ReaDI Program

• Training Finder Tool | The Finder creates a personalized chart of required and recommended training courses, with links to the training and the responsible offices.  The identified courses can be added to your Rascal Training To-do List.
• Recommended Training
  • Responsible Conduct of Research (RCR) | Rascal Course TC0094
  • Good Laboratory Notebook Practices | Rascal Course TC2651
  • Responsible and Ethical Conduct of Research (RECR) for Faculty and Other Senior Personnel | Rascal Course TC7000
  • Recognizing Influences and Biases in Research | Rascal Course TC4900
  • Robust Science: Problems and Solutions | Rascal Course TC4901
  • Best Practices for Data Management When Using Instrumentation | Rascal Course TC2650
  • Guidelines on the Organization of Samples in a Laboratory | Rascal Course TC3250

  • TC7350Handling Digital Scientific Images Dos & Don’ts | Rascal Course TC7350

Whether you call it a lab manual, an onboarding document, or standard operating procedures, a written document can streamline research processes and ensure high-quality research. The document should include your expectations for conducting research (including quality controls) and maintaining data. Other principal investigators have found these documents helpful for communicating with their group members.  Free SOP consulting and drafting services are available for Columbia PIs.

• Why Some Professors Welcome New Lab Members with Clear Expectations—In Writing (Science)
• Creating a New Member Handbook (ORI)
• SOP Examples
  • Prof. Mariam Aly’s Lab Manual (as seen in Nature “The Key to a Happy Lab Life is in the Manual”)
  • Prof. Brent Stockwell’s New Member Information
  • Prof. Helen Lu’s Biomaterials and Tissue Interface Laboratory Procedures
  • Prof. Nicholas Turro’s Laboratory Notebook Procedures
  • Prof. Anne McNeil’s Group Manual (U. of Michigan)
• Developing a Lab Data Management Plan

The webpage by Organizo LLC offers a suite of practical tools and templates designed to streamline organization in research labs. From managing inventories and orders to tracking applications and outlining team responsibilities, these resources aim to improve efficiency and clarity in daily operations. All documents are downloadable and customizable, making them versatile for various professional and lab settings.

Organizational Spreadsheets

• Ordering Spreadsheet: Tracks order statuses to help lab members plan their work based on item arrivals. Can be customized for any group ordering system.
• -80 Freezer Inventory: Simplifies freezer organization for -80°C storage and can be adapted for other temperature-controlled storage (-20°C, 4°C).
• Antibody Inventory: Helps locate lab items like antibodies quickly with a system to define storage locations and contents.

Application Management

• Application Tracker: A spreadsheet for managing deadlines and requirements for funding applications, shareable with institutional grant teams.

Human Resource Tools

• Employee Expectations: A template outlining job expectations for lab aides, customizable for various research roles.
• Lab Responsibilities: A document detailing responsibilities for different roles (e.g., Lab Aide, Research Technician, Animal Technician) to ensure smooth lab operations.

• Research Rigor and Reproducibility: Columbia's Leadership in Research Quality and Gold Standard Science: This "Facilities and Resources" insert for grant applications highlights the ReaDI Program's training courses, online resources, and one-on-one consultations, along with Columbia's triennial symposium series that brings together researchers to discuss reproducibility and emerging challenges in research integrity.
• Scientific Rigor and Reproducibility Toolbox (American Physiology Society Science Policy Committee)
• Rigor of Prior Research:
  • 6 WAYS To Access Rigor of Prior Research
  • Six red flags for suspect work
• Authentication Plans
  • General guidelines for authentication plan (NIH)
  • Authentication plan examples (NIH)
  • Antibody Validation
    • Best Practices for Antibodies (Sigma Aldrich)
    • Antibody Validation (The Human Protein Atlas)
    • Common pitfalls when working with antibodies & common practices for validating antibodies (Biotechniques, 2010)
    • CiteAb - Database that ranks antibodies by the number of times they’ve been cited in publications
  • Cell Line Validation
    • Cell Line Checklist for Manuscripts and Grant Applications (ICLAC)
    • Register of Misidentified Cell Lines (ICLAC)
    • Guidelines for the use of cell lines in biomedical research (Br J Cancer. 2014)
    • Standards for Cell Line Authentication and Beyond (PLoS Biol. 2016)
  • Sex as a Biological Variable (SABV)
    • Sex as a Biological Variable – A Primer (NIH)
    • Gender Decision Tree; SABV in Biomedicine Checklist
  • Sample Authentication Plans: Example 1 (NIH); Example 2 (IDEXX Bioresearch)

• Training and Other Resources for Rigor and Reproducibility (NIH): Resources and training on many aspects of rigor and reproducibility, including sex as a biological variable, research methods, reviewer guidance and more.
• Scientific Rigor Examples (NIH)
• Resources and Tools for Rigorous Experimental Design (NIH)
  • The Experimental Design Assistant – EDA
  • EQUATOR Network Reporting Guidelines
• Experimental Design Checklist (From Casadevall and Fang, How-To Guide)
• Let’s Experiment: A Guide for Scientists Working at the Bench (iBiology) | Scientists from a variety of backgrounds give concrete steps and advice to help you build a framework for how to design experiments in biological research.
• 6 Rules of Thumb for Determining Sample Size and Statistical Power (The Abdul Latif Jameel Poverty Action Lab)
• Sample Size and Power Estimate Online Calculator | Free, Online, Easy-to-Use Power and Sample Size Calculators.

