ERC FUNDED PROJECTS

"Considerable investments have been made in Europe and worldwide in research data infrastructures. Instead of a general lack of data about data, it has become apparent that the pivotal factor that drastically constrains the use of data is the absence of contextual knowledge about how data was created and how it has been used. This applies especially to many branches of SSH research where data is highly heterogeneous, both by its kind (e.g. being qualitative, quantitative, naturalistic, purposefully created) and origins (e.g. being historical/contemporary, from different contexts and geographical places). The problem is that there may be enough metadata (data about data) but there is too little paradata (data on the processes of its creation and use). In contrast to the rather straightforward problem of describing the data, the high-risk/high-gain problem no-one has managed to solve, is the lack of comprehensive understanding of what information about the creation and use of research data is needed and how to capture enough of that information to make the data reusable and to avoid the risk that currently collected vast amounts of research data become useless in the future. The wickedness of the problem lies in the practical impossibility to document and keep everything and the difficulty to determine optimal procedures for capturing just enough. With an empirical focus on archaeological and cultural heritage data, which stands out by its extreme heterogeneity and rapid accumulation due to the scale of ongoing development-led archaeological fieldwork, CAPTURE develops an in-depth understanding of how paradata is #1 created and #2 used at the moment, #3 elicits methods for capturing paradata on the basis of the findings of #1-2, #4 tests the new methods in field trials, and #5 synthesises the findings in a reference model to inform the capturing of paradata and enabling data-intensive research using heterogeneous research data stemming from diverse origins. "

1 944 162 €

Start date: 2019-05-01, End date: 2024-04-30

The statement that individuals’ immediate social circumstances influence how they think and act in the political sphere is a truism. However, both theoretical and empirical considerations have often prevented political scientists from incorporating this logic into analyses of political behavior. In the CONPOL project we argue that it is necessary to return to the idea that politics follows a social logic in order to push the theoretical and empirical boundaries in explaining political behavior. That is, people do not act as isolated individuals when confronting complex political tasks such as deciding whether to vote and which party or candidate to vote for. Instead politics should be seen as a social experience in which individuals arrive at their decisions within particular social settings: the family, the peer group, the workplace, the neighborhood. In what way do parents and other family members influence an individual’s political choices? What is the role of workmates and neighbors when individuals arrive at political decisions? Do friends and friends’ friends affect how you think and act in the political sphere? To answer such questions the standard approach to gather empirical evidence on political behavior based on national sample surveys needs to be complemented by the use of population wide register data. The empirical core of the CONPOL project is unique Swedish register data. Via the population registers provided by Statistics Sweden it is possible to identify several relevant social settings such as parent-child relations and the location of individuals within workplaces and neighborhoods. The registers also allow us to identify certain network links between individuals. Furthermore, Statistics Sweden holds information on several variables measuring important political traits. A major aim for CONPOL is to complement this information by scanning in and digitalizing election rolls with individual-level information on turnout across several elections.

1 621 940 €

Start date: 2016-09-01, End date: 2021-08-31

Pollinator declines in response to land-use intensification have raised concern about the persistence of plant species dependent on insect pollination, in particular by bees, for their reproduction. Recent empirical studies show that reduced pollinator abundance decreases densities of seedlings of insect-pollinated plants and thereby changes the composition of grassland plant communities. Cascading effects on ecosystem functioning and associated organisms are expected, but to which extent and under which conditions this is the case is yet unexplored. Here, I propose a bold, multi-year, landscape-scale experimental assessment of the extent of pollinator-driven plant community changes, their consequences for associated organisms and important ecosystem functions, and their likely contingency on other factors (soil fertility, herbivory). Specifically I will: (1) Set up a network of long-term research plots in landscapes differing in pollinator abundance to measure the changes in plant reproduction over successive years, and assessing experimentally how herbivory and soil fertility mediate these effects. (2) Explore the individual processes linking pollinators, plant communities and ecosystem functioning using long-term experiments controlling pollinator, herbivore and nutrient availability, focusing on a sample of plant species covering both the dominant species and a diversity of functional traits. (3) Assess the context-dependence of pollinator-mediated plant community determination by building and applying process-based models based on observational and experimental data, and combine with existing spatially-explicit pollinator models to demonstrate the applicability to assess agri-environmental measures. This powerful blend of complementary approaches will for the first time shed light on the cornerstone role of this major mutualism in maintaining diverse communities and the functions they support, and pinpoint the risks threatening them and the need for mitigation.

1 998 842 €

Start date: 2019-09-01, End date: 2024-08-31

Oligodendrocytes (OL) are glial cells that mediate myelination of neurons, a process that is defective in multiple sclerosis (MS). Although OL precursor cells (OPCs) can initially promote remyelination in MS, this regenerative mechanism eventually fails in progressive MS. OPCs go through several epigenetic states that ultimately define their potential to differentiate and myelinate. OPCs in progressive MS stall in a distinct epigenetic state, incompatible with differentiation and remyelination. We hypothesize that these OPCs regress to an epigenetic state reminiscent of the state of embryonic OPCs, which remain undifferentiated. In this proposal, we aim to uncover the causes behind the remyelination failure upon disease progression in MS. We will determine the epigenetic/transcriptional states of OPCs during development and in MS, using single cell and bulk RNA sequencing and quantitative proteomics. We will further investigate how the interplay between transcription factors (TFs), chromatin modifiers (ChMs) and non-coding RNAs (ncRNAs) contributes to the transition between epigenetic states of OPCs. The results will allow the identification of ChMs and ncRNAs that can modulate these states and thereby control OPC differentiation and myelination. We will use this knowledge to investigate whether we can reverse the epigenetic state of OPCs in MS, in order to promote their differentiation and remyelination. The unique combination of leading-edge techniques such as SILAC coupled with immunoprecipitation and mass-spectrometry, single-cell RNA sequencing, ChIP-Sequencing, among others, will allow us to provide insights into novel epigenetic mechanisms that might be underlying the effects of environmental and lifestyle risk factors for MS. Moreover, this project has the potential to lead to the discovery of new targets for epigenetic-based therapies for MS, which could provide major opportunities for improved clinical outcome of MS patients in the near future.

1 895 155 €

Start date: 2016-09-01, End date: 2021-08-31