Article by Louise Matsakis and Will Knight: “The US National Institute of Standards and Technology (NIST) is discussing plans to eliminate an entire team responsible for publishing and maintaining critical atomic measurement data in the coming weeks, as the Trump administration continues its efforts to reduce the US federal workforce, according to a March 18 email sent to dozens of outside scientists. The data in question underpins advanced scientific research around the world in areas like semiconductor manufacturing and nuclear fusion…(More)”.
Web 3.0 Requires Data Integrity
Article by Bruce Schneier and Davi Ottenheimer: “If you’ve ever taken a computer security class, you’ve probably learned about the three legs of computer security—confidentiality, integrity, and availability—known as the CIA triad.a When we talk about a system being secure, that’s what we’re referring to. All are important, but to different degrees in different contexts. In a world populated by artificial intelligence (AI) systems and artificial intelligent agents, integrity will be paramount.
What is data integrity? It’s ensuring that no one can modify data—that’s the security angle—but it’s much more than that. It encompasses accuracy, completeness, and quality of data—all over both time and space. It’s preventing accidental data loss; the “undo” button is a primitive integrity measure. It’s also making sure that data is accurate when it’s collected—that it comes from a trustworthy source, that nothing important is missing, and that it doesn’t change as it moves from format to format. The ability to restart your computer is another integrity measure.
The CIA triad has evolved with the Internet. The first iteration of the Web—Web 1.0 of the 1990s and early 2000s—prioritized availability. This era saw organizations and individuals rush to digitize their content, creating what has become an unprecedented repository of human knowledge. Organizations worldwide established their digital presence, leading to massive digitization projects where quantity took precedence over quality. The emphasis on making information available overshadowed other concerns.
As Web technologies matured, the focus shifted to protecting the vast amounts of data flowing through online systems. This is Web 2.0: the Internet of today. Interactive features and user-generated content transformed the Web from a read-only medium to a participatory platform. The increase in personal data, and the emergence of interactive platforms for e-commerce, social media, and online everything demanded both data protection and user privacy. Confidentiality became paramount.
We stand at the threshold of a new Web paradigm: Web 3.0. This is a distributed, decentralized, intelligent Web. Peer-to-peer social-networking systems promise to break the tech monopolies’ control on how we interact with each other. Tim Berners-Lee’s open W3C protocol, Solid, represents a fundamental shift in how we think about data ownership and control. A future filled with AI agents requires verifiable, trustworthy personal data and computation. In this world, data integrity takes center stage…(More)”.
Cloze Encounters: The Impact of Pirated Data Access on LLM Performance
Paper by Stella Jia & Abhishek Nagaraj: “Large Language Models (LLMs) have demonstrated remarkable capabilities in text generation, but their performance may be influenced by the datasets on which they are trained, including potentially unauthorized or pirated content. We investigate the extent to which data access through pirated books influences LLM responses. We test the performance of leading foundation models (GPT, Claude, Llama, and Gemini) on a set of books that were and were not included in the Books3 dataset, which contains full-text pirated books and could be used for LLM training. We assess book-level performance using the “name cloze” word-prediction task. To examine the causal effect of Books3 inclusion we employ an instrumental variables strategy that exploits the pattern of book publication years in the Books3 dataset. In our sample of 12,916 books, we find significant improvements in LLM name cloze accuracy on books available within the Books3 dataset compared to those not present in these data. These effects are more pronounced for less popular books as compared to more popular books and vary across leading models. These findings have crucial implications for the economics of digitization, copyright policy, and the design and training of AI systems…(More)”.
Bubble Trouble
Article by Bryan McMahon: “…Venture capital (VC) funds, drunk on a decade of “growth at all costs,” have poured about $200 billion into generative AI. Making matters worse, the stock market’s bull run is deeply dependent on the growth of the Big Tech companies fueling the AI bubble. In 2023, 71 percent of the total gains in the S&P 500 were attributable to the “Magnificent Seven”—Apple, Nvidia, Tesla, Alphabet, Meta, Amazon, and Microsoft—all of which are among the biggest spenders on AI. Just four—Microsoft, Alphabet, Amazon, and Meta—combined for $246 billion of capital expenditure in 2024 to support the AI build-out. Goldman Sachs expects Big Tech to spend over $1 trillion on chips and data centers to power AI over the next five years. Yet OpenAI, the current market leader, expects to lose $5 billion this year, and its annual losses to swell to $11 billion by 2026. If the AI bubble bursts, it not only threatens to wipe out VC firms in the Valley but also blow a gaping hole in the public markets and cause an economy-wide meltdown…(More)”.