It's never too early to start thinking about how your data will be managed throughout your research. Planning ensures that your data is collected, organized, and stored in a way that maximizes its value and utility, both for your current projects and future research. By establishing clear protocols for data handling from the outset, you can avoid common pitfalls like data loss, ethical breaches, or difficulties in data sharing and reuse. Thoughtful data management also facilitates collaboration, enhances the reproducibility of your work, and ensures compliance with institutional and funding agency requirements. Taking the time now to consider how your data will be handled can save you significant effort later and contribute to the overall integrity and impact of your research.

• What types and formats of data will our lab collect?
• What ethical considerations must we address when working with human or animal subjects, and what steps will we take to ensure privacy, confidentiality, and compliance?
• What documentation and metadata standards will we use to organize and describe our data?
• Who will need access to our data, and how can we ensure it is usable for future research or collaboration?
• What access restrictions will we apply to protect sensitive data?
• Where will we store our data during and after our research projects?
• What are the projected costs associated with managing, documenting, storing, and preserving our lab's data?

• De-identification and Anonymization of Data
  • Strategies for De-Identification and Anonymization of Electronic Health Record Data for Use in Multicenter Research Studies (Kushida CA et al., 2012)
  • A scalable software solution for anonymizing high-dimensional biomedical data (Meurers T et al., 2021)
  • De-identification methods for open health data: the case of the Heritage Health Prize claims dataset (El Emam K et al., 2012)
  • What do I need to know about protecting study participants' privacy, HIPAA, and subject de-identification for dbGaP data submissions? The database of Genotypes and Phenotypes (dbGaP)
  • Protecting privacy using k-anonymity (El Emam K and Dankar FK, 2008)
  • 5 steps for removing identifiers from datasets (John Hopkins Sheridan Libraries)
• Best Practices for Data and Code Management (2015, Innovations for Poverty Action) | This guide outlines best practices in data and code management. The scope of the guide is to cover the principles of organizing and documenting materials at all steps of the project lifecycle with the goal of making research reproducible.
• Guide to writing README for metadata (Cornell University)
• Find Files Faster: How to Organize Files and Folders
• Best Practices for Data Management when Using Instrumentation | Tips and effective practices for collecting, saving, and processing data collected from instruments. Also available on Rascal Training - TC2650.

• Notebooks
  • Good Laboratory Notebook Practices | Tips and best practices for maintaining a laboratory notebook | Rascal Course TC2651.
  • Laboratory Notebook Checklist | Adapted from the tutorial on Good Laboratory Notebook Practices, this checklist can be used by researchers to manage their notebooks or by PIs who may review group members' notebooks.
  • Electronic Laboratory Notebook (ELN) | Columbia University provides a free Electronic Research Notebook service for researchers, instructors, and students. This service is provided by LabArchives. LabArchives may be used on all CU campuses and is approved for PII, RHI, and PHI. It is registered in RSAM #5644.
• Code management and sharing
  • Best Practices for Writing Reproducible Code (Workshop from Utrecht University) | Workshop to help researchers not only to make your work reproducible but also to increase the efficiency of their workflow.
  • How to connect Figshare with your GitHub account
  • Issuing a persistent identifier for your GitHub repository with Zenodo
  • Connect GitHub to an OSF Project
  • TL;DR Legal - Software Licenses in Plain English
  • Choose an open-source license

• Practical tips combining animal welfare and experimental rigor to improve reproducibility in behavioral neuroscience (Morais Loss C et al., 2021)
• Guidelines on the Organization of Samples in a Laboratory | Tips on managing, identifying, and preserving research samples (non-clinical). Also available on Rascal Training - TC3250

