Stefanie Haustein Lightning talk at NISO Altmetrics Initiative meeting

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National Information Standards Organization (NISO)

Stefanie Haustein – "Exploring disciplinary differences in the use of social media in scholarly communication" Lightning talk at NISO Altmetrics Initiative meeting in San Francisco, CA October 9, 2013

Technology Health & Medicine

Exploring disciplinary differences
in the use of social media
in scholarly communication
Stefanie Haustein

Mendeley coverage and correlation with citations of PubMed/WoS
Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Mendeley readers were collected via the API. Mendeley coverage is based on all papers, correlations are
calculated for papers published in 2011, which were at least read once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at
least 30 papers from 2011 on Mendeley. Bubble size represents average number of readers per document on Mendeley.
• coverage: 66.2%
• documents with ≥1 Mendeley
reader: 951,421
• mean number of readers per
document with ≥1 Mendeley
reader: 9.7
• correlation with citations of
P(2011)read: ρ = 0.456**

Twitter
Twitter coverage and correlation with citations of PubMed/WoS
Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Tweets were collected from Altmetric.com. Twitter coverage is based on all papers, correlations are calculated for
papers published in 2011, which were at least tweeted once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30
tweeted papers in 2011. Bubble size represents average number of tweets per tweeted document.
• coverage: 9.4%
• documents with ≥1 tweets:
134.924
• mean number of tweets per
tweeted document: 2.5
• correlation with citations of
P(2011)read: ρ = 0.157**

Twitter coverage and correlation with citations of PubMed/WoS
Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Tweets were collected from Altmetric.com. Twitter coverage is based on all papers, correlations are calculated for
papers published in 2011, which were at least tweeted once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30
tweeted papers in 2011. Bubble size represents average number of tweets per tweeted document. Correlations are significant at the 0.01 (**) or 0.05 (*) level (two-tailed).

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Traditional bibliometric methods of evaluating academic research, such as journal impact factors and article citations, have been supplemented in the past 5-10 years by the development of altmetrics (alternative metrics or article level metrics). Altmetrics measures impact of research, data and publications, such as references in data and knowledge bases, article views, downloads and mentions in social media and news media. This presentation gives a brief background to altmetrics and demonstrates how Rhodes University librarians are using social media to raise the visibility of the research output of their institution. (Rhodes University is in Grahamstown, South Africa)

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Work-in-progress presentation at Social Media & Society 2015 Haustein, S., Bowman, T.D. & Costas, R. (2015). Communities of attention' around journal papers: Who is tweeting about scientific publications? https://socialmediaandsociety.com/ http://smsociety15.sched.org/event/91e44f025248a9f40e64302c12ce567d/edit#.VbfKfBNViko Background: ‘Altmetrics’ have been introduced as a way to capture scientific output and impact beyond papers and citations based on traces on various social media platforms (Priem, Taraborelli, Groth, & Neylon, 2010), of which Twitter is believed to have a particular potential to reflect societal impact of research. The analysis and application of various altmetrics such as tweets to scientific papers, however, still lack adequate interpretative frameworks mainly because the processes behind the metrics are not yet fully understood. Currently each tweet is counted equally on platforms such as Altmetric.com or ImpactStory and studies tend to ignore user type and tweet content, although tweets have been shown to range from serious discussions to humour and self-promotion to automated mentions (Haustein et al., 2015). Objective: Communities of attention around scientific publications on Twitter are identified based on engagement and exposure of users. Engagement is measured as the degree to which the tweet text differs from the title of the tweeted paper. Exposure refers to the potential audience of the tweet as measured by the number of the user’s followers. Methods: Publications from 2012 covered by Web of Science were matched to tweets (until June 2014, excluding retweets) recorded by Altmetric.com via DOI resulting in 660,149 tweets, 237,222 tweeted papers, and 125,083 Twitter users. Engagement was calculated based on the dissimilarity between the tweet text (excluding user names and URLs) and the title of the tweeted document. User data (including the number of followers representing exposure) was collected from Altmetric.com and the Twitter API. Four user categories were defined, classifying users into four quadrants A, B, C and D according to engagement and exposure values above and below the median of the whole dataset (Figure 1). Statistics based on the tweeting behaviour of users were calculated for each of the categories. The connections between 708 users with more than 100 publications based on co-mentions of the same papers were visualized in a network graph in Figure 1. Results: Users in the four categories differ according to tweeting behavior (Figure 1). Users in A have the highest mean tweets per day (based on all tweets) and those in D tweet more about scientific papers (typical for bots identified by Haustein et al. (2015)), while users in A and B discuss publications with slightly higher relative citation rates.