From Insights to Action: Amplifying Positive Deviance within Somali Rangelands
Article by Basma Albanna, Andreas Pawelke and Hodan Abdullahi: “In every community, some individuals or groups achieve significantly better outcomes than their peers, despite having similar challenges and resources. Finding these so-called positive deviants and working with them to diffuse their practices is referred to as the Positive Deviance approach. The Data-Powered Positive Deviance (DPPD) method follows the same logic as the Positive Deviance approach but leverages existing, non-traditional data sources, in conjunction with traditional data sources to identify and scale the solutions of positive deviants. The UNDP Somalia Accelerator Lab was part of the first cohort of teams that piloted the application of DPPD trying to tackle the rangeland health problem in the West Golis region. In this blog post we’re reflecting on the process we designed and tested to go from the identification and validation of successful practices to helping other communities adopt them.
Uncovering Rangeland Success
Three years ago we embarked on a journey to identify pastoral communities in Somaliland that demonstrated resilience in the face of adversity. Using a mix of traditional and non-traditional data sources, we wanted to explore and learn from communities that managed to have healthy rangelands despite the severe droughts of 2016 and 2017.
We engaged with government officials from various ministries, experts from the University of Hargeisa, international organizations like the FAO and members of agro-pastoral communities to learn more about rangeland health. We then selected the West Golis as our region of interest with a majority pastoral community and relative ease of access. Employing the Soil-Adjusted Vegetation Index (SAVI) and using geospatial and earth observation data allowed us to identify an initial group of potential positive deviants illustrated as green circles in Figure 1 below.
Following the identification of potential positive deviants, we engaged with 18 pastoral communities from the Togdheer, Awdal, and Maroodijeex regions to validate whether the positive deviants we found using earth observation data were indeed doing better than the other communities.
The primary objective of the fieldwork was to uncover the existing practices and strategies that could explain the outperformance of positively-deviant communities compared to other communities. The research team identified a range of strategies, including soil and water conservation techniques, locally-produced pesticides, and reseeding practices as summarized in Figure 2.
Data-Powered Positive Deviance is not just about identifying outperformers and their successful practices. The real value lies in the diffusion, adoption and adaptation of these practices by individuals, groups or communities facing similar challenges. For this to succeed, both the positive deviants and those learning about their practices must take ownership and drive the process. Merely presenting the uncommon but successful practices of positive deviants to others will not work. The secret to success is in empowering the community to take charge, overcome challenges, and leverage their own resources and capabilities to effect change…(More)”.
Integrating Social Media into Biodiversity Databases: The Next Big Step?
Article by Muhammad Osama: “Digital technologies and social media have transformed ecology and conservation biology data collection. Traditional biodiversity monitoring often relies on field surveys, which can be time-consuming and biased toward rural habitats.
The Global Biodiversity Information Facility (GBIF) serves as a key repository for biodiversity data, but it faces challenges such as delayed data availability and underrepresentation of urban habitats.
Social media platforms have become valuable tools for rapid data collection, enabling users to share georeferenced observations instantly, reducing time lags associated with traditional methods. The widespread use of smartphones with cameras allows individuals to document wildlife sightings in real-time, enhancing biodiversity monitoring. Integrating social media data with traditional ecological datasets offers significant advancements, particularly in tracking species distributions in urban areas.
In this paper, the authors evaluated the Jersey tiger moth’s habitat usage by comparing occurrence data from social media platforms (Instagram and Flickr) with traditional records from GBIF and iNaturalist. They hypothesized that social media data would reveal significant JTM occurrences in urban environments, which may be underrepresented in traditional datasets…(More)”.
The Language Data Space (LDS)
European Commission: “… welcomes launch of the Alliance for Language Technologies European Digital Infrastructure Consortium (ALT-EDIC) and the Language Data Space (LDS).
Aimed at addressing the shortage of European language data needed for training large language models, these projects are set to revolutionise multilingual Artificial Intelligence (AI) systems across the EU.
By offering services in all EU languages, the initiatives are designed to break down language barriers, providing better, more accessible solutions for smaller businesses within the EU. This effort not only aims to preserve the EU’s rich cultural and linguistic heritage in the digital age but also strengthens Europe’s quest for tech sovereignty. Formed in February 2024, the ALT-EDIC includes 17 participating Member States and 9 observer Member States and regions, making it one of the pioneering European Digital Infrastructure Consortia.
The LDS, part of the Common European Data Spaces, is crucial for increasing data availability for AI development in Europe. Developed by the Commission and funded by the DIGITAL programme, this project aims to create a cohesive marketplace for language data. This will enhance the collection and sharing of multilingual data to support European large language models. Initially accessible to selected institutions and companies, the project aims to eventually involve all European public and private stakeholders.