• Columbia Consulting Services for Statistical Analysis | Services below are provided to Columbia researchers, ranging from no-cost to fee-for-service.
  • The Biostatistics, Epidemiology, and Research Design Resource (BERD) | Provides a wide range of design, statistical, and analytical support services to assist CUIMC faculty members in garnering grant support and publishing study results. In conjunction with the Department of Biostatistics of the Mailman School of Public Health, BERD provides support through consultations and educational initiatives.
  • Department of Statistics Consulting Services | The Department of Statistics offers free statistical consulting to the Columbia community.  Consulting is available by appointment only.
  • Columbia University Department of Psychiatry Biostatistics Consulting | Mental Health Data Science provides comprehensive statistical consulting to all Columbia University Department of Psychiatry and NYSPI employees.
• Courses and Lectures
  • Biostatistics for Clinical Researchers | Part of the “Biostatistics in Action: Tips for Clinical Researcher” lecture series that is sponsored by the Irving Institute for Clinical and Translational Research - Biostatistics, Epidemiology and Research Design resource, which is supported in part by an NIH Clinical and Translational Science Award (CTSA) through its Center for Advancing Translational Sciences (Grant No, UL1TR001873). The speaker, Cody Chiuzan, PhD, is an Assistant Professor in the Department of Biostatistics at the Mailman School of Public Health.
  • Statistical Software Mini-Courses | A two-part mini-course on getting started with statistical software. The mini-course covers the basics of statistical programming in R and SAS. Topics include data manipulation, descriptive statistics, and basic analyses. Statistical Software Mini-courses are offered once per year. Open to the Columbia community at no cost.
  • Johns Hopkins University Data Science Lab | The major educational initiative of the JHUDSL is to create open-source online courses delivered through a range of platforms, including YouTube, Github, Leanpub, and Coursera.

• Trainings | Research Data Services provides a range of services and programs, and host events and workshops to help researchers find, evaluate, understand, steward, and use data.
• The Digital Science Center provides a wide range of software to support research and coursework in several science and engineering disciplines. All of the software below is available for use on the computers located in the Science & Engineering Library.

• Research Tools and Solutions Supported by Columbia
  • LabArchives | Paperless research notebook and lab manual solution for Columbia's researchers.
  • GraphPad Prism Discount | Graphing and statistical software for creating publication-quality graphs and analyzing scientific data with t-tests, ANOVA, linear and nonlinear regression, survival analysis.
  • SnapGene | Molecular biology software for planning, visualizing, and documenting DNA cloning and PCR; allows feature annotation and primer design.
  • ChemDraw | A program to draw structures ChemDraw is the drawing tool of choice for chemists to create publication-ready, scientifically intelligent drawings – ChemDraw Activation Code.
  • NVIVO | NVivo is a software program used for qualitative and mixed-methods research. Specifically, it is used for the analysis of unstructured text, audio, video, and image data, including (but not limited to) interviews, focus groups, surveys, social media, and journal articles – activation code .
  • CrystalMaker is the most-efficient way to visualize crystal and molecular structures. Its interactive design lets you see the wood for the trees" and build your own visual understanding of complex materials – Crystalmaker License.
  • Schrodinger PyMol License Access | Molecular visualization system on an open-source foundation, maintained and distributed by Schrödinger – Schrodinger PyMol License Access.
• Security and Privacy
  • Globus | Secure, efficient and reliable file transfer service for large, non-sensitive data transfers within Columbia and to external collaborators.
  • dWinSCP | Secure FTP program, recommended by CUIT for file transfers to the cunix.cc.columbia.edu server.
  • CUSpider | Windows application for scanning for Personally Identifiable Information (PII) such as Social Security numbers
  • Malwarebytes | Virus and spyware scanning program.
  • Remote Access | Remote access to network files and administrative applications on the Columbia network via VPN and Citrix.
• Writing
  • Overleaf | Collaborative LaTeX editor for writing, editing and producing research papers and project reports (Overleaf Professional license).
  • Turnitin | Plagiarism Detection Services.

• 8 Types of Research Bias and How to Avoid Them? (appinio.com)
• Identifying and Avoiding Bias in Research (Pannucci CJ and Wilkins EG, 2011)
• The roles, challenges, and merits of the p-value (Chén OY et al., 2023)
• Moving beyond P values in The Journal of Physiology: A primer on the value of effect sizes and confidence intervals (Williams S et al., 2023)
• Blind analysis: Hide results to seek the truth (MacCoun R and Perlmutter S, 2015)
• The fickle P value generates irreproducible results (Halsey LG et al., 2015)

To mitigate the risk of disruption, it is recommended that all principal investigators develop research continuity plans for their laboratories and research teams.

• Columbia University Copyright Advisory Services can address issues surrounding the use of scholarly materials by faculty and students in the course of research, teaching, and communicating scholarship.
• Understanding What Plagiarism Is and How to Avoid It
• GSAS Resources for Plagiarism Education | Columbia's Graduate School of Arts and Sciences website on academic integrity has compiled several resources for plagiarism education.
• Indiana University has created comprehensive tutorials and an exam regarding plagiarism. The exam may be a useful risk assessment tool. ORI's 28 Guidelines at a Glance on Avoiding Plagiarism
• Knowing and Avoiding Plagiarism During Scientific Writing (Kumar PM et al., 2014)

Sometimes, plagiarism results from mismanaged or improper citation and source management.  Citation management software can help avoid such problems.

