dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , information and organizational instruments); and cooperation dilemmas to do so with interventions that change pay-offs and understandings (i. e. , economic, command-and-control and information instruments). We did not have strong expectations about alignments between instruments and rules. To test the approach and our conjectures and explore rule linkages we leveraged data from four water management case studies in Europe where government has promoted collective action among local users to tackle a management problem. As illustrated in the analysis, we found an alignment between prisoner's dilemma and conflict situations on the one hand, and economic and command-and-control instruments on the other. This supports the second of our conjectures. Alternatively, we found that organizational and informational instruments were not univocally associated to coordination situations, which goes against our first conjecture and highlights the importance of transaction costs across the board.
Much of the existing interest in big data is therefore focused on commercial applications in specific sectors, particularly healthcare  [13] , through an enhanced understanding of consumer trends  [14] , visualisation of these trends  [15] , and analysing the resulting implications for marketing and ecommerce  [16]. For this study we carried out a systematic keyword search across the web's major scientific databases, including Scopus, Web of Science, and Google Scholar  [17]. For Scopus and Web of Science, which index mainly abstracts and titles, we used the Boolean search term "big data" + "energy model*", while for Google Scholar, which searches full text entries for published works, this was refined to be more specific; we used "big data" + "energy modelling". This exercise was initially carried out in March 2018 and repeated again in December 2018.
The literature review followed a multi-stage process whereby the initial keyword searches were subsequently followed by a human review of all papers to evaluate and classify relevant content, as well as filtering out duplicate entries that appeared in more than one database. In total, 283 unique works were analysed. Of these, 146 were found to have the key search terms located only in their reference lists or to mention them only briefly, without detailed analysis or discussion. A quantitative summary of the remaining 137 papers, coded by thematic area, is presented in Figure  1. The existing scientific literature links big data to energy and technology policy in a few specific domains. The largest group of papers explored energy efficient computing, such as techniques for maximising performance-per-watt in while processing large datasets  [18]  or the potential to reduce energy consumption from datacentres  [19].
The second largest group focused on the possible contribution of big data to achieving an enhanced understanding of the energy performance of buildings (e. g. Zhou and Yang  [20] , Linder et al. [21] , Edwards et al. [22] ) and the new modelling and decision making possibilities this may enable  [23]. The third largest group of publications discussed big data in the context of "smart" cities and urban energy planning, where the collection of large datasets from smartphones and autonomous sensors is an oft-cited example of how a step-change in data availability might transform policymaking and city governance (e. g. Batty  [24] , Rathore et al. [25] ). The fourth significant group investigated the potential of big data for sustainable manufacturing and reducing industrial energy use  [26] , including specific studies on individual processes like steelmaking  [27]  and machining  [28].
Actor behaviour and preferences Decisions made by individuals are typically simplified in models to reflect neoclassical economic assumptions about demand and supply, without integrating more complex insights from fields such as behavioural economics, which are known to be critical  [77]. The Typically, across all data types, it is the power sector and other utilities that hold the most comprehensive and detailed datasets. This particularly the case in those countries with competitive power pools, because a transparent market structure is needed to limit the potential for market manipulation. The high quality datasets for power and other forms of energy production (with the key exception of self-consumption of product, which is considerable in some sectors) usually makes the supply side of the energy system straightforward to characterise when building models.
In contrast however, the resolution of time series information and cross sectional data density for representing energy end-use demand can often be quite poor, even in advanced economies. While many countries have registers or databases on their domestic building stock, it remains difficult to characterise micro-scale patterns in household activity  [82]  in terms of the technologies used, the time of use, and how these differ across regions and demographic groups. The same is true in the commercial and industrial sectors, where energy use is extremely heterogeneous, data availability is typically much poorer, and where there are few centrally held datasets in most countries. Big data potentially offers multiple opportunities for increasing the resolution and coverage of information for characterising energy models.
