2011.Challenges in Enabling Mixed Media Scholarly Research with Multi-Media Data i...
Presentation at the Digital Humanities 2018 Conference, Mexico City, on the development of the Media Suite, an online research environment that facilitates scholarly research using large multimedia collections maintained at archives, libraries and knowledge institutions. The Media Suite unlocks the data on the collection level, item level, and segment level, provides tools that are aligned with the scholarly primitives (discovery, annotation, comparison, linking), and has a 'workspace' for storing personal mixed media collections and annotations, and to do advanced analysis using Jupyter Notebooks and NLP tools.
See the notes for the narrative that goes with the slides.
The role of virtual research environments (VRE's) within the context of an e-...
e-Research framework; Virtual research environments; VRE framework; International and South African examples of VRE;s; Going to the practice
Audiovisual collections, the spoken word and user needs of scholars in the Hu...
Audiovisual collections, the spoken word and user needs of scholars in the Humanities: Observations based on related work in The Netherlands 2005-2012. Presentation at the BL, Opening up Speech Archives, 08-02-2013
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The document discusses the Electronic Records Archives (ERA) program at the US National Archives and Records Administration (NARA). It outlines NARA's challenges in preserving and providing access to massive amounts of electronic records from the US government. The ERA program conducts research to develop next-generation technologies and methods for issues like managing heterogeneous data at large scales, integrating information from different sources, and ensuring records can be accessed over time as technologies change. Examples of ERA's research partnerships with organizations like NIST, the Army Research Laboratory, and the National Science Foundation are provided.
Di d dlf_handout
This document discusses four key lessons learned from the Digging Into Data Challenge, which funded computational research projects in the humanities and social sciences.
Lesson 1 is that these projects require open sharing of resources like hardware, software, data, and communication tools. Information professionals can facilitate partnerships and resource sharing agreements. Lesson 2 is that these projects rely on diverse expertise in domains, analytics, data management, and project management that information professionals can provide. Lesson 3 is that computational tools need to be adapted to evolving research questions through close and distant readings. Lesson 4 is that humanities and social science research now deals with "big data" and produces large datasets that information professionals can help curate and preserve long-
Media Suite: Unlocking Archives for Mixed Media Scholarly Research
This document summarizes the CLARIAH Media Suite, which aims to unlock institutional multimedia collections for mixed media scholarly research. It provides access to over 1.8 million radio/TV items, 60 million newspaper pages, and other collections. The Media Suite connects these collections through a shared virtual workspace that allows scholars to discover, access, annotate, analyze, and export data. It supports scholarly primitives like distant and close reading. However, the document notes some issues like complex interfaces, lack of robust analysis pipelines, and need for manual metadata work. Overall, the Media Suite brings analytical tools to archived data and enables new forms of mixed media research, though further development is still needed.
One Discovery Layer, Eight Front Doors: Implementing Blacklight @ IU
The document summarizes Indiana University's implementation of the Blacklight discovery layer across its eight campuses to provide a shared interface for its online catalog (IUCAT) while allowing for flexibility across campuses. Key points include: IU has a complex data environment with diverse collections across eight campuses previously only served by a one-size-fits-all interface; in 2011 IU selected Blacklight over VuFind as its discovery layer due to flexibility and development community; implementation began in summer 2011 with a public beta in fall 2012 and full transition in May 2013; campus-specific views and call number browsing were customized; and future work includes enhanced customization, transition to Kuali OLE, and improving browse functions.
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This document provides an introduction to data science, including defining data science, discussing the different types of data (structured, unstructured, natural language, machine-generated, graph-based, audio/video/images, and streaming) and tools used (Python, R, SQL, Hadoop, Spark). It also discusses benefits and uses of data science across industries and gives examples to illustrate each type of data.
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This document discusses incentivizing the uptake of reusable metadata in survey production. It notes that there is no universal language used to document survey questions and variables, leading to wasted resources. The Data Documentation Initiative (DDI) is proposed as a standard. Barriers to adopting metadata best practices include legacy systems, manual processes, and reluctance to change. The document outlines ideas to incentivize metadata use such as specifying documentation requirements in funding calls and improving documentation tools and workflows. Showing tangible benefits through applications like question banks and data exploration systems is also suggested.
