Building Lasting Data Connections Through IASSIST 2017

IASSIST 2017 brought researchers, librarians, information professionals and data specialists together at the University of Kansas in Lawrence from 23 to 26 May 2017. Its theme, “Data in the Middle: The Common Language of Research,” reflected a practical reality: useful data work depends on relationships between disciplines, institutions and communities.

Although the conference is now an archived event, its programme remains valuable for anyone studying how professional networks form around research data. The sessions on big data, deep learning, digital agriculture and global food security show how a well-designed conference can connect technical expertise with policy, library practice and real-world research problems.

Networking setting	Best opportunity	Connection that can develop
Plenary session	Hear shared concerns across disciplines	A broad professional relationship
Thematic presentation	Discuss a specific research method or challenge	A potential collaborator
Meal or coffee break	Exchange informal experiences	A trusted peer contact
Practical workshop	Work through a common problem	A skills-based partnership
Online follow-up	Share resources after the event	A continuing research network

Why The Archived Conference Still Matters

An archived conference website can seem less immediate than a live event, yet IASSIST 2017 offers a useful record of how data science was being discussed at a significant point in its development. Big data was moving from a specialist phrase into mainstream research planning, deep learning was gaining visibility, and agricultural data was becoming increasingly important to food policy and environmental decision-making.

For Australian professionals, these themes remain familiar. A data librarian in Canberra, a research software engineer in Melbourne or an agricultural scientist in regional New South Wales may work on different projects while facing similar questions about metadata, access, storage and reproducibility. The archived programme helps reveal where those interests overlap.

Networking in data science is often described as a search for collaborators, but professional connection can begin with a much smaller exchange. A conversation about a dataset’s documentation, a discovery made during a plenary or a recommendation for a useful repository may later become a shared project. The IASSIST archive captures the kinds of settings where those exchanges were possible.

Start With Shared Research Problems

Strong conference conversations usually begin with a problem rather than a job title. Someone working on climate records may be interested in the same data-quality issue as a public health researcher, even if neither person considers their field to be closely related. IASSIST 2017 was structured around this kind of cross-disciplinary contact.

Before reviewing the programme, it helps to identify two or three problems that matter in your own work. These might include inconsistent metadata, limited access to sensitive information, weak data literacy among project teams or difficulties explaining machine-learning results to non-specialists. Clear concerns give a professional conversation direction and make it easier to recognise relevant sessions.

An Australian attendee might connect these questions with national examples. The Australian Research Data Commons supports discovery and collaboration across research institutions, while CSIRO projects often involve large, complex datasets and partnerships beyond universities. These settings create a natural need for people who can translate between technical systems, research objectives and public value.

Use The Programme As A Networking Map

The archived schedule can be read as more than a list of presentations. It provides a map of interests, vocabulary and potential professional communities. Sessions on deep learning may attract statisticians, computer scientists and domain researchers, while digital agriculture can bring together data managers, agronomists, economists and government representatives.

Look for themes that sit between established fields. Big data, for example, is not solely a computing topic. It raises questions about preservation, governance, ethics, discoverability and institutional responsibility. Global food security similarly connects modelling and remote sensing with local knowledge, supply chains and public policy.

A useful approach is to mark sessions in three categories: subjects you already understand, subjects that are adjacent to your work and subjects where you need a basic orientation. The middle category is especially valuable for networking. It offers enough familiarity to begin a conversation while leaving room to learn from someone with a different perspective.

Turn Presentations Into Conversations

A presentation gives you a reason to approach someone, but a vague compliment is rarely enough to create a memorable professional exchange. Refer to a specific idea, method or example from the session. You might ask how the speaker handled data provenance, whether a model was reusable in another field or how a research team persuaded its institution to adopt a new standard.

The aim is not to demonstrate that you understood every technical detail. Good networking can involve admitting that a concept is new to you and asking for a plain-language explanation. People often remember colleagues who show genuine attention and connect their own work thoughtfully, rather than those who attempt to dominate the discussion.

The same principle applies when speaking with other attendees. Instead of asking only where someone works, ask what kind of data their team manages, which stage of a project causes the most friction or what they hope to improve. These prompts move a conversation from routine biography towards shared professional concerns.

Make Informal Time Count

Coffee breaks, meals and travel between venues often produce the most useful connections because they remove the pressure of a formal presentation. A short conversation over lunch can reveal practical information that would never appear in an abstract: which repository is reliable, how a funding body interprets open data or where a team found training in data stewardship.

Conference customs differ across countries, and Australian attendees may recognise the value of informal, low-pressure interaction. A quick chat while waiting for coffee can be more effective than a highly rehearsed pitch. Australians travelling from Sydney, Brisbane or Perth may also need to manage long flights and time-zone changes, so simple preparation helps conserve energy for these social moments.

