Submission Guidelines

This "Instruction for Authors" describes the general information about Data Intelligence including aims and scope, main areas, and specific topics Data Intelligence shall cover, and how to prepare a manuscript for submission. We highly recommend you read this in full and have your contribution consistent with Data Intelligence publication guidelines, including format and style.

Aims & Scope

Data Intelligence, co-sponsored by the National Science Library, Chinese Academy of Sciences and China National Publications Import & Export (Group) Corporation, is a peer-reviewed metadata centric academic journal that is targeted at data creators, data curators, data stewards, data policy makers, domain scientists and others interested in sharing data. The point of the publication is to include, but not limited to, articles discussing methodologies and/or data resources. The aim is to provide a vehicle to assist industry leaders, researchers and scientists in the sharing and reuse each other's data, metadata, knowledge bases, and data visualization tools. The journal will publish not only traditional articles, but also "data articles" with the contents in the form of knowledge graphs, ontologies, linked datasets and metadata resources. Data Intelligence aspires to introduce developing and emerging data-enabled technologies that will allow and facilitate the work of scientists to more deeply understand and extend the potential of their data. The journal maintains an academic center, a key educational channel, to offer intelligent data services and support for both machine and human to read and reuse data.

The objectives of this journal are:

• Publishing papers specifically aimed at technologies and methodologies for data sharing, curation, etc.
• Publishing papers that describe specific data- or metadata- repositories that are being maintained and shared.
• Encouraging data sharing by systematically annotating data resources based on widely-adopted metadata standards.
• Collecting and cataloguing various knowledge bases such as knowledge graph, ontology, linked dataset and corpus, etc. and publishing information about these.
• Enabling automatic data annotation and semantification, and linking from newly imported data to the Data Intelligence repository.
• Providing added value (in the form of data links, synthesized analytics) to articles and data shared in the Data Intelligence repository.
• Promoting scientific activities that focus on creating new datasets.
• Giving explicit credit to data creators and disseminating their contributions both in the journal and in wider social media application.
• Facilitating connecting-dots to build and share real-time knowledge.

The final goal of this journal is to build a research culture that is creating new datasets which are scientifically reward-worthy and sharing data is necessary to ensure the transparency and reproducibility in science.

Peer Review Statement

Manuscript Handling Process

1. The Managing Editor (ME) will review all submissions. This initial check by the ME will evaluate if a submission fits well within the journal’s scope and how well the submission is organized. The ME can either assign the submission to an Associate Editor (AE) or recommend it be sent to at least two reviewers, and an Editor-in-Chief (EiC) will be informed of this at the same time. This requires 1 or 2 days.
2. A basic quality check by an AE: The AE can reject the submission directly based on their own assessments or further solicit at least two reviewers to conduct a peer review. This will take 1 week. The decision of rejection made by the AE will be submitted to an EiC, who will make the final decision about whether the submission is directly rejected or not.
3. We use a single-blind peer review process where reviewers know the identity of the author. The Peer review is conducted by external reviewers: Finding reviewers usually takes 2 or 3 days. Normally we request that comments are returned within 4 weeks and no later than 5 weeks. During the 4 to 5 weeks of the peer review process, reviewers will receive several reminders (once a week) sent by the ME.
4. First revision requested by an AE: The AE will determine if “Minor revision” or “Major revision” is required and if the article will be “Rejected” based on the reviewers’ comments and their evaluation of the submission. This takes 3 or 4 days.
5. Revision by authors: Minor revision of manuscripts must be resubmitted within 3 weeks and Major revision of manuscripts within 5 weeks.
6. Second review and second-round decision by an AE: The AE decides whether the revised submission needs to be sent back to the original reviewers for further review or not. This process usually takes, for Minor-revision submissions, 3 days, and for Major-revision submissions, 3 days to 2 weeks (10 days for further review by reviewers).
7. Final decision by EiCs: The ME will submit the AE-provisionally-accepted manuscript to an EiC for final decision. The EiC is expected to complete this within 3 days.
8. Pub-Express: Timely publishing is crucial to scholarly communications. As such, Data Intelligence is serving a Pub-Express channel, by which a manuscript can receive a direct final decision by an EiC within 7 days upon being submitted. Authors can self-recommend their manuscripts to Pub-Express. So can either an EiC or AE.
9. Special issue organization: For manuscripts submitted to a special issue, the Issue Editors (Guest Editors) will make final decisions on behalf of the EiCs of the journal.
10. For more information please see the Data Intelligence Manuscript Handling Process workflow.

