Publish & Communicate

Learn how the library can help you to publish & communicate your results

The library offers support in the areas of publishing and communicating scientific results. Researchers can learn about Open Access, Research Data Management Plans, IST Austria Research Explorer and Author Identifier.


On this site:

Open Access

Open Access – unrestricted access to scientific publications – is an ongoing trend in the scientific community. Open Access makes scientific publications and other digital material, such as research data, freely accessible via the world-wide-web. IST Austria is committed to providing unrestricted and free online access to scientific publications for all users and researchers as widely as possible. The main goal is to increase the visibility, use, and impact of research output, and thereby generate added value for the scientific community.

Learn more about Open Access on IST


Research Data

The relevance of managing and sharing research data has been growing over the years. Several research funders (e.g. EC) require research data management plans and research data to be as freely available as possible at the end of a project. Besides these requirements, there are many benefits in thinking about data management in advance.

  • Make your research easier
  • Do not drown in irrelevant stuff
  • Save data for later
  • Ensure research integrity
  • Write a data paper
  • Share your data for re-use and/or possible large scale collaborative research
  • Increase the impact of research results and get credit for it
  • Save research funds and resources by avoiding needless duplication of work
  • Enhance data security and minimize the risk of data loss

Research Data Management Plan

In a nutshell, a RDM plan is a strategic document that outlines the handling of data throughout its lifecycle, covering measures during and after your research project. It is a very useful tool for determining and documenting actions as well as supporting the overall project planning. As a basis for the reusability of data, it can prevent many data management issues or help to handle others. There are many ways to draft an RDM-plan, mostly depending on research field, complexity and size of the project. But, typically, researchers are asked to cover:

Data description

Characterize the data you are working with. Categorize the source (observational, experimental, simulated, derived, compiled ), form (text, numeric, audio visual, models, computer code), format, data stability (fixed, constantly growing, revisable) and the expected volume of the data (file size, amount of data).
As to accessibility and reusability, it makes sense to consider very common and open data formats or to make sure that it is feasible to archive them in a better usable format. You will find more infomation at the Library of Congress: Sustainability of Digital Formats

Data organization

When working in a team, it is crucial to agree on ordering principles and to document them. This means you need to agree on conventions for files (e.g. naming, versioning, the directory structure). Here is some general advice regarding file naming:

  • Use descriptive names that indicate what the file/folder contains
  • Use short names (less than 50 characters)
  • Use simple names that are easy to understand
  • Use alphanumeric characters
  • Use underscores (_) or dashes (-) rather than spaces
  • Avoid special characters such as: \ ‘= /,<>^:;()#*?%,”@!+{}~`[]
  • Avoid using internal project codes or acronyms that individuals outside of your laboratory or research group would not understand
  • Incorporate the temporal or spatial information when applicable

Metadata and documentation

Metadata – data about the data – helps to find, understand, analyze and reuse data. Metadata is the structured element of documentation with fixed formal criteria and only the most important contextual information, which makes it also machine-readable.
In general, data documentation describes the who, how, what, where, when and why of the data set. Depending on various data characteristics, data documentation varies in length and structure and often comes in no standardized form. The procedure varies from one research area to the other (e.g. lab notebook in experimental research, description of data processing in computational research). It should be easy to read and to understand (clear, short, familiar words) to allow traceability and reproducibility of data operation even beyond research fields. Here are some issues that need to be addressed:

  • The scientific reason why data were collected
  • What data parameters were collected, including units and formats
  • What instruments/platforms were used to collect/generate the data
  • A list of the data files that make up the dataset
  • Codes used in the dataset and definitions of what each code means
  • When and how frequently the data were collected
  • How each parameter was measured or produced (methods)
  • For each parameter, the units of measure and the format
  • For each parameter, the precision and accuracy (if known)
  • Data processing that was performed
  • Standards or calibrations used, if applicable
  • Software used to open and manipulate the data
  • Quality assurance and quality control methods
  • Date when the dataset was last modified
  • Known problems that limit data use

Ethics and intellectual property

When it comes to research data, the following regulations could be of importance:

Privacy / data protection: applies if personal data is collected during the research (especially life sciences). For storing and further use, a declaration of consent is needed and the data has to be anonymized to avoid disclosure of individuals.