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The document provides an overview of research data management and the importance of avoiding a "DATApocalypse" or data disaster. It discusses the definition of research data, why data management is important, questions to consider, best practices for data management planning, documentation, and long-term preservation. The goal is to help researchers and institutions properly manage data to enable sharing and preservation, as required by most major funders.
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- Details on how to budget for and include a data management plan in proposals
- A checklist for developing a data management plan covering topics like the types and volumes of data, data sharing and reuse, and making data FAIR
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The document introduces the topic of data science and the programming language R. It defines data science and provides examples of using data science for social good. Additionally, it outlines some of the main features and pros and cons of using R for data science applications and statistical analysis.
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This document provides an introduction to data visualization for analysis. It discusses exploring datasets that can include textual, numerical, and other data. The document outlines the data visualization process and mentions some common tools and methods used. It also discusses extending your toolset and provides an example exercise exploring a dataset and creating a visualization to gain insights. The objective is to appreciate the variety of techniques available to digital humanities scholars for data analysis and visualization.
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This document discusses change management for libraries in the digital age. It notes that digital technologies are blurring traditional lines between types of resources, institutions, and access to information. Users now expect online access and searching across all information formats and locations. The management of digital information requires investment in people, technology, and resources. Libraries must develop new skills and roles to integrate physical and digital collections and provide one-stop searching. Repositories are important for managing and preserving the growing amount of digital research output and data. Metadata standards help link resources across repositories at multiple levels from institutional to international.
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Crowdsourcing or bust: The Indexer, Archives NZ
Digital History workshop: Crowdsourcing in the Humanities and cultural heritage sector. Victoria University of Wellington 23 April 2013
Session: Crowdsourcing or bust (The Indexer, Archives New Zealand)
Presenter: Tracie Almond
http://wtap.vuw.ac.nz/wordpress/digital-history/events/crowdsourcing-workshop/presenters/
E research africa presentation (19 nov 2014)
This document summarizes a presentation about research data management (RDM) at the University of Pretoria. It discusses the university's efforts to implement RDM, including conducting surveys of current practices, developing policies, and piloting RDM projects. Two key pilot projects involved using the Alfresco content management system to manage research data from the Institute for Cellular and Molecular Medicine and a neurophysiology group. The presentation outlines the university's process for implementing these pilots and next steps around dissemination, preservation, and addressing ongoing hurdles in developing comprehensive RDM.
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Presentation delivered at the eResearch Africa Conference, held 23-27 November 2014, at the University of Cape Town, Cape Town, South Africa. Various approaches to Research Data Management at Higher Education Institutions focus on an aspect or two of the research data cycle. At the University of Pretoria the approach has been to support researchers throughout the research process covering the whole research data cycle. The idea is to facilitate/capture the research data throughout the research cycle. This will give context to the data and will add provenance to the data. The University of Pretoria uses the UK Data Archive’s research data cycle model, to align its Research Data Management project-development. This model identifies the stages of a research data cycle as: creating data, processing data, analysing data, preserving data, giving access to data, and reusing data. This paper will give a short overview of the chronological development of research data management at the University of Pretoria. The overview will also highlight findings of two surveys done at the University, one in 2009 and one in 2013. This will be followed by a discussion of a number of pilot projects at the University, and how the needs of researchers involved in these projects are being addressed in a number of the stages of the research data cycle. The discussion will also give a short overview of how the University plans to support those stages not currently being addressed. The second part of the presentation will focus on the projects and technology (software and hardware) used. The University of Pretoria has adopted an Enterprise Content Management (ECM) approach to manage its Research Data. ECM is not a singular platform or system but rather a set of strategies, tools and methodologies that interoperate with each other to create a comprehensive management tool. These sets create an all-encompassing process addressing document, web, records and digital asset management. At the University of Pretoria we address all these processes with different software suites and tools to create a complete management system. Each process presented its own technical challenges. These had to be addressed, while keeping in mind the end objective of supporting researchers throughout the whole research process and data life cycle. Various platforms and standards have been adopted to meet the University of Pretoria’s criteria. To date three processes have been addressed namely, the capturing of data during the research process, the dissemination of data and the preservation of data.
