October 2026
Which file formats to use for research data (and which to avoid)
Which formats still open in twenty years, why Excel is a risky archive, and the two-copy rule for working and preservation files.
Read more →Writing
Notes on AI, research computing, and data in higher education
These are the questions I get asked most, written out so you can use them without a call. If one lines up with something you're working on, the contact form is at the bottom of the home page.
October 2026
ORCID, DOIs, and persistent identifiers: a plain-language guide for researchers
What ORCID iDs, DOIs, and ROR IDs are, why funders and repositories ask for them, and a short setup checklist.
Read more →October 2026
How to write a README for your dataset
What belongs in a dataset README, a plain-text template to copy, and the mistakes that make shared data hard to reuse.
Read more →October 2026
What is a data use agreement, and how do you get one signed?
What a data use agreement is, the clauses that cause trouble, who actually signs, why it takes weeks, and a checklist for getting one through your research office.
Read more →October 2026
How long should you keep research data?
The federal three-year floor, the six-year misconduct window, IRB and FDA rules, the conflict with destruction promises in consent forms, and a five-question retention plan you can paste into a DMP.
Read more →September 2026
How to write consent language that lets you share your data
What a consent form has to say for data sharing to be possible, the template phrases that quietly rule it out, sample wording to adapt with your IRB, and what to do with data you already collected.
Read more →September 2026
How to budget for data management and sharing in a grant
What NIH and NSF allow you to charge for data management and sharing, how to show it now that NIH dropped the single budget line, a six-step way to estimate the cost, example justification entries, and the mistakes that get costs cut.
Read more →September 2026
How to de-identify research data before you share it
Direct and indirect identifiers, how HIPAA Safe Harbor, FERPA, and the Common Rule differ, an eight-step de-identification pass, a simple small-cell check in R or Stata, and what to do when the data can't be safely de-identified.
Read more →September 2026
How to write a codebook (data dictionary) for your research data
What a codebook or data dictionary is, the dataset-level and variable-level details it needs, a copyable template with sample rows, how to generate a draft in Stata, SPSS, R, REDCap, or Qualtrics, and the mistakes to avoid.
Read more →September 2026
The FAIR data principles, in plain language
What each of the four letters asks of you in practice, the misreading that FAIR means open, how to describe FAIR in a data management plan without empty promises, and an eight-step checklist.
Read more →September 2026
How to cite a dataset (and why it counts)
Why data belong in the reference list, the seven parts every data citation needs, APA 7 and DataCite formats with a real example, how to cite versions, subsets, and public data without a DOI, and the common mistakes.
Read more →September 2026
AI Ready NJ: what New Jersey's community college AI initiative actually offers
Faculty mini-grants, paid student Sprinternships through Break Through Tech, and a statewide inventory of AI courses across New Jersey's 18 community colleges, plus where a campus AI policy still has to fill in the gaps.
Read more →September 2026
New Jersey's AI Hub and its state supercomputer: what it means for small colleges
New Jersey signed a memorandum of understanding with NVIDIA for a $25 million statewide supercomputer for higher education. What's confirmed, what's still open, and how a small campus positions itself now.
Read more →September 2026
How to write a data availability statement
What journals want, the five statements that fit almost every paper (public repository, human-subjects restrictions, third-party data, public data reuse, no new data), wording you can adapt, and the mistakes editors flag, including why "available upon request" is losing favor.
Read more →August 2026
How to make your analysis reproducible six months from now
A practical guide to reproducible research: pinning your software environment, setting random seeds, keeping raw data raw, avoiding hard-coded paths, and writing a README a reviewer can follow.
Read more →August 2026
How to start research data services at a small institution
A practical guide for librarians and administrators at small campuses who want to offer data services without a dedicated team or big budget. Start with DMP review, borrow national infrastructure, and grow one offering at a time.
Read more →August 2026
How to use IPEDS data without misreading it
The free federal dataset behind almost every college statistic: where to get it, which of the four tools fits your question, and the definitions (graduation rate cohorts, two enrollment counts, two accounting standards) that mislead first-time users.
Read more →August 2026
Your first job on a research computing cluster
You have a cluster account and no idea what to type. Login nodes, schedulers, submit scripts, storage that isn't backed up, and the mistakes that kill first jobs.
Read more →July 2026
How to choose a research data repository
Where the data go when the paper is done: discipline, institutional, and general repositories, the six things to check before you pick one, and how to share data about people without getting it wrong.
Read more →July 2026
How to write a campus AI policy people will actually follow
Not a ban and not a press release: what a real university AI policy covers, how it maps to policies you already have, and the one-page data grid that answers most questions.
Read more →July 2026
How to organize research data so future you can find it
Folder structure, file naming, versioning, a README that works, and backups that survive a lost laptop. Five decisions made once, at the start, that save the project later.
Read more →July 2026
OSPool or ACCESS? A researcher's guide to free national computing
Your analysis won't fit your laptop and you have no campus cluster. How to tell whether the OSPool or NSF ACCESS fits your work, both free, and get an account this week.
Read more →July 2026
Did the program work? A plain-language guide to program evaluation
Why "we ran the program and things got better" isn't an answer, the "compared to what" problem at the center of every evaluation, and which designs a real budget can actually support.
Read more →July 2026
Data ethics for researchers: questions to ask before you collect, share, or automate
What consent actually covers, why dropping the name column doesn't de-identify anything, when secondary use needs another look, and what to check before data touches an AI tool.
Read more →July 2026
National research computing for small institutions: what's free and how to start
Your researchers likely qualify for national computing resources like the OSPool and NSF ACCESS at no cost. Why small campuses miss out, and how to get a first researcher running.
Read more →July 2026
AI literacy for higher education: what to actually teach
The core skills worth teaching faculty and staff, a few durable questions to ask of any AI answer, and how to run a workshop that changes habits instead of just impressing the room.
Read more →July 2026
How to write a data management plan a funder will accept
What each required section of a DMP actually asks for, the mistakes that get plans flagged, and a reusable outline you can adapt per grant.
Read more →July 2026
Private AI over institutional data: a primer for universities
What private, document-grounded AI is, when a university should use it instead of a public chatbot, and how to build one responsibly.
Read more →