We are pleased to report that the County Surveys of Great Britain 1793 – 1817 project, which is related to the Statistical Accounts, has now released an online bibliographic search tool. This is a key output of this pilot project and will be of wide interest to historians and researchers in many fields.

Here we re-post of the County Surveys blog announcement:

We are delighted to announce that our bibliographic search tool is now live and accessible from the ‘Search’ tab in the menu above.

Our demonstrator includes bibliographic data from some of the best collections of the surveys and, where possible, provides links to library catalogue entries and  digital editions. Researchers can search by modern county name, by series, by county and by author. Results are presented in a new tab after each search, so that you can compare multiple search results by toggling between pages. There are also detailed analyses of collections, revealing the extent of holdings and coverage, and indicating which surveys would be needed to complete each collection.

We hope that the demonstrator will be a useful finding aid and discovery tool for those interested in the County Surveys, the history of statistical reporting and British history more broadly. We would welcome any feedback on the tool, and would be very keen to hear about how it is used or whether it could usefully offer other features and information. If you have ideas, please get in touch with us at edina@ed.ac.uk.

Blog post by University of Manchester project developer Tom Higgins:

Yesterday I gave a short presentation on the Data Vault project at an event in Lancaster:

https://www.eventbrite.co.uk/e/research-data-management-solutions-for-your-needs-tickets-17100593335

I based this on the original pitch with a few updates reflecting the work we’ve done over the last couple of months.

Here’s some of the feedback and questions from the event – I think a lot of these are more relevant for “phase 2 and beyond” than the current prototyping:

• How does the Data Vault differ from iRODS? Perhaps the policy model from iRODS could be useful or iRODS could serve as a back-end. There was a comment that iRODS may be more useful where the researcher’s workflow is known and can be encoded into the system (e.g. it’s deeply involved in the day-to-day active data).
• Archivematica (being explored by a project in York) can handle many preservation activities but has a specialist user interface which is not suitable for researchers to use directly. Perhaps a Data Vault could be used to ingest data and hand it over the Archivematica for preservation.
• How would a Data Vault handle sensitive data? Would it be need to be certified? What if the “back-end” was using a certified storage system – would that ease the burden at all? I mentioned that perhaps both a “general” and a locked-down “sensitive” instance of the software could be run in parallel.
• How could a Data Vault handle a dataset that is changing over time? Perhaps snapshots could be captured periodically – would this use a lot of storage space?
• Could data be ingested from instruments automatically? I think this is an interesting one because the researcher will presumably want to access the data on active storage too (e.g. just ingesting into the vault isn’t particularly useful since you’d then need to pull it back out to actually work with the data, but you may want to have a frozen copy of the raw data too).
• How could a Data Vault handle complex data e.g. from a database or an object store? In the simple case a user could export their data (e.g. in a backup format) and store that data (similar to how they might back up a database to a USB drive). Does it make sense for the a vault to try to understand complex data?

Here are some examples of “Active” and “Archive” systems which might be useful targets for integration:

• Box
• Hitachi Content Platform
• DuraCloud
• iRODS
• Archivematica

The development model we chose for the Data Vault is to get us all in a room (Robin, Tom, Claire, Mary, Stuart) and to collaboratively develop the proof of concept system over a few days.  We were kindly hosted by the University of Manchester IT services in their Sackville Street building.

We started by looking at the skeleton framework that Tom and Robin had worked on, and then assigned areas of code to each person to write.  For example work was required on the user interface that the user sees, the broker in the middle that manages the system, and the backend workers that perform the archiving.

All of the code is stored openly in github, and is open source with an MIT license:

• https://github.com/DataVault/datavault

Work is now continuing following the hackathon to complete a few areas of remaining code before the next Jisc Data Spring programme meeting where we can share the system with others.

On 10th of June, the Data Library team ran two workshops in association with the EU Horizon 2020 project, FOSTER (Facilitate Open Science Training for European Research), and the Scottish Graduate School of Social Science.