Copyright: Primary research data (i.p. measurement data) itself are in many cases not affected by copyright but are subject to the public domain. As soon as they are accumulated in a specific order or processed, they are protected (e.g. database right). If you use external data which is copyright protected, you have the option to obtain the usage rights from the copyright holder via a transactional transfer.

Clarifying ownership of and rights relating to research data is recommended before a project starts. If you plan to share your data, provide clear guidance on what re-users can do with it. One way of clarifying the terms of use is to license your data. The traditional methods are data sharing agreements or collaborations. Another option is to use open licenses and granting rights to anyone.

For sharing and reusing data, CC0 public domain dedication is the recommended license. For explanation and further information see the DCC’s guide How to License Research Data.

Data access and sharing

As to data access during the project, you have to be aware of data security, authentication, access rights, and data synchronization.

Data sharing is especially an issue after the project. When preparing your data for sharing, things like formats, documentation, ownership and confidentiality are main factors to think about. There are various ways for data sharing. The most common ways are sharing via email or physical device after an individual request, putting it online on a personal webpage, adding it as supplementary material to the publication at the journal’s platform, depositing it in an open repository and publishing a data paper.

A number of funding agencies (EC, FWF) and science publishers (Nature Publishing Group, BMC, PLOS) require data sharing underlying published research via an open repository. Nevertheless there are reasons to restrict access to certain data or parts of it (e.g. sensitive data, copyright protected data). Present a strong case for any restrictions on sharing, such as embargo periods or restricted access, and ensure these are properly justified.

At IST Austria, you have the opportunity to deposit your data in the institutional data repository IST Austria Research Explorer. A DOI will be assigned to the datasets and they will be publicly accessible and stored for the long term. In your RDM plan you may insert the subsequent phrase to articulate your intentions of storage and sharing:
At the Institute of Science and Technology Austria an institutional  and publicly accessible data repository (IST Austria Research Explorer: research-explorer.app.ist.ac.at) for data publication and sharing is provided. Furthermore deposited data is registered with DataCite and therefore assigned a DOI which enables data citation.
(Name of the project/researcher/etc.) commit(s) to data deposit in IST DataRep within the period agreed, for data which are appropriate to share.

If you find a suitable subject repository, we recommend you choose it over the institutional repository as subject repositories can provide very particular services to subject specific requirements.

Here are some issues you might deal with:

  • Risks to data security and strategies to avoid them
  • Handling of sensitive data
  • Risk of data being procured illegally and manipulated
  • Safe transfer of field data into the main storage
  • Access rights of project members and collaborators
  • Providing password protection for data access
  • Funder’s requirements regarding data sharing
  • Repository and its requirements for data deposit

Storage and long-term archiving

How to store and backup data during the project are issues that need to be considered. Besides taking into account the resources already available, it is important to evaluate and decide on further measures if necessary.

After the project data might have to be retained for different reasons (Austrian data protection law DSG, funder requirements, long-term value of the data). Preparing data to expected standards for archiving are time-consuming processes, for which you should allocate significant resources. Data, which underpin publications, should be extracted, captured in machine-readable form and deposited in a repository so they remain accessible. Make sure you know about any repository policies that might affect your data (e.g. accepted data, preferred formats, normalization processes).

Recurring topics regarding storage and long-term archiving might be:

  • Availability of storage space
  • Charges for additional services
  • Data back up strategy
  • Responsibilities for backup and recovery
  • Data recovery in the event of data loss or corruption
  • Location to archive data for the long term
  • Availability of discipline-specific repositories
  • Archiving software or tools necessary to use the data
  • Time span of data retainment

Below you can find links to material that assist you in creating an RDM plan:

General Guidance
Data Management General Guidance (DCC)

Guidelines on FAIR Data Management in Horizon 2020

Guidelines on Open Access in Horizon 2020 to Publications and Data for IST Austria affiliates
Framework for creating a data management plan

Checklists
Checklist for RDM planning

Elements of a RDM plan

Examples for a RDM Plan
Horizon 2020
Biology and Chemistry

Templates for RDM Plans
DMP Online Tool
E-infrastructures Austria: Template for Data Management Plan


IST Austria Research Explorer

IST Austria Research Explorer is the new institutional repository at IST Austria which combines the former IST PubRep, IST PubList and IST DataRep within one repository. 

