Glossary

Terms and abbreviations relevant to this project

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Chapter 1: 3R principles

Guiding principles of animal experimentation

Replacement

Methods that achieve a purpose without conducting experiments or procedures on animals

Reduction

Methods that obtain comparable information using fewer animals or maximize data collection from the same number of animals.

Refinement

Methods that minimize pain, suffering, and distress while enhancing animal well-being.

Chapter 2: Reproducibility

Definitions from National Academy of Science

Reproducibility or computational reproducibility

Obtaining consistent computational results using the same input data, computational steps, methods, code, and conditions of analysis.

Replicability

Obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data.

Generalizability

The extent that results of a study apply in other contexts or populations that differ from the original one.

Chapter 3: Open Research Data

Research data

The actual records (such as numerical scores, textual records, images, and sounds) resulting from research that is partially or fully funded by public funds, used as primary sources for scientific research, and that are commonly accepted in the scientific community as necessary to validate research findings. This term does not cover laboratory notebooks, preliminary analyses, or drafts of scientific papers, plans for future research, peer reviews, personal communications with colleagues, or physical objects, (e.g., laboratory samples, strains of bacteria, or test animals).

Research Data Management (RDM)

The handling of research data (collection, organisation, storage, and documentation) during and after a research activity.

Open Research Data (ORD)

Data that can be freely accessed, reused, remixed and redistributed, for academic research and teaching purposes and beyond. Ideally, open data have no restrictions on reuse or redistribution, and are appropriately licensed as such.

Chapter 4: Preregistration

Preregistration

A publication of research plan (hypotheses, methods, analyses) before an study is conducted.

Registered reports

A publication involving a two-stage peer review process where a study proposal is reviewed before data collection and the final manuscript is reviewed after analysis. At stage 1, researchers can obtain in-principle acceptance, so even if no statistically significant results are found in the planned analyses, the paper would be accepted for publication.

Peer community in registered reports (PCI-RR)

The PCI initiative is a researcher-run initiative that publishes peer-reviews of preprints and the PCI-RR community is dedicated to receiving, reviewing and recommending registered reports across disciplines.

Chapter 5: The FAIR principles

Guiding principles for guiding the management of digital resources, specifically scientific data and metadata.

Findable

It should be possible for others to discover your data. Rich metadata should be available online in a searchable resource, and the data should be assigned a persistent identifier.

Accessible

It should be possible for humans and machines to gain access to your data, under specific conditions or restrictions where appropriate. FAIR does not mean that data need to be open! There should be metadata, even if the data are not accessible.

Interoperable

Data and metadata should conform to recognized formats and standards to allow them to be combined and exchanged.

Reusable

Lots of documentation is needed to support data interpretation and reuse. The data should conform to community norms and be clearly licensed so others know what kinds of reuse are permitted.

Chapter 6: Data Management Plan

Data Management Plan (DMP)

A document required by SNSF grants that outlines a plan of the life cycle of the data. It includes data handling practices, ethical and legal issues, storage, and sharing plans. DMPs remain editable during the entire lifetime of the grant.

Chapter 8: Standards

Metadata and data organization standards

Community-specific descriptions of how to structure data, metadata, filenaming conventions and file formats.

Chapter 9: Documentation, protocols and reporting

Electronic lab notebooks (ELNs)

Software system designed to help researchers organize and store experimental procedures, protocols, plans, notes and data. ELNs are a digital version of the traditional paper notebooks most researchers kept in the lab during data collection. Ideally, a ELN will use open formats and enable version control.

Good Laboratory Practices (GLP)

Guidelines with the aim to ensure quality of the process and conditions under which studies are planned, performed, monitored, recorded, archived and reported.

README files

These are plain text files usually in .txt or Markdown (.md) format. They are intended mostly for humans rather than for machine-readability. README files typically describe the important information, written in a concise manner, to help understanding the content of a folder.

Reporting guideliens

Guidelines with recommendations and checklists to make reporting more complete and transparent, maximizing quality and reliability of published research.

Standard operating procedures (SOPs)

Detailed instructions or step-by-step guides describing how to conduct specific research, for example how to conduct an experiment or collect data. Their aim is to make specific research practices and settings consistent.

Chapter 10: Tables and spreadsheets

Comma-separated values (CSV)

A text file format using commas to separate values and newlines(`\r\n in Windows and \n in Mac and Linux ) to separate rows or records. It is a format preferred for interoperability over spreadsheet file formats like .xls. An alternative is tab-separated values (TSV) files.

Codebook

In the context of tables and spreadsheets, a codebook is a table or text file describing the variable names from a table or spreadsheet, and their meanings.

Chapter 11: Metadata companion files

Metadata

Metadata refers to information about digital objects, in this case, about research data. It is as important as the data, as without metadata we cannot find, interpret or use the data. Metadata can be structured, machine-readable files (e.g., .JSON or .XML) or tables (e.g., .csv) or unstructured text (e.g., README files) and documentation aimed at human readers.

Metadata companion files

Are structured files with metadata that accompany data files, providing with machine-readable information about the data files. They are also call sometimes ‘sidecar’ files.

JavaScript Object Notation (JSON)

An open and text-based standard format for data interchange. It is often recommended as a format for metadata files because it is easy to read by humans and by many programming languages. JSON is also a good format for larger (metadata) data that have a hierarchical structured relationship. The JSON structure has name/value pairs separated by commas.

Chapter 12: Controlled volcabularies and ontologies

Controlled vocabulary

standardized and organized arrangement of words and phrases presented as alphabetical lists of terms or as thesauri and taxonomies with a hierarchical structure of broader and narrower terms.

Ontology

An ontology is a formal way of representing knowledge about a specific domain, using a structured framework of concepts and their relationships. A hierarchical graph covering a specific subject area or domain.

Taxonomy

a controlled vocabulary in which all the terms belong to a single hierarchical structure and have parent/child or broader/narrower relationships to other terms. The structure is sometimes referred to as a tree.

Chapter 13: File formats

Interoperability

Interoperability refers to how well data (and metadata) can be shared and reused across systems, disciplines and organization. Open formats have better interoperability and are less prone to obsolescence.

Chapter 14: Version Control

Git

Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. Git allows to tracks change in files, including timestamps and to revert changes. It is the de facto standard for version control in code development.

Version control

Version control refers to the record of changes made in a file or set of files over time. The record should allow collaborators to track history , review and revert changes.

Versioning

The management of changes in a file or project

Chapter 15: Archives and online repositories

Archiving

Data archiving usually refers to long-term storage and preservation of the data.

Cold data archive

Refers to archiving data not expected to be accessed in a long time but that can still be considered important and should still be curated, unlike data considered disposable. Cold data tends to involve larger storage volume than warm and hot data, as it includes all data, including that of limited utility. Tapes are relatively cheap and robust to data quality deterioration if stored in facilities that ensure data integrity preservation.

Warm data archive

Refers to archiving data expected to be accessed regularly, long-term archived in online servers or cloud repositories. Usually there are multiple back-up copies in physically separated locations to protect data integrity. Warm data are considered of high-utility for research reuse.

Hot data archive or storage

Refers to storage of data that are accessed constantly for a specific period of time, for example, while a project is running to share it with collaborators. Hot data are stored in relatively expensive online servers or cloud repositories, and are more likely to move location than cold and warm data.

Repository

A technical infrastructure that stores data, ensures data integrity and makes them accessible in the long-term with appropriate services to manage content and metadata and access control.