Potential sources include real-time feedback from electric power grids  [83] , mobility data on travel patterns from public metro transit authorities or private transportation network companies  [84] , smart utility meters in the residential sector  [85] , advanced databases on buildings  [86] , satellite imaging  [87] , and social data from the world wide web  [88]. In private road transport and commercial freight, smartphones or embedded networked sensors could potentially serve as data acquisition tools under the internet of things paradigm, which is a concept that is itself facilitated by big data technologies and concepts  [89]. High spatial and temporal resolution data could potentially give decision makers a clear idea of how energy is being used and produced at a very granular level with very fine time slices.
). They also face a fundamental challenge when attempting to account for sector behaviour that does not maximise household welfare though maximizing consumption and leisure, or maximising firm profits  [9]. We believe that the most promising pathway for the future development of next generation energy economy modelling is to employ big data with bottom-up hybrids  [81]. Examples of this emergent energy system model family include BLUE  [117] , CIMS  [118]  and IMACLIM  [119]. These models combine most of the technical detail of bottom-up models with much of the behavioural realism of top-down models, e. g. CIMS is designed to handle intricate mixes of real world technology regulations and energy/carbon pricing while clearing energy and end-use markets, while IMACLIM does all of the above while going one step further and also adjusts the long run structure of the economy in response.
2). Transforming a bottom-up hybrid into a convincing surrogate model would require very large amounts of revealed empirical data, which would differ highly by sector. As the name implies, bottom-up hybrids build up their representation of the energy system from detailed sectoral sub-models, representing decision making at the point of investment and operation. As a result of this granular-scale detail, they rely on a lot of technical data as well as behavioural parameters (e. g. decision specific discount rates  [123] , end-use demand elasticities  [43] , non-financial welfare costs for modes  [124]  or technologies  [125] ). While open source databases can be built for technical performance and equipment pricing data relatively quickly from manufacturing catalogues, behavioural parameters are notoriously onerous and expensive to gather.
Reasons for caution While the authors acknowledge the potential of big data technologies to challenge some of the long standing limitations of energy economy models, we are not blind to the potential barriers and pitfalls that face practitioners working in this area. First, there are critical questions to be addressed regarding the costs of data collection and the ownership of the resulting information. Data is central to the modern information economy and is highly prized by private companies who trade on their business intelligence or sectoral expertise. The incentives to maintain private for-profit databases are therefore extremely large. Accordingly, access to this data is unlikely to come at low costs to governments or researchers unless the data owners are effectively compensated. Government may be the only actor with the necessary size and agency to make large strides towards more open data in the public interest, but careful thought is required to understand how to implement large-scale data collection for the public good in order to achieve mutual societal and environmental benefits without violating individual privacy rights.
These include methods for revealing the underlying structures, patterns and relationships in data, such as data mining  [133]  machine learning  [134] , and advanced visualisation  [135] ; as well as combining the resulting multi-source, multi-relational information together into federated or composite databases, in a discipline known as data fusion  [136]. Notable approaches for data fusion and integration include data warehousing  [137] , data virtualisation and "data lakes"  [138]. The skills to make sense of massive heterogeneous datasets are currently in short supply relative to the potential demand  [139] , and may not be widespread in the energy systems modelling community. If the future emphasis in energy economics begins to shift away from theoretically grounded models that are populated with scarce data, and towards analysis that relies more on the discovery and exploration of useful patterns in abundant data, then a major priority for research will be skills development and capacity building to transition towards a more data-intensive approach.
Reasons for optimism For discussion purposes, let us assume that big data privacy concerns could be comprehensively addressed, that the skills and techniques could be put in place to make use of large datasets, and that useful data could be collected in the first instance. Could big data resolve long-standing unknowns in energy economy models? Could conditions of data abundance radically transform the way in which energy economy models are built, used, applied and interpreted, and change how decision makers think about energy and climate policy? We speculate that this could indeed be the case on all counts, although we make no claims about the likely speed with which these developments might unfold. Economic modelling, and economics in general, has been called "an inexact and separate science" for decades, with an often fundamentally different outlook on issues such as truth, replicability, and validation to the natural sciences  [140].