• Tutorials for using F1000 Workspace
• Tutorials for using EndNote
• Tutorials for using Mendeley
• Tutorials for using Zotero

• Questionnaire to determine the right reporting checklist for your work (EQUATOR library)
• Instant feedback for your manuscript (Penelope) - Checks academic manuscripts in Microsoft Word. It assesses structure, declarations, statistics, referencing, and other common reporting errors in seconds. 
• Principles and Guidelines for Reporting Preclinical Research (NIH)
• ARRIVE Guidelines (Animal Research: Reporting of In Vivo Experiments) The ARRIVE guidelines, originally published in PLOS Biology, were developed in consultation with the scientific community as part of an NC3Rs initiative to improve the standard of reporting of research using animals.
• Reporting Standards for Research in Psychology (APA)
• Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals (IMCJE)
• Research Reporting Guidelines and Initiatives: By Organization - This chart lists the major biomedical research reporting guidelines that provide advice for reporting research methods and findings (NIH - NLM)

• What Constitutes Authorship? A discussion document for authorship from COPE
• NIH Guidelines for Authorship

Predatory journals and publishers often operate under the auspices of open-access publishing. They charge authors fees without reviewing research for quality or providing editorial and publishing services. Below are questions and resources to help you determine if a journal is predatory.

• If the journal is open access, is it registered with the Directory of Open Access Journals?
• Does the journal list the names of its editorial and advisory boards?
• Are the journal's peer review and editorial policies openly available?
• Do you recognize the names of current contributors as scholars in your field?
• Do you recognize the journal's publisher? Is this information easy to find? Is that publisher a member of COPE (the Committee on Publication Ethics)?
  • Resources
    • Columbia University Libraries Scholarly Communication is available to help! Email questions to: [email protected]
    • Think. Check. Submit . Helps researchers identify trusted journals for their research. Through a range of tools and practical resources, this international cross-sector initiative aims to educate researchers, promote integrity, and build trust in credible research and publications.
  • Selected Literature
    • “Predatory” vs trustworthy journals: What do they mean for the integrity of science? by Sacha Boucherie
    • Academics and Scientists: Beware of Predatory Journal Publishers from Federal Trade Commission
    • Beware of Predatory Journals by Anders Rydholm
    • How I became Easy Prey to a Predatory Publisher by Alan H. Chambers

In February 2013, the White House Office of Science and Technology Policy (OTSP) issued a memo with the purpose of increasing access to federally funded research. This memo required any Federal agency that awards at least $100 million/year in support of research to develop a plan that would increase public access to publications and data resulting from federally-funded projects. In response to this memo, a number of public and private funders have established new requirements for researchers. 

Items to Consider in Data Sharing Plan*:

• What data will be shared?
• Who will have access?
• Where will shared data be located?
• When will data be shared?
• How will the data be located and accessed?

Additional information about the requirements for NIH and NSF are available here:

• NIH Public Access Policy
• NSF Public Access Policy

For more information please visit the Public Access Mandates and Resources page.

*Source: NIH Data Sharing Plan. Individual funders may have different requirements for data sharing plans.

Public Access Mandates and Resources

• AHRQ
  • Publication Access: PubMed
  • Data Access: AHRQ will coordinate upon receipt of funding
  • Documentation: Policy
• CDC
  • Publication Access: PubMed
  • Data Access: Repository of choice* (special considerations for sensitive data)
  • Documentation: Plan
• DoD
  • Publication Access: DTIC
  • Data Access: Repository of choice*
  • Documentation: Policy, Plan
• DOE
  • Publication Access: PAGES
  • Data Access: OpenEI
  • Documentation: Plan
• DOT
  • Publication Access: National Transportation Library (NTL)
  • Data Access: Data Repository conformant with DOT Public Access Policy
  • Documentation: DOT's What You Need to Know
• FDA
  • Publication Access: PubMed
  • Data Access: Discipline specific repository
  • Documentation: Plan, OpenFDA
• NASA
  • Publication Access: NASA PubSpace
  • Data Access: Repository of choice*
  • Documentation: Plan, NASA Public Access to Results
• NIH
  • Publication Access: PubMed
  • Data Access: Appropriate NIH repository (ex: NLM, NCBI, BMIC)
  • Documentation: Plan, Public Access Policy Data Sharing Policy, NIH Sharing Policies and Guidance, CU's Resource Page
  • Note: Institutes, centers, and offices may have different data sharing policies
• NIAAA
  • Publication Access: PubMed
  • Data Access: Human subjects data: NIAAA DA
  • Documentation: Data Sharing Policy: NOT-AA-19-020
• NIMH
  • Publication Access: PubMed
  • Data Access: NIMH NDA
  • Documentation: Data Sharing Policy: NOT-MH-19-033
• NIJ
  • Publication Access: NACJD
  • Data Access: Data Archiving Plans
  • Note: Not subject to OTSP memo
• NIST
  • Publication Access: PubMed
  • Data Access: NIST data through data.gov
  • Documentation: NIST Plan, NIST Public Access
• NOAA
  • Publication Access: NOAA Institutional Repository
  • Data Access: NOAA Data Catalog
  • Documentation: Plan, National Centers for Environmental Information
• NSF
  • Publication Access: NSF-PAR, Preparing for manuscript deposit
  • Data Access: Appropriate repository by research field as described in data management plan*, Dissemination and Sharing of Research Results FAQs
  • Documentation: Plan,  Public Access, Public Access FAQs
• ASPR (The Office of the Assistant Secretary for Preparedness and Response)
  • Publication Access: PubMed
  • Data Access: Repository of choice as described within data sharing plan*
  • Documentation: Plan
• USAID
  • Publication Access: Development Experience Clearinghouse (DEC)
  • Data Access: Development Data Library (DDL)
  • Documentation: Plan,  Data policy FAQ
• USDA
  • Publication Access: PubAg
  • Data Access: Repository of choice as described within data management plan*, Ag Data Commons Beta Site
  • Documentation: Plan
• USGS
  • Publication Access: USGS Publications Warehouse
  • Data Access: USGS accepted data repository
  • Documentation: Public Access, USGS Data Management resources
• VA
  • Publication Access: PubMed
  • Data Access: N/A
  • Documentation: Public Access
• Notes:
  • A list of possible data repositories is available through re3data.org
  • Instructions for uploading to PubMed are available