It contains all publications, published research data and grants from researchers at IST Austria and supports the mission to make research at IST Austria as accessible as possible to the public. The data sets are provided with a Digital Object Identifier (DOI), which allows persistent citation.

The system is based on the software LibreCat, which is developed by the Universities of Bielefeld, Ghent and Lund. 

Researchers affiliated with IST Austria can add publications via different import functions (e.g. using DOI, ArXiv ID or PubMed ID ) and publish their scholarly papers Open Access under the conditions of the publisher. The IST Research Explorer therefore enables researchers to meet the requirements for Open Access to the research cycle as defined by such grants and funders as the ERC Horizon 2020 or FWF.

The metadata records (except the copyright protected abstracts) are available under a CC0 license.

User Guides

  • Create New Entries
    This manual guides you through creating a new entry in the institutional repository.
  • Search and Find
    This manual explains how to search and find items in the institutional repository.
  • Upload Research Data
    This manual deals specifically with creating a new research data entry in the institutional repository.

Author Identifiers (ID)

What is an Author ID?

An Author ID is a persistent and unique digital identifier that allows researchers to distinguish themselves from other researchers and to unambiguously link themselves to their research activity.

Why should a researcher have an Author ID?

Most personal names are not unique, and especially for researchers who publish their scholarly literature online this can cause different problems. Name ambiguities arise from:

  • identical names of different researchers
  • a large number of common names, especially surnames (e.g. Chan, Smith, Müller)
  • inconsistent name formats
  • legal name changes
  • cultural differences in the position of surnames
  • compound or hyphenated names

An identifier helps to solve the problem of author ambiguity and makes it easier to distinguish authors of publications from each other. But there are even more advantages to creating an Author ID:

  • makes it easier to get credit for scientific work.
  • simplifies the grant submission workflow for funding organizations and makes tracking the output of the research that they funded easier.
  • eases the tracking of achievements of members of scholarly societies.
  • helps institutions to collect, display and evaluate the research activities of their faculty.
  • makes (electronic) paperwork much easier. Personal details need not be filled out in electronic form since researchers can simply use their Author ID.

Which Author IDs exist?

ORCID is run by the non-profit organization Open Researcher Contributor Identification Initiative and provides researchers with a unique Author ID. It is used across nations and disciplines and is available free of charge. 

In order to get an ORCID you need to register online. To do so, go on the ORCID website http://orcid.org/ where you will find the instructions on creating your own ORCID. Once registered, you can link your ID to other Author IDs such as ResearcherID or Scopus Author ID. 

ResearcherID is a feature of the Web of Knowledge database. It connects all your publications that are listed in the Web of Science to your personal profile. You can also be connected with other Author IDs, such as ORCID.

To create a ResearcherID you need to register on the website http://www.researcherid.com/SelfRegistration.action. After filling out the form and submitting the personal details, an ID and a webpage will be provided. The webpage can be used as a link to the personal profile. 

A Scopus Author ID assigns researchers with a unique number and makes it possible to group together their documents. It is possible to assign variants of the author’s name to one single profile. This helps to connect an author to their work even if cited in different ways.

Scopus automatically creates an Author ID for you when you publish in a journal that is indexed by the database. It is very easy to link your scholarly publications to ORCID through a direct link on the author detail page.

Creating a Google scholar citation profile enables Google to link all your publications to you (either automatically or reviewed by yourself first). The profile provides information about your name, your research interest, generated citation metrics, and citations

To creat a Google scholar citation profile, go to the website http://scholar.google.com/intl/en/scholar/citations.html. You need to have a Google account to create a profile. Sign in to your account and register online for your citation profile. If you set your profile to public, your information can be found in Google scholar when somebody searches for your name.



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