Data Management for Undergraduate Researchers
This document summarizes a seminar on data management for undergraduate researchers. It discusses what data is, why it needs to be managed, and key aspects of the data management process such as data organization, metadata, storage, and archiving. Topics covered include file naming best practices, version control, documentation, metadata standards, storage options, and long-term archiving. The goal is to help researchers organize and document their data so it can be understood, preserved, and reused.
Digital Tools, Trends and Methodologies in the Humanities and Social Sciences
This document provides an overview of digital tools, trends and methodologies for the social sciences and humanities. It discusses defining digital humanities and gives examples of digital projects and resources. A case study is presented on exploring the lives of 19th century Ontario farmers through digitizing and analyzing journal entries. The document encourages thinking about how digital approaches can inform research and lists upcoming seminars on digital topics.
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Leveraging the Graph for Clinical Trials and Standards
Katja Glaß
OpenStudyBuilder Community Manager - Katja Glaß Consulting
Marius Conjeaud
Principal Consultant - Neo4j
Northern Engraving | Nameplate Manufacturing Process - 2024
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Mutation Testing for Task-Oriented Chatbots
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
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Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.Recently uploaded (20)
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
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Computational journalism projects
- 1. Reporterslab.org Presentation for computational journalism students February 2012
- 2. STRUCTURED DATA .. And most reporters’ inability to deal with it
- 3. New York Times reporters used Word searches and annotations to analyze Wikileaks documents in 2010 and 2011.
- 4. PANDA project trying to help gather data inside newsrooms
- 5. Barriers to Structured data analysis in the newsroom • Expensive • Too hard to collect. • It takes practice • It takes patience. • Once collected, data has a short shelf life – its value inside the newsroom effectively ends once a story is published.
- 6. Web-scraping software: ephemeral or too expensive for a task not viewed as mission- critical.
- 7. Solutions • User-friendly tool for scraping websites for structured data • Packages of algorithms from fraud and other forensic fields for use with public records datasets online. • Packages of queries and statistical tests for money, dates, geographical identifiers, names and codes, presented in standard English • Tools for fuzzy matching of datasets: include scoring, best match likelihood, interactive machine learning for different datasets.
- 8. TOO MUCH MATERIAL With too little information
- 9. Too many sources with too little news • Twitter, Facebook, LinkedIn and other social media • RSS feeds from other news organizations and blogs • Press releases from government agencies or beat subjects Lack of archiving is just as troubling as the lack of structure. Reporters can’t hold the powerful accountable without information from the past.
- 10. Solutions • Archiving users’ feeds locally or in the cloud • Mash-up social media, rss feeds into an app that reveals more insight into the sources • Formalize each reporter’s definition of “news” through machine learning. • Alerts for important source material. Example: changing time of a press conference.
- 11. The buried treasure UNUSABLE RECORDS
- 13. Solutions • Visual extractor of data from scanned forms. • Separate scanned boxes of documents into their pieces for further analysis • Use speech recognition tools on government audio and video • OCR video to find the speaker at a hearing
- 15. For unstructured data ANTIQUATED METHODS
- 16. Our way A newer way • Hand-enter individual items • Leverage web scraping and into spreadsheets paid crowdsourcing for data • Transcribe entry (MT) interviews, hearings and • Use speech recognition for other audio and video the first pass on searchable content for searching audio and video • Read each document • Use clustering, information extraction and other methods for overview of documents
- 17. Reporterslab.org working to tame audio and video
- 18. Associated Press project to bring order to unstructured data
- 20. Jigsaw
- 22. REPORTERSLAB.ORG Creating sample data and documents for researchers based on real stories