Open source hardware for scientific researc

around

customisation options

Using these project management features can be beneficialeven when only one or two people work on a proje

, and avoids many pitfalls of projectsthat are only “open when ready

This can help fill in the gapsin incomplete instructions, and serves as an invaluable resource for troubleshooting andcustomisation that complements complete documentation in a mature projec

discussio

from assembly instructions to initially construct an instrument

. For a potential collaborat

Git-based platforms such asGitHub or GitLab are commonly used for thi

Starting an open collaboration

all parties are aware of ongoing wo

Their complaint was this: why should there be a separate field of computer science, any more than there is a separate field of refrigerator science?

Therefore, there is ongoing work on rep-resenting logical knowledge and proofs in web languages.

An important advantage of logic is that it can provideexplanationsfor conclusions:the series of inference steps can be retraced.

logiccan be used to uncover ontological knowledge that is implicitly given. By doing so, itcan also help uncover unexpected relationships and inconsistencies

Horn logic,

ontologies providea shared understanding of a domain

Anontology is an explicit and formal specification of a conceptualization.

: XML describesstructure while RDF makes statements about pieces of information

dealingwith the ever-increasing scale, lowering the barrier of adoption, and of course fightingthat omnipresent bane of information systems: semantic heterogeneity

1. We must agree on standard syntax to represent data and metadata.2. We must have sufficient agreement on the metadata vocabularies in order to shareintended semantics of the data.3. We must publish large volumes of data in the formats of step 1, using the vocab-ularies of step 2.

It is this decouplingthat is the essence of the weblike nature of the Semantic Web

3. useontologies(briefly: hierarchical vocabularies of types and relations) as thedata model to formally represent the intended semantics of the data

1. uselabeled graphsas the data model for objects and their relations, with ob-jects as nodes in the graph, and the edges in the graph depicting the relationsbetween these objects.

The Semantic Web (or The Web of Data, as it is becoming known in recent years2)follows different design principles, which can be summarized as follows:1. make structured and semi-structured data available in standardized formats onthe web;2. make not just the datasets, but also the individual data-elements and their rela-tions accessible on the web;3. describe the intended semantics of such data in a formalism, so that this intendedsemantics can be processed by machines.

We identify inclusion models with regard toexpertise,9which is attributed to theincluded actors. Following on from Collins and Evans [2007], we shall distinguishseveral levels of expertise, which — in descending order — are consideredcomparable to science:–contributory expertise: Expertise corresponding to a scientist in a particularscientific area,–interactional expertise: Expertise enabling technical communication withexperts of a given scientific area as well as informed decisions,–primary source knowledge: Expertise originating from the reception of scientificliterature,–beer-mat knowledge: Expertise requiring only schematic knowledge which fitson a beer mat.1

Paradigmatically, reference needs to be made to the “Biohacking” Scene, which hasjoined the above-mentioned trends to be able to practice life science also outsideestablished institutional settings [Bennett et al., 2009; Delfanti, 2010; Wylie et al.,2014]. With regard to biohackers, Seyfried, Pei and Schmidt [2014] have come to theconclusion that prototypical forms of the opening up of science can be outlined,which are no longer focused on a safe experts/lay people difference [Seyfried, Peiand Schmidt, 2014]

The major changesbrought about by digitization include the fact that digital communicationfacilitateslinksbetween amateur scientists, produces reciprocal visibility andenables the publicrepresentationof research beyond the circle of professional scientists to reach thepublic at large, through the creation and editing of Wikipedia articles. Finally,digitalization increases themeansat the disposal of amateur scientists through theuse and further development of open-source software and hardware [Haklay, 2013;Wylie et al., 2014; Xue, 2014].

With its focus on interactions betweenniches, regimes and landscape, the MLP perspectiveprovides narrative explanations. This does not imply sto-rytelling or empiricism. The main point is that narrativeexplanations do not work with dependent and indepen-dent variables, but explain in terms ofpatternsthatresult from interactions. This is a specific type of theory,coined in the literature as process theory (Abbott, 2001;Pettigrew, 1997; Poole et al., 2000).