Find more information about the Alliance for Language Technologies European Digital Infrastructure Consortium (ALT-EDIC) and the Language Data Space (LDS)…(More)”
Panels giving scientific advice to Census Bureau disbanded by Trump administration
Article by Jeffrey Mervis: “…U.S. Secretary of Commerce Howard Lutnick has disbanded five outside panels that provide scientific and community advice to the U.S. Census Bureau and other federal statistical agencies just as preparations are ramping up for the country’s next decennial census, in 2030.
The dozens of demographers, statisticians, and public members on the five panels received nearly identical letters this week telling them that “the Secretary of Commerce has determined that the purposes for which the [committee] was established have been fulfilled, and the committee has been terminated effective February 28, 2025. Thank you for your service.”
Statistician Robert Santos, who last month resigned as Census Bureau director 3 years into his 5-year term, says he’s “terribly disappointed but not surprised” by the move, noting how a recent directive by President Donald Trump on gender identity has disrupted data collection for a host of federal surveys…(More)”.
New AI Collaboratives to take action on wildfires and food insecurity
Google: “…last September we introduced AI Collaboratives, a new funding approach designed to unite public, private and nonprofit organizations, and researchers, to create AI-powered solutions to help people around the world.
Today, we’re sharing more about our first two focus areas for AI Collaboratives: Wildfires and Food Security.
Wildfires are a global crisis, claiming more than 300,000 lives due to smoke exposure annually and causing billions of dollars in economic damage. …Google.org has convened more than 15 organizations, including Earth Fire Alliance and Moore Foundation, to help in this important effort. By coordinating funding and integrating cutting-edge science, emerging technology and on-the-ground applications, we can provide collaborators with the tools they need to identify and track wildfires in near real time; quantify wildfire risk; shift more acreage to beneficial fires; and ultimately reduce the damage caused by catastrophic wildfires.
Nearly one-third of the world’s population faces moderate or severe food insecurity due to extreme weather, conflict and economic shocks. The AI Collaborative: Food Security will strengthen the resilience of global food systems and improve food security for the world’s most vulnerable populations through AI technologies, collaborative research, data-sharing and coordinated action. To date, 10 organizations have joined us in this effort, and we’ll share more updates soon…(More)”.
Government data is disappearing before our eyes
Article by Anna Massoglia: “A battle is being waged in the quiet corners of government websites and data repositories. Essential public records are disappearing and, with them, Americans’ ability to hold those in power accountable.
Take the Department of Government Efficiency, Elon Musk’s federal cost-cutting initiative. Touted as “maximally transparent,” DOGE is supposed to make government spending more efficient. But when journalists and researchers exposed major errors — from double-counting contracts to conflating caps with actual spending — DOGE didn’t fix the mistakes. Instead, it made them harder to detect.
Many Americans hoped DOGE’s work would be a step toward cutting costs and restoring trust in government. But trust must be earned. If our leaders truly want to restore faith in our institutions, they must ensure that facts remain available to everyone, not just when convenient.
Since Jan. 20, public records across the federal government have been erased. Economic indicators that guide investments, scientific datasets that drive medical breakthroughs, federal health guidelines and historical archives that inform policy decisions have all been put on the chopping block. Some missing datasets have been restored but are incomplete or have unexplained changes, rendering them unreliable.
Both Republican and Democratic administrations have played a role in limiting public access to government records. But the scale and speed of the Trump administration’s data manipulation — combined with buyouts, resignations and other restructuring across federal agencies — signal a new phase in the war on public information. This is not just about deleting files, it’s about controlling what the public sees, shaping the narrative and limiting accountability.
The Trump administration is accelerating this trend with revisions to official records. Unelected advisors are overseeing a sweeping reorganization of federal data, granting entities like DOGE unprecedented access to taxpayer records with little oversight. This is not just a bureaucratic reshuffle — it is a fundamental reshaping of the public record.
The consequences of data manipulation extend far beyond politics. When those in power control the flow of information, they can dictate collective truth. Governments that manipulate information are not just rewriting statistics — they are rewriting history.
From authoritarian regimes that have erased dissent to leaders who have fabricated economic numbers to maintain their grip on power, the dangers of suppressing and distorting data are well-documented.
Misleading or inconsistent data can be just as dangerous as opacity. When hard facts are replaced with political spin, conspiracy theories take root and misinformation fills the void.
The fact that data suppression and manipulation has occurred before does not lessen the danger, but underscores the urgency of taking proactive measures to safeguard transparency. A missing statistic today can become a missing historical fact tomorrow. Over time, that can reshape our reality…(More)”.