The Lawrence setting offered local opportunities alongside the formal programme. Attendees could use check-in guidance, transportation information and local recommendations to move confidently around the event. Familiarity with practical details matters because uncertainty about getting to a venue or finding a meal can make people retreat to their own group.

Useful habits for informal professional exchange include:

• Keep a short explanation of your current project ready in plain English.
• Ask about the other person’s data challenge before describing your own work.
• Make a note of one distinctive detail after an important conversation.
• Exchange contact details only when there is a clear reason to continue.
• Include students, early-career staff and quieter participants in group discussions.

Build Bridges Across Technical And Social Fields

The IASSIST 2017 programme linked technical subjects such as deep learning with broader concerns about research practice and public benefit. That combination is important because data science rarely succeeds through algorithms alone. It also depends on governance, documentation, human judgement, domain expertise and communication.

A machine-learning specialist may need help understanding the context in which data was collected. A subject librarian may understand discovery and preservation but want to learn how researchers are using automated methods. An agricultural researcher may have detailed local knowledge but need support with data structures and reproducible workflows. Each person brings a different part of the solution.

Australian institutions face similar intersections. Research teams may work across universities, state departments, industry and community organisations. In agriculture, data from farms, satellites, weather stations and supply networks can be combined, but that does not mean it should be combined without careful consent, documentation and governance. Indigenous data sovereignty also reminds professionals that data about communities requires respect for authority, context and cultural responsibility.

Connection grows when participants recognise these differences as assets rather than barriers. A useful professional network includes translators: people who can explain technical choices to decision-makers, research needs to information specialists and community concerns to project teams.

Follow Up With Something Useful

A new contact becomes part of a network only when there is a meaningful follow-up. Sending a generic message several weeks later may be polite, but it gives the relationship no direction. A better message refers to the conversation and offers something relevant, such as a paper, repository guide, event recording or example of a data-management workflow.

Follow-up does not need to propose a major collaboration. You might arrange a short call to compare approaches to metadata, share a training resource with a colleague or invite the person to a professional webinar. Small exchanges create familiarity and make future contact feel natural.

For Australian professionals, local networks can provide a practical continuation point. Groups in Melbourne, Sydney, Canberra and Adelaide often host research-data, digital-humanities and research-software events. A person met through an international conference may later become a speaker, reviewer or collaborator in an Australian setting.

A simple follow-up sequence can include:

• Write a brief note within a week while the conversation is still fresh.
• Mention the session, topic or problem that created the connection.
• Share one useful resource rather than sending a large collection of links.
• Record the contact in a professional system with appropriate privacy care.
• Reconnect when you have a genuine update or relevant opportunity.

Develop A Network That Can Travel

Conference networking is most effective when it extends beyond one event, institution or country. The contacts associated with IASSIST 2017 could support conversations about data curation, digital scholarship and research infrastructure long after the Lawrence meeting ended. An international network can expose professionals to different approaches to funding, repository design and collaboration.

For Australians, distance makes this especially important. Travelling from Perth to North America involves considerable time and cost, while even domestic travel between Darwin, Brisbane and Melbourne can affect attendance decisions. Digital meetings, professional mailing lists and shared repositories help maintain contact between in-person events.

A strong network should also remain useful when a person changes roles. Researchers move between universities, libraries, government agencies and industry. Skills in data governance, reproducibility and research communication are transferable, so relationships built around genuine expertise can remain valuable even when job titles change.

The archived IASSIST material supports this long view. It shows that networking is not a separate activity added to technical work. It is part of how research communities define common problems, exchange methods and develop shared standards. Reviewing an older programme can therefore prompt current professionals to ask which connections are missing from their own projects.

Carry The Lessons Into Current Practice

The most lasting lesson from IASSIST 2017 is that data science depends on a common language. That language is not limited to programming terms or statistical methods. It includes questions about purpose, evidence, stewardship, access, cultural responsibility and the people affected by research decisions.

A professional who approaches networking with these concerns in mind is more likely to form useful, durable connections. The goal is not to collect the largest number of business cards or social-media contacts. It is to recognise compatible interests, listen carefully and create a reason for a future exchange.

The archived conference website remains a valuable starting point for that process. Its programme and practical information show how plenaries, specialist sessions and informal encounters can work together. For an Australian audience, the same principles apply across universities, CSIRO-linked projects, public agencies, libraries, farms and technology companies.

Building connections in data science is ultimately an exercise in translation and trust. When people can explain their work clearly, respect different forms of expertise and follow through on small commitments, a conference becomes more than a temporary gathering. It becomes part of a professional network capable of supporting better research long after the event itself has ended.