Revolutionising the state-of-the-art Flowing from the findings of this review, we propose a set of short, medium and long term recommendations for future experiments with energy economy modelling and big data. Near-term: Our first, shorter term set of recommendations involves setting up experiments to test how big data can inform critical relationships used in energy economy models. At this stage there will be critical methodological developments and learning needed on both sides on issues such as on the data mining of existing databases to extract useful parameters for models, data curation, and the process of interchanging information between big data sets and energy economy modelling parameters. As a result of the need to prototype and standardise these kinds of working practices between big data experts and energy modelling practitioners, it is prudent to first start with a small set of tractable energy economy research questions.
Could a world of data abundance facilitate ondemand construction of more useful "bespoke" models, built from real world empirical data (Category 1) at the appropriate social, economic and technical level of detail for any given policy problem? Could it be possible to conceive of a data and modelling structure that could adapt dynamically to the research question of the moment? Could scientists invoke semi-autonomous agents to both curate and mine databases  [149] , and have them construct energy models on-the-fly according to user-specified parameters, such as geographic scale, sectors of interest and overall theoretical structure (e. g. a top-down hybrid CGE framework, or a bottom-up hybrid simulation framework)? The possibilities are intriguing, and we encourage the energy modelling community to think beyond the constraints imposed by current modelling paradigms and imagine the cutting edge possibilities that might exist in this space.
Third, energy modellers must be willing to look dispassionately at the basic dynamic assumptions and datasets underpinning their models, and answer honestly whether increased availability of Category 1 and Category 2 data can transform them from being theoretically interesting substitute models to policy useful surrogate models. Finally, there must be a willingness in the energy modelling community to embrace fundamental changes in their modelling paradigms, and to embrace the new spring of data abundance after living through a long winter of data scarcity.
Acknowledgements Funding support for  Francis Li  was provided by the  UK Engineering and Physical Sciences Research Council (EPSRC)  under the projects "Modelling the Political, Societal, and  Regulatory Implementation of the UK Energy Decarbonisation Transition " [Grant  EP/R002096/1 ] and the  "Whole Systems Energy Modelling Consortium (wholeSEM)" [Grant   EP/K039326/1 ]. Chris Bataille  was supported by the  French government  under the framework of the  programme "Investissements d'avenir ", managed by  ANR  (the  French National Research Agency)  under the reference  ANR-10-LABX-01 , and also received support that helped lead to this article under a wholeSEM Fellowship at  University College London  [Grant  EP/K039326/1 ].
Absolute chronology The absolute chronology was based upon 44 14 C AMS dates (Table  1 ) and 26 210 Pb dates for two sections of the profile: (i) 400e28 cm e a Bayesian age-depth model based on 39 14 C AMS dates (Table  1 ), (ii) 28e0 cm -26 210 Pb dates and validated by 4 14 C AMS dates. 14 C AMS dates were provided by the Pozna n Radiocarbon Laboratory (Poland) (Table  1 ). The age-depth model was calculated using the OxCal 4. 3 software (Bronk  Ramsey, 1995)  applying the P_Sequence function with parameters: k 0 ¼ 0. 6, log 10 (k/k 0 ) ¼ 1, and interpolation ¼ 1 cm  (Bronk Ramsey, 2008; Ramsey and Lee, 2013). The IntCal13 atmospheric curve was used as the calibration dataset  (Reimer et al. , 2013). Four 14 C dates, i. e. Poz-92755, Poz-89550, Poz-92754, and Poz-89543, which are significantly different from neighbouring ones were excluded prior to the age-depth modelling.