The aim of the morning workshop, “Good practice in data management & data sharing with social research,” was to provide new entrants into the Scottish Graduate School of Social Science with a grounding in research data management using our online interactive training resource MANTRA, which covers good practice in data management and issues associated with data sharing.

The morning started with a brief presentation by Robin Rice on ‘open science’ and its meaning for the social sciences. Pauline Ward then demonstrated the importance of data management plans to ensure work is safeguarded and that data sharing is made possible. I introduced MANTRA briefly, and then Laine Ruus assigned different MANTRA units to participants and asked them to briefly go through the units and extract one or two key messages and report back to the rest of the group. After the coffee break we had another presentation on ethics, informed consent and the barriers for sharing, and we finished the morning session with a ‘Do’s and Dont’s exercise where we asked participants to write in post-it notes the things they remembered, the things they were taking with them from the workshop: green for things they should DO, and pink for those they should NOT. Here are some of the points the learners posted:

DO
– consider your usernames & passwords
– read the Data Protection Act
– check funder/institution regulations/policies
– obtain informed consent
– design a clear consent form
– give participants info about the research
– inform participants of how we will manage data
– confidentiality
– label your data with enough info to retrieve it in future
– develop a data management plan
– follow the certain policies when you re-use dataset[s] created by others
– have a clear data storage plan
– think about how & how long you will store your data
– store data in at least 3 places, in at least 2 separate locations
– backup!
– consider how/where you back up your data
– delete or archive old versions
– data preservation
– keep your data safe and secure with the help of facilities of fund bodies or university
– think about sharing
– consider sharing at all stages. Think about who will use my data next
– share data (responsibly)

DON’T
– unclear informed consent
– a sense of forcing participants to be part of research
– do not store sensitive information unless necessary
– don’t staple consent forms to de-identified data records/store them together
– take information security for granted
– assume all software will be able to handle your data
– don’t assume you will remember stuff. Document your data
– assume people understand
– disclose participants’ identity
– leave computer on
– share confidential data
– leave your laptop on the bus!
– leave your laptop on the train!
– leave your files on a train!
– don’t forget it is not just my data, it is public data
– forget to future proof

Our message was that open science will thrive when researchers:

• organise and version their data files effectively,
• provide comprehensive and sufficient documentation for others to understand and replicate results and thus cite the source properly
• know how to store and transport your data safely and securely (ensuring backup and encryption)
• understand legal and ethical requirements for managing data about human subjects
• Recognise the importance of good research data management practice in your own context

The afternoon workshop on “Overcoming obstacles to sharing data about human subjects” built on one of the main themes introduced in the morning, with a large overlap of attendees. The ethical and regulatory issues in this area can appear daunting. However, data created from research with human subjects are valuable, and therefore are worth sharing for all the same reasons as other research data (impact, transparency, validation etc). So it was heartening to find ourselves working with a group of mostly new PhD students, keen to find ways to anonymise, aggregate, or otherwise transform their data appropriately to allow sharing.

Robin Rice introduced the Data Protection Act, as it relates to research with human subjects, and ethical considerations. Naturally, we directed our participants to MANTRA, which has detailed information on the ethical and practical issues, with specific modules on “Data protection, rights & access” and “Sharing, preservation & licensing”. Of course not all data are suitable for sharing, and there are risks to be considered.

In many cases, data can be anonymised effectively, to allow the data to be shared. Richard Welpton from the UK Data Archive shared practical information on anonymisation approaches and tools for ‘statistical disclosure control’, recommending sdcMicroGUI (a graphical interface for carrying out anonymisation techniques, which is an R package, but should require no knowledge of the R language).

Finally Dr Niamh Moore from University of Edinburgh shared her experiences of sharing qualitative data. She spoke about the need to respect the wishes of subjects, her research gathering oral history, and the enthusiasm of many of her human subjects to be named in her research outputs, in a sense to own their own story, their own words.

Links:

• Scottish Graduate School of Social Science
• FOSTER Open Science
• Research Data Management Training – MANTRA course

Rocio von Jungenfeld & Pauline Ward
EDINA and Data Library