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This study explores the readerships in Mendeley across 5 major fields of science in Leiden Ranking 2013 for a data set of 1,107,917 Web of Science (WoS) publications (reviews and articles) from all disciplines published in 2011 with DOI available. The main objective is to know if there are different patterns in terms of readership and citation impact depending on the different ‘Academic Status’ of Mendeley readers. In case of finding different pattern, this could help to introduce the possibility of considering the different users as potential predicting elements of citations. The current study is built upon the previous study of analyzing Mendeley users with focus on the types of the different Mendeley users (known users) in order to explore their patterns of saving publications in terms of subject fields, citation and readership impact. Particular attention has been paid to the extent to which the readerships of the publications saved by the different types of users in Mendeley correlate with their citation indicators and across 5 major fields of science in the Leiden Ranking (LR); also, the potential of identifying highly cited papers by different user types in Mendeley has been investigated. For this reason, we present an exploratory analysis of the patterns of reading of the different types of users in Mendeley and we study their relationship with citations and across LR fields.

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Haustein, S. (2015). Scientific Interactions and Research Evaluation: From Bibliometrics to Altmetrics Keynote at ISI2015 in Zadar, Croatia http://isi2015.de/?session=keynote-c-i Abstract. Since its creation 350 years ago, the scientific peer-reviewed journal has become the central and most important form of scholarly communication in the natural sciences and medicine. Although the digital revolution has facilitated and accelerated the publishing process by moving from print to online, it has not changed the scientific journal and scholarly communication as such. Today publications and citations in peer-reviewed journals are considered as indicators of scientific productivity and impact and used and misused in research evaluation. As scholarly communication is becoming more open and diverse and manuscripts, data, presentations and code are shared online, the altmetrics and open science movement demand the adaption of evaluation practices. Parallels are drawn between the early days of bibliometrics and current altmetrics research highlighting possibilities and limitations of various metrics and warning against adverse effects.

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Spring Boot vs Quarkus the ultimate battle - DevoxxUK

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Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

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DianaGray10

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Dropbox

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Zilliz

Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Jeffrey Haguewood

ICT role in 21st century education and its challenges

rafiqahmad00786416

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

Artificial Intelligence Chap.5 : Uncertainty

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The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

apidays

The action of the next cyber saga takes place in the mystical lands of the Asia-Pacific region, where the main characters began their digital activities in the middle of 2021 and qualitatively strengthened it in 2022. Corporate espionage, document theft, audio recordings, and data leaks from messaging platforms were all a matter of one day for Dark Pink. Their geographical focus may have started in the Asia-Pacific region, but their ambitions knew no bounds, targeting a European government ministry in a bold move to expand their portfolio. Their victim profile was as diverse as a UN meeting, targeting military organizations, government agencies, and even a religious organization. Because discrimination is not a fashionable agenda. In the world of cybercrime, they serve as a reminder that sometimes the most serious threats come in the most unassuming packages with a pink bow.

Cyberprint. Dark Pink Apt Group [EN].pdf

Overkill Security

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

panagenda

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Angeliki Cooney

Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

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Stefanie Haustein Lightning talk at NISO Altmetrics Initiative meeting

1. Exploring disciplinary differences in the use of social media in scholarly communication Stefanie Haustein

2. Mendeley coverage and correlation with citations of PubMed/WoS Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Mendeley readers were collected via the API. Mendeley coverage is based on all papers, correlations are calculated for papers published in 2011, which were at least read once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30 papers from 2011 on Mendeley. Bubble size represents average number of readers per document on Mendeley. • coverage: 66.2% • documents with ≥1 Mendeley reader: 951,421 • mean number of readers per document with ≥1 Mendeley reader: 9.7 • correlation with citations of P(2011)read: ρ = 0.456**

3. Twitter Twitter coverage and correlation with citations of PubMed/WoS Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Tweets were collected from Altmetric.com. Twitter coverage is based on all papers, correlations are calculated for papers published in 2011, which were at least tweeted once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30 tweeted papers in 2011. Bubble size represents average number of tweets per tweeted document. • coverage: 9.4% • documents with ≥1 tweets: 134.924 • mean number of tweets per tweeted document: 2.5 • correlation with citations of P(2011)read: ρ = 0.157**

4. Mendeley coverage and correlation with citations of PubMed/WoS Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Mendeley readers were collected via the API. Mendeley coverage is based on all papers, correlations are calculated for papers published in 2011, which were at least read once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30 papers from 2011 on Mendeley. Bubble size represents average number of readers per document on Mendeley. Correlations are significant at the 0.01 (**) or 0.05 (*) level (two-tailed).

5. Twitter coverage and correlation with citations of PubMed/WoS Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Tweets were collected from Altmetric.com. Twitter coverage is based on all papers, correlations are calculated for papers published in 2011, which were at least tweeted once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30 tweeted papers in 2011. Bubble size represents average number of tweets per tweeted document. Correlations are significant at the 0.01 (**) or 0.05 (*) level (two-tailed).

Stefanie Haustein Lightning talk at NISO Altmetrics Initiative meeting

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