Data Policy Guidelines

1. Data Policy

Machines and Algorithms is committed to promoting transparency, reproducibility, and trust in scientific research. This Data Policy outlines the expectations for data availability, sharing, and citation in all submitted manuscripts.

A. Data Availability Requirement:

  • All authors must include a Data Availability Statement (DAS) in their manuscript describing:
    • Whether and where the data are publicly available
    • Any restrictions on data access
    • How readers can obtain the data if not publicly available

Examples of DAS:

  • "The datasets generated during and/or analyzed during the current study are available in the [name] repository, [DOI/link]."
  • "Data are available from the corresponding author on reasonable request due to [reason for restriction]."
  • "No new data were created or analyzed in this study."

B. Data Sharing Guidelines:

  • Deposit datasets in trusted, publicly accessible repositories (e.g., Zenodo, Figshare, Dryad, GitHub, OSF).
  • Assign persistent identifiers (DOI).
  • Share code/scripts used in analysis under an open-source license if possible.

C. Exceptions and Restrictions:

  • Data involving human subjects, privacy concerns, or confidentiality may be exempted but must be justified in DAS.
  • Authors must still provide metadata or summary descriptions and terms for possible access.

D. Data Citation:

  • All datasets must be formally cited in references, including author(s), year, title, repository, DOI/stable link.

E. Editorial and Peer Review Oversight:

  • Editors/reviewers may request access to underlying data.
  • Failure to comply without justification may lead to rejection or retraction.

F. Post-Publication Corrections:

  • If issues arise, the journal may issue a Correction, Expression of Concern, or Retraction.

G. Licensing and Ownership:

  • Authors must ensure data rights and avoid infringement.
  • Shared data should preferably be under Creative Commons licenses (e.g., CC BY, CC0).

H. Compliance:

  • Non-compliance may result in rejection, delays, or editorial sanctions.

2. Image and Data Manipulation Policy

All forms of image and data manipulation must be transparently disclosed and justified. Machines and Algorithms prohibits manipulations that mislead readers or distort data integrity.

A. Acceptable Manipulation:

  • Adjusting brightness/contrast uniformly
  • Cropping images for clarity
  • Data smoothing/normalization (with explanation)
  • Compression for formatting

B. Unacceptable Manipulation: Any falsification, fabrication, or distortion of original data.

C. Author Responsibility:

  • Declare image/data processing methods in Methods or Figure Legends
  • Retain original unprocessed data
  • Ensure manipulations do not alter scientific meaning

D. Editorial and Peer Review:

  • Editors may request original data for verification
  • Undisclosed/inappropriate manipulation may result in rejection or retraction

E. Post-Publication Action:

  • Corrections, Expressions of Concern, or Retractions may be issued
  • Authors may be reported to institutions for investigation

F. Best Practice: Follow ethical use of editing tools (e.g., Photoshop, ImageJ). Consider submitting raw data as supplementary files for transparency.