Everyone develops their own set of software tools over time. There are lots of handy specialist software programmes out there. Look out for those that have a strong support community around them. This is what makes R such a pleasure to learn and Nvivo so frustrating. I’ve selected ones I use that should be useful for researchers whatever their specialism.

For my references: Zotero with WebDAV for file syncing. A free pCloud account gives me 10GB of storage. I will never need that many pdfs. It works with Google Docs which is another bonus.

For presenting: Google Slides – shareable and simple.  The requirement to use Powerpoint for digital lecturing has put the kibosh on this for the now. Portability matters a lot to me and being able to drop work on the laptop and immediately pick it up on an iPad smooths things out.

For literature reviews: any spreadsheet programme will do here. I use Numbers because it’s free, on my mac and iOS, and the file saving system means it writes changes immediately – no crash risk. I started using a spreadsheet for literature reviews following a post by Elaine Gregerson https://alawuntoherself.com/2016/05/20/how-i-use-excel-to-manage-my-literature-review/ and see also https://www.insidehighered.com/blogs/gradhacker/organizing-your-literature-spreadsheet-style

For extended writing: Scrivener is one of a few word processors that are designed with writers in mine. Draft, store, organise and restructure anything from an article to a book.

For collaborative writing: Google Docs is easily shared and has a low barrier to entry. I’ve long since surrendered to the vampire squid on that front.

There’s plenty of other incidental software and platform choices we make – email, word processing, cloud storage, anti-virus and it’s worth taking the time to use ones that will work for you.  Here’s one overview to selecting the right tools and developing the right cast of mind: http://www.docs.is.ed.ac.uk/skills/documents/3933/3933.pdf

Visiting the art college and the Informatics school reminds me how uncreative most academic buildings and arrangements are. Many bear a striking resemblance to Dunder Mifflin, a monastery made of MDF, or the offices of a well established but slightly shady law company specialising in lengthy divorce battles. Students are often skilled in working the space to find places to work and gather. Talking to them about how they use the space is a revelation.

Looking closer, university estates provide an archeology of educational theory and a record of the changed purpose of the university in society. This is why some of the buildings appear to be designed by a rogue group of social psychologists testing the effect of de-spatialisation on the human mind. Floors that are undifferentiated expect by a small patch of differently faded carper. Naming conventions produced by a random number generator. Several university buildings I have visited decided to letter rather than number their floors. Even better, one chose not to letter them alphabetically. There appeared to be a missing floor. These strange choices are often the result of elements of building design that no longer apply to its current use. Floors were named after departments or teams that no longer inhabit them. Two separate buildings were joined at different levels and in an unsuccessful attempt to make things less confusing two different numbering systems were used.

Myths emerge around the building, of secret passageways and hidden rooms. Some of these are true. One building had a set of rooms that had been forgotten about for decades and were uncovered when someone shifted a filing cabinet from its 30 year perch. In another students had gained the impression that they were not permitted to go about the first floor. This was partly based on reality. The building had been designed to encourage them to remain in the lower floors where most student services were located. It tells of who the building is for. Wouldn’t it be better to design it so that students and staff mingle? It’s preferable to monastic silence and self isolation. Are we a community of scholars or an education industry?

Many more aspects of design and location tell you about the relationship between the university and society. Is it on a campus or distributed? What are the buildings made of? Which departments would you expect to find in stone buildings compared to brick or concrete ones? Where is the administrative centre, relative to the literal centre? What is the public face? These aspects tell us what the past, present and future direction are. Sometimes the news is good, sometimes not. There are many who would abolish the university as a site altogether and have us study and work in no-place. Even no-place matters though, and universities remain sites apart for a reason. Sometimes they are places of violent struggle and resistance, for a reason. They should not be tame.

In the early stages of the PhD, you work to ‘reduce the problem space’. Define the problem using the smallest number of steps, variables or data points. This shows you the scope of your study. Then elaborate the problem in as many contexts as you think it applies. This shows you the applicability of it. Most of our advice boils down to ways of doing both. The term comes from computer science. The initial problem state and solutions to it are partly unknown. The algorithm is designed to propose potential solution states which are selected according to a heuristic.

All research involves a heuristic: a way of engaging with the research material or context that captures some essence about it. Sela-Smith (2002) characterises it as tied to phenomenological research where the research seeks discovery by recreating the research subject’s experience. My view – one shared with her – is that a heuristic approach is involved throughout any kind of research or theory. In qualitative research it is more usually tied to the self, and therefore becomes more apparent. However it is quite typical in other methods too. A theorist uses a heuristic reading of his or her subject matter. A computer scientist using human in the loop machine learning is doing the same. As Sela-Smith says, heuristic scholars can be found everywhere research is being done. They have some notable qualities, not afraid to take risks, or recommit to learning throughout their lives. Much of this is quiet though, taking place often in places that are not on the list of high status academic achievements.