Numerical analyses and graphical output Threshold Indicator Taxa ANalysis (TITAN) was used to estimate the plant community (expressed as pollen percentages) thresholds to the reconstructed fire activity (see  Baker and King (2010)  for a full description of the method). Briefly, TITAN identified the optimum changing point of every plant taxon frequency and abundance along the MIC gradient using bootstrapping and permutation tests to assess the uncertainty of every changing point  (Baker and King, 2010). TITAN detects changes in species distributions (taxon that increase [normalised z þ scores] or decrease [normalised zscores] in abundance) along a gradient and assesses synchrony among species responses as evidence for community thresholds. The quality and estimation of the critical tipping point area in a plant community (representing trees, shrubs and human indicators) was tested using 1000 permutations and 150 bootstrap replicates (resampling with replacement) using the TITAN2 R package  (Baker and King, 2010).
The reconstruction of statistically significant fire peaks/episodes, background charcoal and fire frequency was undertaken using contiguous macroscopic charcoal data in the application CharAnalysis using local threshold, lowess smoother robust to outliers and a 1000 yrs smoothing window  (Higuera et al. , 2010)  (Suppl. Fig. 1 ). The relationships between pollen relative abundance (%), NPPs (%) and environmental variables (MIC influx, MAC influx (two size groups), CAR and DWT) were assessed using redundancy analysis (RDA). The chosen pollen and NPPs (representing trees, shrubs and human indicators) data were Hellinger transformed  (Legendre and Gallagher, 2001)  prior to the analysis. The significance of the model, axes and of each explanatory variable included in the model was tested using 999 permutations.
Calculations were performed using the vegan package  (Oksanen et al. , 2017 ) using R software  (Team, 2018). Quantitative reconstruction of the TA-based depth to water table (DWT) and pH reconstructions were performed in C2 software  (Juggins, 2003) , using a training set developed for northern Poland by  (Lamentowicz and Mitchell, 2005; Lamentowicz et al. , 2008b). The local training set was used to avoid biogeographical and taxonomic bias  (Lara et al. , 2015; Singer et al. , 2015). Diagrams with palaeoecological proxy data were plotted using C2  (Juggins, 2003)  or Tilia graph  (Grimm, 1992)  software. To adjust the description of the proxy data we divided each diagram into zones based upon the pollen and NPP spectra, as these proxies represent the broadest spatial set of environmental changes.
Zonation was performed using constrained cluster analysis (CON-ISS) applied in the rioja package  (Birks and Gordon, 1985; Bennett, 1996; Juggins, 2017; Juggins, 2017 Juggins, , 2017)). To draw the synthesis figure, DataGraph was used  (MacAskill, 2012). The final graphical output was edited using Affinity Designer (affinity. serif. com).
A stable presence of cultivated land pollen indicators suggests locally intensive human impact, therefore humans may have been responsible for the P. sylvestris retreat. During this stage Tilia cordata and C. avellana also gradually retreated. In Stage G3 the MIC influx records high and stable values of ca. 10,000 particles/cm 2 /yr. The higher abundance of microscopic charcoal suggests extra-local fires, which may be connected with forest clearance at ca. 3200-3100 cal. BP (a decrease in percentages of arboreal pollen and increases in cultivated land/ruderal taxa). The highest values of macroscopic charcoal were also recorded at the beginning of the zone and three fire episodes are noted at ca. 3360 and 3210 cal. BP. Later fire activity decreased, and the following peak was recorded at the end of the zone, at ca.
J) Statistically significant fire peaks/episodes calculated with the software CharAnalysis  (Higuera et al. , 2010). J) MAC -macroscopic charcoal influx (presented as sum of particles >500 mm and 100e500 mm) (particles/cm 2 /year). K) MIC -microscopic charcoal influx. indicator of inundation which would suggest that this peatland possessed long-term high water table with additional nutrient inputs  (Lamentowicz et al. , 2008b). The contrast between NPPs and TA is highly intriguing, but it also suggests that small kettle hole peatlands with small catchment areas respond sharply to snowmelt and higher precipitation which result in flooding. They are also closely related to changes in the catchment conditions; therefore, land use features may modify the hydrological signal. In this sense they are closer to lakes in their signal than to large raised bogs which are expected to store the regional climatic signal  (Charman, 2002).