US Private Funders

• Alfred P. Sloan
  • Publication Access: "Information Products" to be disseminated as outlined by "IP Plan"
  • Data Access: "Information Products" to be disseminated as outlined by "IP Plan"
  • Documentation: Grant Proposal Guidelines
• Autism Speaks
  • Publication Access: PubMed
  • Data Access: Not specified
  • Documentation: Policy
• Ford Foundation
  • Publication Access: CC-BY license
  • Data Access: CC-BY license
  • Documentation: Terms and Condition of Use
• Bill and Melinda Gates Foundation
  • Publication Access: CC-BY license
  • Data Access: CC-BY license
  • Documentation: Policy
• Hewlett Foundation
  • Publication Access: CC-BY license
  • Data Access: CC-BY license
  • Documentation: Press Release, Guiding Principles
• Howard Hughes Medical Institute (HHMI)
  • Publication Access: PubMed
  • Data Access: Data supporting publications to be made available at no cost. Choose an appropriate discipline specific repository (if available).
  • Documentation: Policy, Publication Policy
• MacArthur Foundation
  • Publication Access: CC-BY license
  • Data Access: CC-BY license
  • Documentation: Policy
• Microsoft Research
  • Publication Access: Microsoft Research open-access repository
  • Data Access: Not specified
  • Documentation: Policy
• Gordon and Betty Moore Foundation
  • Publication Access: Prospective grantees to develop a Data Sharing and/or Intellectual Property Plan
  • Data Access: Prospective grantees to develop a Data Sharing and/or Intellectual Property Plan
  • Documentation: Policy
• World Bank
  • Publication Access: Open Knowledge Repository
  • Data Access: Not specified
  • Documentation: Policy
• Note: The above list of private funders is not comprehensive. Additional private funders' open access policies can be found on the Registry of Open Access Repository Mandates and Policies

US Directives

• White House Office of Science and Technology Policy (OTSP) Memo
• Blog post on Increasing Access to the Results of Federally Funded Science 
• Fair Access to Science and Technology Research Act of 2015 (pending legislation) 

Open access (OA) is the free and unrestricted availability of digital content online. It can apply to any type of content, including scholarly work, software, audio, video, and more. 

Features of open access

• Free: OA content is available at no cost to the reader. 
• Unrestricted: OA content has few restrictions on its use or reproduction. 
• Digital: OA content is available in a digital format. 
• Open licenses: OA content often uses open licenses, like Creative Commons licenses, which allow for more reuse and sharing. 

Benefits of open access

• Increased access: OA provides greater access to information for the general public, students, teachers, and libraries. 

• Increased visibility: OA increases the visibility of research outputs, which can lead to a greater impact. 

• Increased transparency: OA makes scientific research more transparent and accessible. 

Open Access Policies at Columbia

• Columbia University Libraries
• Lamont-Doherty Earth Observatory
• Mailman School of Public Health
• Columbia University School of Social Work

For more information contact Scholarly Communication & Publishing.

There are a number of ways to maintain and share your data in order to make it available to the scholarly community and the broader public. Check out the links and resources below to find more information about managing and sharing data, and to find a repository that is right for you and your research.