Types of Articles

Data Intelligence primarily publishes the following different kinds of full-length articles:

Data Articles

Data articles which describe an ontology, a knowledge graph, a vocabulary or thesaurus, a linked data set or a cluster of interoperable data sets and corresponding services, evaluation benchmarks or methods, APIs and software frameworks, workflows, crowdsourcing task designs, protocols and metrics. The contents should include the background of the work performed, the representation of standards used, information on how the datasets and services were built, descriptions of reliability, versioning, up time and sustainability and the application implications, disruptiveness and limitations as well. A full version of the data is encouraged to be stored in a sustainable, FAIR compliant repository or at minimum is to be linked to a journal or a third-party data repository to facilitate extended value through sharing, disseminating and reusing in other papers and applications as public domain resources.

Essential sections: Introduction, Value of the data, Acquirements of the data, Application and Limitation of the Data.

Along with essential sections, articles must include general background of the data acquirements and application, a brief summary of related research (literature review), theories and methodologies that contribute to the author's approach, specific methods on how the data was acquired, major value and significance, possible application and limitation data collection. The following types of articles fall under the "data articles" category:

• A KOS paper including an ontology, a specific metadata and its standards, which describes thoroughly a KOS
• A linked dataset descriptor
• A corpus descriptor

Perspective or Commentary Articles

Perspective or commentary articles which express new perspectives including outlook, challenge, and opportunities on a specific topic in the authors' area of expertise of high interest to the Data Intelligence community/audience.

Note: The Perspectives to be published by Data Intelligence are at the invitation of the Co-Editors-in-Chief and other Editorial staff. Unsolicited Perspectives will not be considered.

Research Articles

Research articles which present state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the topics on data generation, data analysis, data integration, data sharing, data management and related topics in the field of Data Intelligence.

Data Application Articles

Data application articles which report specific domain or cross domain applications based on data resources, repositories and data-enabled technologies.

Essential sections: Introduction, Methodology, Results, Discussion and Conclusion

Application articles consist of two major types:

1. Technology application Essential elements: context of application, system environment, key points, implementation challenges or problems during application and how to solve them, operational framework, application effects and experiences, etc.
2. Technology application: Essential elements: context of service, service environment, key points and implementation challenges and problems during service application and how to solve them, service implementation architecture, service effects and related experiences, etc.

Letters to the Editor

Letters to the Editor (LTE) which are rapid communications to publish short articles with a high degree of novelty.

Basic Format and Style

• LaTeX Template
• Word Template

Author Contribution Statement

Everyone listed as an author should meet our criteria for authorship. Everyone who meets our criteria for authorship must be listed as an author. The contributions of all authors must be described. Author lists should accurately reflect these contributions. We expect that all authors will take public responsibility for the manuscript content submitted to Data Intelligence.

All authors will be contacted by email at submission to ensure that they are aware of and approve the submission of the manuscript, its content, and its authorship. All authors must see the final draft of the manuscript before it is published. Those who do not meet the criteria for authorship should be mentioned in the Acknowledgments.

Those who performed the following work are named as "Author/s," including but not limited to those who:

• Proposed the research problems
• Performed the research
• Designed the research framework
• Collected and analyzed the data
• Wrote and revised the manuscript

Those who participated in discussion or just offered language, editing, or related help should not be included in the author list, but may be mentioned in the Acknowledgments.

References and Citations

All available sources cited in the text, tables or figures must be listed in the reference list. Unavailable and unpublished work, including manuscripts that have been submitted but not yet accepted and personal communications should not appear in the reference list but should be cited in the text only. Instead, those data should be included as supplementary material or deposited in a publicly available database. Please note: Authors are responsible for the accuracy of the references. Make sure the parts of the manuscript are in the correct order before ordering the citations. Do not include citations in abstracts.

References should be numbered sequentially in the order in which they appear and denoted in the text through numbers. In references the full title of the paper should be given along with the first and last page numbers. Personal communications and unpublished works should not appear in the reference list but should be cited in the text only.

Make sure format the references properly so as to be linked electronically. Example formats are listed below.

Books

Book with author(s)
Mons, B.: Data stewardship for open science: Implementing FAIR principles. CRC Press, Boca Raton (2018)

Book with editor(s)
Ding, Y., Rousseau, R., Wolfram, D. (eds.): Measuring scholarly impact: Methods and practice. Springer, Berlin (2014)

Chapter in a book
Prensky, M.: Computer games and learning: Digital game-based learning. In: Raessens, J., Goldstein, J. (eds.) Handbook of Computer Games Studies, pp. 97-122. MIT Press, Cambridge (2005)

Journal papers

Al-Jadir, L., Parent, C., Spaccapietra, S.: Reasoning with large ontologies stored in relational databases: The Onto-MinD approach. Data & Knowledge Engineering 69(11), 1158–1180 (2010)

Mons, B., et al.: Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services & Use 37(1), 49-56 (2017)

Online, advance publication

Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

Conference papers

Tang, J., et al.: ArnetMiner: Extraction and mining of academic social networks. In: Proceedings KDD, pp. 990–998 (2008)

Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 328–339 (2018)

