Open access dataset citing is becoming more important. Funders do not only increasingly demand open access publishing of funded research articles, but also the underlying datasets. This is good practice as it also supports reproducability of studies and thus supports credibility of research results reported in articles.
Zenodo is a very popular repository for this type of making datasets available. As a matter of fact, you can publish all sorts of "data" and "supplemental materials" on Zenodo and back them with a forever persistent unique digital object identifier: a DOI, that thing that you also have for all your articles that a published in respectable journals. The very thing that is the reference when Reuters is counting your citations :-) So, you basically just create an account, upload your datasets, reserve a DOI and fill out the metadata, like title etc.
Furthermore, Zenodo allows you for example to link your GitHub repositories, your ORCID
But you also want that the citation looks good in your manuscript, or at least in the references sections. I had a few tries and read a few articles on the web about citing datasets with Mendeley in particular, but I didn't really get to the point where I could reproduce something like following reference:
Kmoch, A. & Uuemaa, E. Geo-referencing of journal articles and platform design for spatial query capabilities. Dataset on Zenodo (2018). doi:10.5281/zenodo.1153887
Several articles suggested to create a Bibtex entry with the "misc" type (instead of "journal" as seen below), because you want to indicate that it is actually a dataset and not an article (or book section). Similar issues happen when you want to cite a software program that is not described by a scientific article which in turn is what you would cite in your work.
Thus, in Mendeley, the free online citation manager, for Linux, Windows and Mac, with Bibtex, Endnote and RIS Import/Export support, I ended up creating a journal article entry, where the journal name is "Dataset on Zenodo". And most citation styles, such as MDPI IJGI, or Nature if you like, will nicely list your data this way (see above) in your references section :-)
For example, in our recent article "Enhancing Location-Related Hydrogeological Knowledge" on MDPI IJGI (http://www.mdpi.com/2220-9964/7/4/132) we added the Zenodo repository as supplementary material (https://zenodo.org/record/1153887), and it is nicely visible directly on the journal article's landing page.
"""
@article{Kmoch2018,
abstract = {We analyzed the corpus of three geoscientific journals to investigate if there are enough locational references in research articles to apply a geographical search method, on the example of New Zealand. We counted place name occurrences that match records from the official Land Information New Zealand (LINZ) gazetteer in the titles, abstracts and full texts of freely available papers of the New Zealand Journal of Geology and Geophysics, the New Zealand Journal of Marine and Freshwater Research, and the Journal of Hydrology, New Zealand, for the years 1958 to 2015. We generated ISO standard compliant metadata records for each article including the spatial references and make them available in a public catalogue service.},
address = {Tartu},
author = {Kmoch, Alexander and Uuemaa, Evelyn},
doi = {10.5281/zenodo.1153887},
journal = {Dataset on Zenodo},
mendeley-groups = {my datasets},
publisher = {Dataset on Zenodo},
title = {{Geo-referencing of journal articles and platform design for spatial query capabilities}},
url = {https://zenodo.org/record/1153887},
year = {2018}
}
"""
Wednesday, 28 February 2018
Wednesday, 7 February 2018
Info Session for MSc programme in Geoinformatics for Urbanised Society
Today we broadcast a webinar info session about the new MSc programme in Geoinformatics for Urbanised Society at the University of Tartu.
One of the recurring core topics in this new MSc curriculum is Data and GIS use in Urban Planning.
Until just a decade ago spatial planning and analytics projects had problems with getting enough data. But nowadays there is so much data available, that it is increasingly hard to make sense of it – because of the 3 V’s of big data - volume, variety, velocity. The open data movement, government agencies, research institutes and citizen scientists alike make more and more data available publicly, mobile phones, sensor networks and satellites generate a multitude of datasets every day
In order solve the Interdisciplinary challenges of urban planning we want to empower you with skills and knowledge to analyse, visualise and understand processes and data. For that we teach the Full cycle of spatial data management, from the various methods of data acquisition, followed by efficient and practical processing techniques, to subsequent meaningful analysis and visualisation; in order to consequently make successful planning decisions for a sustainable future.
So what does it mean to study Geoinformatics for Urbanised Society with us in Tartu?
You will learn how to combine geography and IT in the age of BIG data. This is essentially what we believe modern Geoinformatics is representing. Mastering Geoinformatics will provide you with tools to analyse social and natural processes in space for interdisciplinary decision- and policy-making.
Watch the whole recorded session for more info:
Links:
One of the recurring core topics in this new MSc curriculum is Data and GIS use in Urban Planning.
Until just a decade ago spatial planning and analytics projects had problems with getting enough data. But nowadays there is so much data available, that it is increasingly hard to make sense of it – because of the 3 V’s of big data - volume, variety, velocity. The open data movement, government agencies, research institutes and citizen scientists alike make more and more data available publicly, mobile phones, sensor networks and satellites generate a multitude of datasets every day
In order solve the Interdisciplinary challenges of urban planning we want to empower you with skills and knowledge to analyse, visualise and understand processes and data. For that we teach the Full cycle of spatial data management, from the various methods of data acquisition, followed by efficient and practical processing techniques, to subsequent meaningful analysis and visualisation; in order to consequently make successful planning decisions for a sustainable future.
You will learn how to combine geography and IT in the age of BIG data. This is essentially what we believe modern Geoinformatics is representing. Mastering Geoinformatics will provide you with tools to analyse social and natural processes in space for interdisciplinary decision- and policy-making.
Links:
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