• CU-Supported Data Repositories
  • List of options for the storage, sharing and transfer of digital research data that is available to Columbia researchers. This table is maintained by the ReaDI Program. For more detail regarding the resources listed in the table below, please download the research data storage options PDF. All systems located at Columbia University’s Morningside Heights or Manhattanville Campus that process, transmit and/or store Sensitive Data must be registered with the CU Information Security Office. All Systems located at CUMC (“CUMC Systems”) must be registered with the CUMC Information Security Office. See Data Security webpage for more information.
  • See RSAM User Guide for Registering your Device.
• Repositories for Sharing Scientific Data (NIH)
  • Browse through this listing of NIH-supported repositories to learn more about some places to share scientific data. Select the link provided in the “Data Submission Policy” column to find data submission instructions for each repository.
  • Learn more on how to evaluate and select appropriate data repositories.
• Open Domain-Specific Data Sharing Repositories (BioMedical Informatics Coordinating Committee - BMIC)
  • Domain-specific repositories are typically limited to data of a certain type or related to a certain discipline.
• Generalist Repositories (BioMedical Informatics Coordinating Committee - BMIC)
  • Generalist repositories accept data regardless of data type, format, content, or disciplinary focus.
  • We are currently recommending researchers to use Dryad, which is freely available to all CU researchers. You may also want to refer to the CU Data Repository Finder for other repositories that meet the NIH’s suggested requirements.

Data and Sample Repositories

• Repository Finder Decision Tree: A pilot project of the Enabling FAIR Data Project led by the American Geophysical Union (AGU) in partnership with DataCite and the Earth, space and environment sciences community, can help you find an appropriate repository to deposit your research data. 
• EarthChem: A suite of data systems that assist geoscientists with accessing, sharing, and using geochemical, petrological, and geochronological data. 
• Geochron: A database system designed to capture complete data and metadata to document geochronologic age estimation, allowing future reuse, recalculation, and integration with other data.
• Marine Geology and Geophysics: The Marine Geoscience Data System (MGDS) provides a suite of tools and services for free public access to marine geoscience research data acquired throughout the global oceans and adjoining continental margins.
• SESAR: A centralized registry that provides and administers unique identifiers for geoscience samples

Tutorials

• Sample Management: Training Module for Rock Outcrop Samples: Presented as a three part training module for student researchers to learn sample collection, processing, and management protocol including proper sample documentation, registration, and tracking in a small academic department collaborative research setting.
• Sample Management: Training Module for Soil Cores: This module describes the steps for collecting, processing and managing soil cores sampled manually by augering. The purpose is to provide a protocol for collecting, processing and managing this type of physical samples to facilitate future discovery and use, including assigning International Geosample Numbers (IGSN).
• Digital Sample Management for SESAR: Tutorials available from System for Earth Sample Registration (SESAR)

Data Management

• Data Management Training Clearinghouse: A registry for online learning resources about research data management.  It was created in a collaboration between the U.S. Geological Survey's Community for Data Integration, the Earth Sciences Information Partnership (ESIP), and DataONE.

Resources from Special Interest Groups and Communities

• COPDESS: The Coalition for Publishing Data in the Earth and Space Sciences connects Earth and space science publishers and data facilities to help translate the aspirations of open, available, and useful data from policy into practice.
• DataONE: A community driven project providing access to data across multiple member repositories, supporting enhanced search and discovery of Earth and environmental data. DataONE promotes best practices in data management through responsive educational resources and materials.
• EarthCube: initiated by NSF in 2011 to transform geoscience research by developing cyberinfrastructure to improve access, sharing, visualization, and analysis of all forms of geosciences data and related resources.
• ESIP: Earth Science Information Partners (ESIP) is a 501(c)(3) nonprofit, volunteer and community-driven organization that advances the use of Earth science data.
• IEDA: IEDA systems serve as primary community data collections for global geochemistry and marine geoscience research and support the preservation, discovery, retrieval, and analysis of a wide range of observational field and analytical data types.
• USGS: Community for Data Integration: is a dynamic community of practice working together to grow USGS knowledge and capacity in scientific data and information management and integration.

Functional MRI

• The Secret Lives of Experiments: Methods Reporting in the fMRI Literature by Joshua Carp
• Fostering Reproducible fMRI Research (Nature Neuroscience)
• Reproducibility of fMRI in the Clinical Setting: Implications for Trial Designs by Rose Bosnell et. al.