Bloehdorn, S., Sure, Y.: Kernel methods for mining instance data in ontologies. In: Aberer, K., et al. (eds.) ASWC/ISWC-2007. LNCS, vol. 4825, pp. 58–71. Springer, Heidelberg (2007)

Theses and dissertations

Rijgersberg, H.: Semantic support for quantitative research. PhD dissertation, Vrije Universiteit van Amsterdam (2013). Available at: http://dare.ubvu.vu.nl/handle/1871/40428. Accessed 2 May 2020

Electronic sources

Morin, A., Urban, J., Sliz, P.: A quick guide to software licensing for the scientist-programmer. PLoS Computational Biology 8(7), e1002598 (2012). https://doi.org/10.1371/journal.pcbi.1002598

The European Commission High Level Expert Group report: Realizing the European Open Science Cloud. Available at: https://ec.europa.eu/research/openscience/index.cfm?pg=open-science-cloud-hleg (2017). Accessed 22 May 2020

Data Policies

As a journal committed to exploring an innovative publishing mode both for human beings and machines, Data Intelligence makes every possible way to foster data sharing culture and implement the FAIR (finable, accessible, interoperable and reusable) Principles. As such, Data Intelligence provide hosting data and long-term preservation services for our authors for free. We highly recommend authors to submit their data to the journal’s repository when publishing their articles with Data Intelligence.

Data articles are one of Data Intelligence’s main article types, which describe academically valuable data resources. Besides the common-sense data resources, Data Intelligence focuses those created, produced, and used by AI specialists, such as knowledge graphs, linked datasets, domain metadata, vocabularies, ontologies and annotated corpus etc. These resources must be made available to editors and referees at the time of submission, and are strongly encouraged to be shared with the academic community and the public. The metadata must be openly and freely available without restrictions if the data resource cannot be for reasonable and legal reasons. Here, we provide detailed information on data policies of the journal.

Data Availability

We require authors to share the data that described in data articles through depositing the data in the journal’s repository wherever possible at www.scidb.cn/surl/di. Any restrictions on the data availability should be disclosed to the journal at the time of submission.

The data should be made available to the journal and peer-reviewers at the time of submission for the purposes of evaluating the manuscript. Peer-reviewers will be asked to comment on the terms of access to the data sets.

Data Availability Statement

From the issue three of 2021, all data articles should include a data availability statement. This should, wherever possible, include a link to the repository authors have used, and citation of any data sets analyzed or generated in the study, when these are available in an appropriate public repository. Whenever possible the scripts and other artefacts used to generate the analyses presented in the paper should also be publicly archived. The data availability statement should be placed at the end of the manuscript of data articles, immediately before the “References” section.

Statement templates

• The data sets generated during and/or analyzed during the current study are available in the [NAME] repository, [PERSISTENT WEB LINK TO DATASETS].
• The data sets generated during and/or analyzed during the current study are not publicly available due [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request.
• Data sharing not applicable to this article as no data sets were generated or analyzed during the current study.

ScienceDB

This journal supports Science Data Bank (ScienceDB), enabling you to deposit any research data (including raw and processed data, video, code, software, algorithms, protocols, and methods) associated with your manuscript in a free-to-use, open access repository. During the submission process, after uploading your manuscript, you will have the opportunity to upload your relevant data sets directly to ScienceDB. The data will be listed and directly accessible to readers next to your published article online. For an example, visit the article at https://doi.org/10.1162/dint_a_00090, click Supplemental Material.

For more information about ScienceDB, visit the ScienceDB at http://www.scidb.cn/en/English/ours/use.

How to Cite Data

In line with emerging industry-wide standards for data citation, references to all data sets described or used in the manuscript should be cited in the text with a superscript number and listed in the “References” section in the same manner as a conventional literature reference. An example is listed as follows:

Zhou, L.: GeoLink. ScienceDB https://doi.org/10.11922/sciencedb.j00104.00006 (2020)

Code Availability

For all studies using custom code, a statement should be included under the subheading "Code Availability" indicating whether and how the code can be accessed, including any restrictions to access. This section should also include information on the versions of any software used, if relevant, and any specific variables or parameters used to generate, test, or process the current data set.

Supporting Information

Authors should submit essential supporting files and multimedia files along with their manuscripts. All Supporting Information will be subject to peer review. Authors may use almost any description as the item name for a Supporting Information file as long as it contains an “S” and a number, e.g. “S1 Appendix” or “S2 Appendix” or “S1 Table” or “S2 Table”.

List Supporting Information captions in a separate page as part of the manuscript file. The file number and name are required in a caption, and we highly recommend including a one-line title as well. You may also include a legend in your caption, but it is not required. We recommend that you cite Supporting Information in the manuscript text, but this is not a requirement.

How to Submit

Please submit your papers at https://mc03.manuscriptcentral.com/di.

Thanks very much for considering Data Intelligence for your publications. We look forward to reading your submissions!