The MLP typically is a global model that maps theentire transition process. Such a global model tends togive less attention to actors. Yet, the MLP does allowthe analyst to zoom in on actors: “Because the link-ages between processes at different levels are made byactors in their cognitions and activities, the dynamics [inthe MLP] are not mechanical, but socially constructed.(...) Transitions are contested and different groups strug-gle, negotiate, and form coalitions”

If landscape pressure takes the form of ‘disrup-tive change’, asequence of transition pathwaysis likely,beginning with transformation, then leading to recon-figuration, and possibly followed by substitution orde-alignment and re-alignment.

Technological substitution

De-alignment and re-alignment path

Landscape changes only exert pressure if they areperceived and acted upon by regime actors.

Transformation path

P0.Reproduction process:

Niche-innovations have acompetitiverelationshipwith the existing regime, when they aim to replace it.Niche-innovations havesymbioticrelationships if theycan be adopted as competence-enhancing add-on in theexisting regime to solve problems and improve perfor-mance.

Reinforcing landscape developments have stabilisingeffects on the regime and form no driver for transitions.Disruptive landscape developments exert pressure on theregime, creating impulses for change

we propose the following proxies asreasonable indicators for the stabilisation of viableniche-innovations that are ready to break through morewidely: (a) learning processes have stabilised in adominant design, (b) powerful actors have joined the sup-port network, (c) price/performance improvements haveimproved and there are strong expectations of furtherimprovement (e.g. learning curves) and (d) the innova-tion is used in market niches, which cumulatively amountto more than 5% market share.5

,Van Driel and Schot (2005)elaboratedthe landscape concept, distinguishing three types: (1)factors that do not change or that change only slowly,such as climate; (2) long-term changes, such as Germanindustrialisation in the late 19th century; (3) rapid exter-nal shocks, such as wars or fluctuations in the price ofoil.

kinds of rules1: regulative, normative, and cognitive.Examples ofregulativerules are regulations, standards,laws. Examples ofnormative rules are role relationships,values, behavioural norms. Examples ofcognitiverulesare belief systems, innovation agendas, problem defini-tions, guiding principles, search heuristics.

Both niches and regimes have thecharacter of organisational fields (community of inter-acting groups). For regimes, these communities are largeand stable, while for niches they are small and unstable.Both niche and regime communities share certain rulesthat coordinate action. For regimes these rules are sta-ble and well articulated; for niche-innovations, they areunstable and ‘in the making’.

he analystshould first demarcate the empirical level of the objectof analysis, and then operationalise the MLP

This article aims tomake progress by developing a typology ofmultipletran-sitions pathways

MLP is “overlyfunctionalistic. Despite the breadth of the regime con-cept, there is a tendency to treat regime transformationas a monolithic process, dominated by rational actionand neglecting important differences in context. We alsoargue that existing approaches tend to be too descrip-tive and structural, leaving room for greater analysis ofagency”

One new addition to this figure are downwardarrows towards the niche-level, indicating that percep-tions of niche actors and the size of support networksare influenced by broader regime and landscape devel-opments.

Evolutionas ‘unfolding’ is included by understanding regimechanges as reconfiguration processes.

Cascade dynamics are important,meaning that changes in one elements of the regimetriggers changes in other elements which, in turn, trig-ger further changes. Such reconfiguration processestake place on all dimensions of the socio-technicalregime (e.g. markets, user groups and user practices,technologies, production networks, policies).

The general pattern by which radi-cal innovations break out is that they follow trajec-tories ofniche-cumulation

even dimensions in thesociotechnical regime: technology, user practices andapplication domains (markets), symbolic meaning oftechnology, infrastructure, industry structure, policyand techno-scientific knowledge

“It is the alignmentof developments (successful processes within theniche reinforced by changes at regime level and at thelevel of the sociotechnical landscape) which deter-mine if a regime shift will occur”

These internal niches processes have beenanalysed and described under the heading of strategicniche management

Because these niches are protected or insulated from‘normal’ market selection in the regime, they act as‘incubation rooms’ for radical novelties (Schot, 1998)

The landscape is an external structure orcontext for interactions of actors. While regimes referto rules that enable and constrain activities withincommunities, the ‘ST-landscape’ refers to widertechnology-externalfactors.