Mixed Methods and Qualitative Research

• Analysing Qualitative Data by Catherine Pope, Sue Ziebland, and Nicholas Mays
• Assessing Quality in Qualitative Research by Nicholas Mays and Catherine Pope
• Introductory Social and Behavioral Science Training Material from OBSSR (NIH)
• Mixed Methods in Biomedical and Health Services Research by Dr. Leslie A. Curry, et. al.
• Qualitative and Mixed Methods Provide Unique Contributions to Outcomes Research by Dr. Leslie A. Curry, Dr. Ingrid M. Nembhard, Dr. Elizabeth H. Bradley
• Using Qualitative Methods in Health Related Action Research by Julienne Meyer

Patient-Centered Outcomes Research and Observational Studies

• Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide Edited by Priscilla Velentgas, Nancy A. Dreyer, Parivash Nourjah, Scott R. Smith, and Marion M. Torchia
• Systematic Review and Evidence Integration for Literature-Based Environmental Health Science Assessments by Andrew A. Rooney, Abee L. Boyles, Mary S. Wolfe, John R. Bucher, and Kristina A. Thayar
• Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data by Martijn J. Schuemie, George Hripcsak, Patrick B. Ryan, David Madigan, and Marc A. Suchard

Clinical Trial Design Learning Resources

• Review of Recent Methodological Developments in Group-Randomized Trials: Part 1-Design by Elizabeth L. Turner, Fan Li, John A. Gallis, Melanie Prague and David M. Murray
• Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis by Elizabeth L. Turner, Melanie Prague, John A. Gallis, Fan Li, and David M. Murray
• Clinical Study Design Checklist from SPIRIT
• Improved Designs for Cluster Randomized Trials by Catherine M. Crespi
• Reporting and methodological quality of sample size calculations in cluster randomized trials could be improved: a review by Clare Rutterford, Monica Taljaard, Stephanie Dixon, Andrew Copas, and Sandra Eldridge
• Are missing data adequately handled in cluster randomised trials? A systematic review and guidelines by Karla Díaz-Ordaz, Michael G Kenward, Abie Cohen, Claire L Coleman, and Sandra Eldridge
• Sample size calculations for the design of cluster randomized trials: A summary of methodology by Fei Gao, Arul Earnest, David B. Matchar, Michael J. Campbell, David Machin
• GRT Sample Size Calculator
• IRGT Sample Size Calculator
• Online Courses
  • Introduction to the Principles and Practice of Clinical Research (IPPCR). The NIH Clinical Center's Introduction to the Principles and Practice of Clinical Research (IPPCR) course trains registrants on how to effectively and safely conduct clinical research. The course focuses on the spectrum of clinical research and the research process by highlighting biostatistical and epidemiologic methods, study design, protocol preparation, patient monitoring, quality assurance, ethical and legal issues, and much more.
  • Principles of Clinical Pharmacology. This course is an online lecture series covering the fundamentals of clinical pharmacology as a translational scientific discipline focused on rational drug development and utilization in therapeutics. The course focuses on the following core principles of pharmacology: pharmacokinetics; drug metabolism and transport; drug therapy in special populations; assessment of drug effects; drug discovery and development; pharmacogenomics and pharmacotherapy.
  • Other clinical training opportunities are offered by NIH's Office of Clinical Research

Clinical Trial Protocol Development

• NIH e-protocol writing tool: The electronic protocol writing tool aims to facilitate the development of Phase 2 and 3 IND/IDE Clinical Trial Protocol Template as well as the Behavioral and Social Sciences Research Involving Humans. The tool has been developed through the National Institutes of Health (NIH) Office of Science Policy.
• SPIRIT Group resources: an international group of stakeholders with the initiative to improve the completeness and quality of trials protocols. (text adapated from website)
  • SPIRIT 2013 Statement: Defining Standard Protocol Items for Clinical Trials
  • SPIRIT 2013 Explanation and Elaboration: Guidance for Protocols of Clinical Trials
  • Guidelines for Inclusion of Patient-Reported Outcomes in Clinical Trial Protocols: The SPIRIT-PRO Extension
  • Strengthening the Credibility of Clinical Research
  • Related publications and resources from SPIRIT

Reporting Guidelines

• Questionnaire to determine the right reporting checklist for your work from the EQUATOR library
• Instant feedback for your manuscript from Penelope- checks academic manuscripts written in Microsoft Word. In seconds, it assesses structure, declarations, statistics, referencing and other common reporting errors. 
• Research Reporting Guidelines and Initiatives by Organization - This chart lists the major biomedical research reporting guidelines that provide advice for reporting research methods and findings (from NIH - NLM)

Retrospective Chart Review

• Compilation of suggested practices for creation of a retrospective chart review form
• The Retrospective Chart Review: Important Methodological Considerations by Vasser Matt and Holzmann Matthew

Scientific Integrity

• A Multidisciplinary Approach to Ensure Scientific Integrity in Clinical Research by Dr. Ko Bando, et. al

Simulation-Based Research

• The INSPIRE network has collaborated with global partners (including four influential journals: Simulation in Healthcare, BMJ Simulation, Clinical Simulation in Nursing, and Advances in Simulation) to develop extensions specific to simulation-based research for both the CONSORT and STROBE statements.