Sela-Smith outlines six aspects of the heuristic approach: ‘indwelling, tacit knowing, intuition, self-dialogue, focusing, and the internal frame of reference’. I word this as consistent self reflection, developing a feel for things/getting skin in the game, sparking understanding, creating the internal conversation, living with the problem and knowing the problem set. Framing it as I have pulls it away from Sela-Smith’s focus on the self so it changes the meaning and quality of the heuristic, so you should look her article to understand it. Each element has a related activity: ‘initial engagement, immersion, incubation, illumination, explication, and creative synthesis.’ Some of these tasks will be very familiar to the PhD researcher.  Each can be broken down into sub-tasks which contribute to the whole. For example, developing a taxonomy can be part of incubation. Creative synthesis is an activity that I have seen some PhDs do very successfully. They use methods such as creating fictions from the data in the form of narratives of what was, is and might be.

The problem space approach might seem to work against creativity as it focuses on producing defined solution states. However if we see the solution state as being a product of the creative process then it fits better. One different I have with the heuristic approach is that it tends to focus on the individual, rather than the researcher as embedded within a community. Researchers might feel the individual qualities of the heuristic process as being demanding or involving risks e.g of self-alienation. I can see risks in piling requirements on the emotional labour of the PhD. This is where having a creative community comes in, where the work is held within the group rather than being a lonely labour of love. Computer science has a set of explicit protocols to do this but social science does not so that is something we can learn about.

Is someone likely to use it to detonate under me at a conference (‘but surely you know that Bodkins has covered your very argument in her seminal work in the journal of Ipretendtoreadit studies?’)

Am I likely to meet the author and give them a bland ‘loved the article’, and are they the type of person to say, ‘what did you like about it exactly?’

Is it too recent to be written off as outdated, and not recent enough to be ‘provisional findings I can forget about’?

Can I write a contrarian reading that will at last establish me as the unparalleled lone wolf polymath I know myself to be?

Will I look like an idiot if I haven’t read it?

Will I look like a wierdo if I have read it?

Can I hate read it?

Does it finally define ‘habitus’?

Can I crib their bibliography?

Is it neoliberal, or just capitalist?

Is it performative, or just displayed?

Is it paradoxical, or just contradictory?

Is it dichotomous, or just separate?

Is it criminalised, or just unlawful?

Is it totemic, or just symbolic?

Is it a practice, or just an activity?

Is it bounded, or just limited?

Is it a social construct, or just a creation?

Is it a moral panic, or just an upset?

Is it a structure, or just a pattern?

Do I fixate on the overuse of neoliberalism too much? Almost certainly.

When you start out as a research student, what do you think your main area of work will be? It might be data collection, the many challenges of access to a field, learning new skills to wrangle data, and always writing, writing, writing. It is a surprise when most of the work appears to have nothing to do with that. Instead the several labours of research students are:

• Finding your place in the field, the institution and the discipline
• Keeping keen, maintaining your drive and self belief
• Managing others, especially your supervisor
• Creating boundaries and reasonable expectations
• Legitimating – shaping your agenda and bending others to it
• Recognising your own agency
• Finding a path between complete and finished

Becker HS and Richards P (2007) Writing for Social Scientists: How to Start and Finish Your Thesis, Book, or Article: Second Edition. Chicago: University Of Chicago Press.

1. How do you write? What are the non function rituals and habits, that appear to having nothing to do with writing.
2. When do you turn to magic? The Trobrianders produce finely crafted boats, ideal for their function. They speak of their craft with deep scientific knowledge (if not scientifically expressed). When they face storms, the one event they cannot control by their craft, they turn to magic. What in your PhD creates the same trepidation – the factor you cannot control? What rituals do you use to ward off the weather gods? He cites: ways of signalling your writing is not done, so can’t be subject to the full wrath of the supervisory death stare.
3. Sociologists often imply strong cause but run away from saying it (my personal bugbear #1/9126)
4. Scientific writing is a rhetoric (Gusfield, Rhetoric of Social Science). Meaning writers use conventions to make themselves appear scientific. Think about the contradiction there. Many sociologists rail against scientific positivism, reductionism etc while using writing conventions to make themselves seem like they have the qualities involved. Distance on the subject, objective standpoint etc. This is your theory of writing which everyone uses – you have it even if you don’t think you do. There’s always a hidden purpose to the way you write.
5. So there are various research ‘rhetorics’ – styles with a purpose – and also ‘languages’ – organised meaning sets. They are a little independent of each other but not entirely. Anyway, the languages are usually more elaborated and dangerous to mix. Invoking feminist materialism having started on the path of Judith Butler is asking for problems but it might indicate a critical point in your quest where your path turns away from your intended goal.
6. Undergraduate skills don’t prepare you for a PhD. ‘Meet deadline … reach word count limit … balance out argument’. Yes, this is the academy’s fault. New scholars learn that obfuscation=academic capital and status. The problem isn’t quite as bad as that, but there aren’t enough checks and balances in the system at an early stage. We don’t model good behaviour, which involves someone saying ‘Wait a minute, what you said doesn’t make sense’. The way Becker frames this is probably reflective of his specific time and place – low oxygen US academia. It’s less true of the UK system but we have developed other problems e.g. research writing infested with gov-speak and NGO-vocab. One problem is correction via avoidance. People have learnt that theory=tortuous prose and so just avoid theory altogether. You. Can’t. Do. That.
7. Writing=risk of exposure.