‘sociotechnicalregimes’ to refer to the semi-coherent set of rulescarried by different social groups. By providing ori-entation and co-ordination to the activities of relevantactor groups, ST-regimes account for thestabilityof ST-configurations. This stability is of a dynamickind, meaning that innovation still occurs but is of anFig. 2. The multi-actor network involved in sociotechnical regimes.incremental nature

Technical trajectories are notonly influenced by engineers, but also by users, policymakers, societal groups, suppliers, scientists, capitalbanks etc.

Organisations, and the actors involved, remember bydoing. Such routine-based behaviour also goes forengineers, e.g. in the form of search heuristics. In sofar as engineers and firms share similar routines, theseform atechnological regime. Technological regimesresult intechnological trajectories, because the com-munity of engineers searches in the same direction.Technological regimes create stability because theyguide the innovative activity towards incremental im-provements along trajectories.

Within evolutionary economics, there are two viewson the process of technological evolution.1In the firstview, evolution is a process of variation, selectionand retention. InSection 2, I build upon this view byusing Nelson and Winter’s concept of ‘technologicalregimes’ to understand inertia of established tech-nologies. In the second view, evolution is a process ofunfolding, creating ‘new combinations’ (Schumpeter,1934, p. 66), resulting in paths and trajectories

Thedifferent levels are not ontological descriptions ofreality, but analytical and heuristic concepts to under-stand the complex dynamics of sociotechnical change.

the idea that linkages be-tween technical and social elements provide stabilityis particularly emphasised in actor-network theory(e.g.Latour, 1991, 1993; Law and Callon, 1992).Sociotechnical change is described as a process ofshifting assemblies of associations and substitutions, areweaving of elements.

TT consist of a changefrom one sociotechnical configuration to another

Configurations that work cannot easily bebounded from the rest of society in a simple and ob-vious way. Things and skills are part of routines, ofpatterns of behaviour, of organisations. They workonly because they are embedded. Societal functionsare fulfilled by sociotechnical configurations.

In thisperspective technology, of itself, has no power, doesnothing. Only in association with human agency, so-cial structures and organisations does technology fulfilfunctions.

Smith et al. (2005)caution against too muchemphasisinniche-derivedagencyintransitions,arguingthatincre-mental reforms in regimes can lead to radical transformations overthe long periods under consideration. They suggest a typology oftransitionpathways(also,GeelsandSchot,2007),andsuggestwaysthat power and agency could be incorporated more centrally intoMLP analysis.

The allure of the MLP is that it provides a relatively straight-forward way of ordering and simplifying the analysis of complex,large-scale structural transformations

It argues ‘normal’ innovation pat-terns reproduce broad socio-technical regimes. Regime shifts occurthrough inter-linkages and interactions between multiple devel-opments on three levels. These involve the rise of both strongsocio-technical alternatives in nichesandfavourable openingsin regime selection environments. The latter is associated withthe unsettling of regimes arising from landscape developments.

andscapeprocesses include environmental and demographic change, newsocial movements, shifts in general political ideology, broad eco-nomic restructuring, emerging scientific paradigms, and culturaldevelopments.

valuable lessons beinggenerated, supportive institutional requirements becoming betterarticulated, and enrolling commitments from a growing networkof actors (including potential investors and users from more main-stream settings) (Raven, 2006). Niche actors need to performconsiderable cognitive, institutional, economic and political work:they have to be persuasive to a variety of constituencies on dif-ferent terms (Smith, 2007; Hendriks and Grin, 2007).