Statistics

• Statistics in Medicine publishes statistical tutorials  
• The Biostatistics in Action Lectures YouTube Channel 
• Strategies for Integration of Gender and Sex in Research
  • Essential metrics for assessing sex & gender integration in health research proposals involving human participants by Suzanne Day, et. al. 
  • Sex and Gender Equity in Research: rationale for the SAGER guidelines and recommended use by Shirin Heidar 

Systematic Reviews

• Covidence - The cloud-based software supports import and de-duplication of citations, title, abstract and full-text screening, risk-of-bias assessment and data extraction. Columbia researchers can sign in for free with their UNI (provided by Augustus C. Long Health Sciences Library)
• How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-Analyses, and Meta-Syntheses by Siddaway AP, Wood AM, and Hedges LV
• Clarifying differences between review designs and methods by David Gough, James Thomas & Sandy Oliver 
• What is a Systematic Review? from Curtin University

For researchers, preserving the credibility of science now demands more than simply disclosing when AI tools have been used; it calls for a thoughtful reckoning with how collaborating with AI shifts questions of responsibility, oversight, and scholarly norms. To help navigate these challenges, we highlight five key principles — drawn from Jamieson, Kearney, and Mazza (2024) — for mitigating the risks of scientific misconduct when using generative AI.

1. Transparent Disclosure and Attribution: Scientists should clearly disclose the use of generative AI in research, including the specific tools, algorithms, and settings employed. Human and AI contributions must be accurately distinguished, and prior literature should be properly cited even when AI omits those citations. Model creators should publish details about their models and training data and maintain long-term archives to enable replication. (See: McNutt et al., "Transparency in Authors' Contributions and Responsibilities to Promote Integrity in Scientific Publication," PNAS, 2018)

2. Verification of AI-Generated Content and Analyses: Scientists are accountable for the accuracy of data, imagery, and inferences drawn from generative models. This requires using appropriate methods to validate AI-assisted findings, disclosing supporting evidence, and monitoring for biases in AI output that could skew research outcomes. Model creators should disclose limitations and provide well-calibrated confidence assessments. (See: Fostering Responsible Computing Research: Foundations and Practices, NASEM, 2022)

3. Documentation of AI-Generated Data: All AI-generated or synthetic data, inferences, and imagery must be marked with provenance information so they are not mistaken for real-world observations. Model creators should annotate synthetic data used in training and monitor issues arising from the reuse of computer-generated content in future models. (See: Reproducibility and Replicability in Science, NASEM, 2019)

4. A Focus on Ethics and Equity: AI use should produce scientifically sound and socially beneficial results while mitigating risk of harm. Scientists and model creators should adhere to ethical guidelines around attribution, intellectual property, privacy, and consent, and promote equitable access to AI tools — particularly for underserved communities. AI should not be used without careful human oversight in peer review or funding decisions. (See: London, "A Justice-Led Approach to AI Innovation," Issues in Science and Technology, 2024; Parthasarathy & Katzman, "Bringing Communities In, Achieving AI for All," Issues in Science and Technology, 2024)

5. Continuous Monitoring, Oversight, and Public Engagement: Scientists, together with academia, industry, government, and civil society, should continuously evaluate AI's impact on the scientific process and adapt strategies as technologies evolve. Research communities must anticipate harmful uses, harness AI's societal potential, and solicit meaningful public participation in governance. (See: Gasser, "Governing AI with Intelligence," Issues in Science and Technology, 2024; Aidinoff & Kaiser, "Novel Technologies and the Choices We Make," Issues in Science and Technology, 2024)

Additional Resources:

• Original PNAS Editorial: Blau et al., "Protecting Scientific Integrity in an Age of Generative AI" (2024)
• Fostering Integrity in Research, NASEM (2017)
• Automated Research Workflows for Accelerated Discovery, NASEM (2022)
• Gray, "A Human Rights Framework for AI Research Worthy of Public Trust," Issues in Science and Technology (2024)
• Blueprint for an AI Bill of Rights, White House OSTP (2022)

Explore a growing Columbia collection of resources on the responsible use of generative AI.

• Teaching and Learning in the Age of AI: Thinking about the role of AI in your courses? Explore the following pedagogical resources and join us for workshops and events for strategies and perspectives on teaching and learning with generative AI.
• AI Community of Practice: The community is a platform for learning, discussion, and application of AI principles across various fields of study at Columbia University. We aim to demystify AI, spur innovation, and approach challenges with a fresh, AI-centric perspective through regular meetings, workshops, and collaborative projects. To learn about joining, send in your interest intake form.
• AI Services: CUIT is developing a suite of AI services designed to open new modes of discovery in interdisciplinary research and to enhance productivity. The services in development include advanced audio transcription, text anonymization, and automated text mining. The aim of this initiative is to make cutting-edge advances in AI and LLMs as accessible as possible to the community at Columbia.