It was also intermittently crossed ca. 6000 and 5500 years BP, when it was natural or possibly connected to the activity of the Meso-and Neolithic societies. We compared existing archaeological data and our multi-proxy palaeoecological indicators of human impact. In total, 19 sites situated within a radius of approx. 7 km from Głe ˛boczek were identified based upon archival research, as well as the available literature (all mentioned archaeological sites are presented in Fig. 1  and Supplementary Table  1 ). Archaeological reconnaissance field surveys undertaken during the last years at nearby Lake Czechowskie (Fig. 1 ) served to document the presence of a few sites situated in the Iwiczno village.
Conclusions We provided a new proxy-based reconstruction of the past environmental change and human impact in N Poland based on the densely-dated peat profile. The archaeological data confirm a human presence in the Głe ˛boczek area as early as the Neolithic. This peatland connected with the wetland system of the nearby lakes experienced several critical transitions and bog-fen-bog shifts which imply connection and disconnection from the local ground water tables. Using the charcoal and pollen data we determined that the local pollen-based community threshold for fire intensity (measured as micro charcoal influx) is ca 7500 particles/cm 2 /year. We discovered that it was also important tipping point for the divergence between plants related positively (e. g. human indicators, Carpinus betulus) or negatively (e.
Acknowledgements The research was funded by a grant from the  National Science Centre (Poland)  ( No 2015/17/B/ST10/01656 ). The work was also made in the framework of the  National Programme of Development of Humanities project  ( No 2bH15015483 ) as well as budgetary sources for scientific activity in 2016e2019, project number  0342/ IP1/2016/74. V. E. J. J. was supported by the  French National Research Agency  ( MIXOPEAT  project, grant number  ANR-17-CE01e0007 ). We thank  Julie Loisel  help with the calculation of the peat carbon accumulation rates. We thank also  Jerzy Sikora  and  Paweł Zawilski  for defining the chronology of the potsherd found during field surveys in the Głe ˛boczek vicinity, and  Sambor Czerwi  nski for constructing the lidar terrain map of the study area.
IHH cells were exposed for 1 h to selective inhibitors of mitochondrial complexes, followed by 24 h exposure to AZA1 (A), -2 (B) or -3 (C), and the effects evaluated by the MTT assay. The results are expressed as % with respect to the untreated controls (for AZAs alone) and to untreated controls that were exposed to the specific inhibitor (for AZAs in association with the inhibitors). Data are the means ± SE of five independent experiments performed in triplicate and they are reported as % of untreated control. Statistical differences: *, p < 0. 05; **, p < 0. 01; ***, p < 0. 001 (two-way ANOVA and Bonferroni's post test).
To this aim, we carried out an in-depth search on the Model Organism Protein Expression Database (MOPED)  [27]  to select the main K + and Cl -transporters that are expressed in hepatocytes. Among the K + transporters, the ATP-sensitive inward rectifier K + channel (K ATP channels, also known as Kir6. x), the human ether-a-go-go-related gene channel (hERG, also known as Kv11. 1), and the Na + /K + ATPase were considered. As shown in Figure  5 , all of the inhibitors significantly antagonized the effects of AZAs, further supporting a role for K + ions in mediating the toxin effects. AZA2 has been previously shown to increase the hERG levels on the surface of hERG Chinese Hamster Ovary recombinant cells after 12 h exposure  [28]. Moreover, a possible involvement of these channels in mediating AZAs cytotoxic effects has been suggested in a study showing that these toxins can act as hERG channels open-state blockers in the HEK-293 cells  [29].