Niche protection can be affordedthrough lead markets, subsidised projects for research demonstra-tion and learning, or specific cultural milieu of early adoption andexperimentatio

Niches provide ‘protective spaces’ for path-breaking,radical alternatives whose performance may not be competitiveagainst the selection environment prevailing in the regime

Socio-technical regimes constitute themainstream,andhighlyinstitutionalised,wayofcurrentlyrealisingsocietal functions. Change within the regime tends to be incre-mental and path dependent. Regimes also exert a structuring forceupon novel alternatives, which arise in niche spaces. In a Kuh-nian vein, regimes tend to produce ‘normal’ innovation patterns,whilst ‘revolutionary’ change originates in ‘niches’. The implica-tion is that the quality of incremental innovations subsequentlygenerated within a new regime will be radically different to thoseunder the preceding regime. Finally, both niches and regimes aresituated within a broader landscape of social and physical factorsthat provides a macro-level structuring context. Over time, the riseof some regimes can prove quite influential upon broader land-scapedevelopments

Transition scholars are interested inthe broader societal transformations arising from the establish-ment of novel technological regimes, but also how broader societaldevelopments are effectively selecting between possible would-beregimes and exerting pressure on them to adapt or causing themto wither

quasi-evolutionary’ conceptuali-sation of historic transitions in societal functions. It combinessociological appreciation of processes of technology shaping, as inSCOT, with more structural notions of ‘technological regimes’ and‘paradigms’ prevalent in evolutionary economics (Rip, 1995, seeGeels’ paper in this special section). Research emphasises the waysselection environments are anticipated, reinterpreted and (par-tially) transformed by (networked) innovators (Rip, 1992; van denBeltandRip,1987;DiscoandvanderMeulen,1998).Relevantstud-ies are those interested in how new technologies become involvedin wide-ranging processes of social change (e.g. the move from gaslight to electric light (Schot, 1998), from sail to steam ships (Geels,2002), the rise of the turbo-jet in aviation (Geels, 2006), improve-ments in public sanitation (Geels, 2006b).

A sociological sensibility extends the regime notion toembrace institutions (such as regulations and markets), heteroge-neous networks (including devices and people), user relations, andsocial expectations. Mutual reinforcement across these plural pro-cesses creates the structural patterns that shape innovation andcreates trajectories of social development

The systems innovation literature frames its prob-lem as concern for the realisation of ‘societal functions’ throughthe configuration and alignment of heterogeneous socio-technicalelements and processes, and how these restructure over long peri-ods of time

The conceptual understanding of transitionsto new regimes draws upon theories at the interface of innovationstudies and STS. They combine a view on the micro-level processesof constructing new technologies, with a view on emerging macro-and meso-level patterns of culture, organisation, markets, regula-tion and infrastructures (Rip and Kemp, 1998; Schot, 1998; Geels,2002).

The first purpose of thispaper is to explain the logic for this broadening of perspective.

The starting point for a quasi-evolutionary model is the search processes which are embedded in heuristics. These search processes and the variations pursued are heterogenous by their very nature. The products of the search processes encounter selection environments, which have their own dynamics which are only partly determined by the variation process. Actors do try to anticipate and influence the selection environment.

there is no independent variation and selection (the building blocks of the evolutionary model) but rather coevolution of both technology and the selection environment. Technologies appear as actor networks of heterogenous elements. The analysis must then focus on how links are made.

ntent and form are given to technical developments simultaneously with the construction of their context.

The latter concept embraces not only the neo-classical market (structure and size of supply and demand as well as prices) but also all kinds of institutional factors (regulation, relationship between employers and employees, political structure) and even geographical factors.

a set of rules or heuristics that indicate what the relevant problems are and in which direction the solution should be sought.

The division of labor contributes to the black boxing oftechnology in such a way that technology actually appears primarily as a set oftools. This division of labor in making and using tools dates from ancient times,but became very strong with the Industrial Revolution in the eighteenth andnineteenth centuries, and the increasing role of research and development(R&D) in the twentieth century.

Artifacts can be used without an indication of their history andtheir inner working; this is called “black boxing.”

(a) niche accumulation, (b) co-evolution of technologies, and (c) actor-related patterns

Several phases can be distinguished in transitions

iteral connotation ofrelativedhardnessTand to include the material aspects of society

Niches are important, because they provide locations for learning processes

This stability is dynamic

technological niches

Their activities reproduce the elements and linkages in sociotechnical systems

Atechnological regime is the rule-set or grammar embedded in a complex of engineering practices,production process technologies, product characteristics, skills and procedures, ways of handlingrelevant artefacts and persons, ways of defining problems; all of them embedded in institutions andinfrastructuresQ