dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). However, the computational burden is significant because BOED requires solving the inverse problem for each possible dataset, as the posterior distribution must first be estimated to compute the Bayesian utility function. Surrogate modeling and Bayesian Model Averaging (BMA) in combination with preposterior estimation are the two main simplifications that have been suggested in the field of Earth Sciences to make posterior computation tractable. Thibaut et al. (2021)  provide an overview of these two simplifications. Iterative approaches requiring multiple forward problem runs, such as Markov chain Monte Carlo  (McMC)  methods, are commonly used to solve the problem  (Laloy & Vrugt, 2012; Vrugt, 2016; Ryan et al. , 2016). Zhang et al. (2015) , for example, use a McMC algorithm to solve the BOED of sampling well locations and source parameters identification of groundwater contaminants, but defined a surrogate for the contaminant transport equation to reduce the computational burden of McMC.
This contribution is unique in five ways when compared to previous applications of BEL to experimental design: 1. We use two different type of predictors, geophysical and borehole data, which vary in time, to predict a four-dimensional target, the temperature field magnitude in the aquifer over time. It will first be demonstrated how to predict the target using each data set separately. 2. Then, it will be demonstrated how to use them together. Because the predictors are of different types, the dimensionality reduction step in the pre-processing section is applied to each instance separately, and then concatenated before being fed to the learning algorithm. 3. The Transport Map method is used to sample in the low dimensional space. 4. The data utility function is calculated in the low dimensional latent space, without the need to back-transform the data to its original space.
Experimental setup We present a method for estimating the posterior distribution of an unknown fourdimensional temperature field during a heat injection-storage-pumping experiment. The experiment has been described in  Lesparre et al. (2019). In short, hot water was injected at 3 m 3 /h into an aquifer at a temperature of 42 °C for six hours, followed by another injection at 14. 5 °C for 20 minutes. Then, it was stored in the aquifer for 92 hours, and then pumped back out for 16 hours and 15 minutes at a flow rate of 3 m 3 /h. The goal was to track the evolution of temperature distribution in the aquifer over time. The experiment was monitored using time-lapse ERT and temperature data in wells. The timevarying predictor (measured voltage for ERT, direct temperature for wells) and the target (temperature distribution in the aquifer) are high-dimensional, and their relationship is non-linear.
It took 38 seconds to initialize the model, load the dataset, pre-process the data, and save the files. The prediction part of the experiment took 27 seconds, bringing the total time to 65 seconds, including the time required to save the results to a file. Input Throughout, the process used no more than 2. 08 GB of RAM. Given that the entire implementation was created for research purposes, the implementation can be enhanced for speed and memory management (e. g. , parallelization, low-level programming language implementation, etc. ). Finally, the computational costs differ depending on the case study and the size and number of data sets used.
Target prediction This section shows how to predict a single four-dimensional temperature field, using three distinct predictors: (i) the geophysical data, made of resistance measurements from 848 quadrupoles over 106 observation time steps, which are combined to form the predictor, (ii) a single temperature profile over 106 observation times, and (iii) the combination of the geophysical data with all 4 borehole temperature curves. We show that BEL can accurately estimate the target posterior distribution with varying uncertainty levels according to the type of predictor used. The dataset in this section has a total size of n = 250. The training set is then reduced to n 80% = 200 models, with the remaining models being used to validate BEL's ability to predict the target, and later on for OED.
Previous BEL applications have demonstrated that making accurate predictions with a dataset of this size is possible  (Hermans et al. , 2016 (Hermans et al. , , 2018 (Hermans et al. , , 2019;; Athens & Caers, 2019; Michel et al. , 2020; J. Park & Caers, 2020; Yin et al. , 2020; Thibaut et al. , 2021). While a small training set size is inevitable due to the time-consuming nature of the simulations, it is sufficient because the prediction is a temperature distribution that varies smoothly in both time and space and results from advection, diffusion, and dispersion processes. Such target is much simpler than the underlying K model. BEL is a Bayesian method that incorporates uncertainty. Therefore, a large training set size is not required and the method is more robust against overfitting. Furthermore, the use of cross-validation ensures the robustness of the results.
, 2014; Lu et al. , 2015; Pezard et al. , 2016; Schmidt-Hattenberger et al. , 2016) , soil properties for agriculture applications  (Blanchy et al. , 2020) , or even erosion  (Masi et al. , 2020)  and ground-loosening processes  (Kim et al. , 2022). We also point out that the approach can be used to combine datasets of any type and any dimension in order to stochastically solve geophysical inverse problems. Although different geophysical methods are complementary, JafarGandomi and Binley (2013) argue that inconsistency in their acquisition geometries (i. e. , 1D, 2D, or 3D) makes the data fusion process difficult. In their inversion strategy, they re-casted the subsurface model to a set of locally layered 1D earth-models to overcome the incompatibility of the acquisition geometries and also to reduce the computational cost of the 2D and 3D inversions.
under the terms of the CC BY-SA 4. 0 License. It includes the data used in the paper (Temperature field monitoring with ERT data) and Jupyter notebooks to reproduce the results. systems. Energies, 7 , 5083-5118. Retrieved from http://www. mdpi. com/ 1996-1073/7/8/5083 doi: 10. 3390/en7085083 Hermans, T. , Oware, E. , & Caers, J. (2016, 9). Direct prediction of spatially and temporally varying physical properties from time-lapse electrical resistance data. Water Resources Research, 52 , 7262-7283. Retrieved from http:// doi. wiley. com/10. 1002/2016WR019126 doi: 10. 1002/2016WR019126 Hermans, T. , Vandenbohede, A. , Lebbe, L. , & Nguyen, F. JafarGandomi, A. , & Binley, A. (2013, 9). A bayesian trans-dimensional approach for the fusion of multiple geophysical datasets. Journal of Applied Geophysics, information as a context-specific measure of uncertainty in groundwa-Lu, C.
Since PM 2. 5 has important health impacts, it is well measured at air quality monitoring networks. The model PM 2. 5 biases at several locations in the United States, Europe, and China are within the 60 %-80 % range. However, some models (CanAM5-PAM, CIESM-MAM7, GEOS-CHEM, GEM-MACH, and Oslo-CTM) show biases larger than 200 %, especially in the western US and Alaska. The large inter-model differences in Fig. 23   The simulated surface-level SLCFs were quite sensitive to the different meteorological conditions such as boundary layer stability and levels of photochemistry, which differed between the two time periods chosen in this study. 2014-2015 was also a time period with more wildfires compared to 2008-2009. For example, according to the CMIP6 emission data that were used in most of the models, emissions of BC from Canadian wildfires in 2014-2015 were 340 % higher than in 2008-2009, whereas the emissions from the USA and Russia were similar for these years.
In CIESM-MAM7, the primary emissions of black carbon (BC), organic carbon (OC), ammonia (NH 3 ), volatile organic compounds (VOCs), sulfur dioxide (SO 2 ), and oxidizing gases (H 2 O 2 , O 3 , OH) are prescribed by the input data uniformly provided by the AMAP-SLCF group. The emission amounts of dust (DU) and sea salt (SS) are calculated online. Aerosol size distributions in CIESM-MAM7 are described by the seven overlapping lognormal distributions, including Aitken, accumulation, primary carbon, fine dust and sea salt, coarse dust, and sea salt modes. The geometric standard deviation of each mode is prescribed (see Table  1  of  Liu et al. , 2012). A simplified gas-and liquidphase chemistry is included in CIESM-MAM7. SO 2 and dimethyl sulfate (DMS) can be oxidized to sulfuric acid gas (H 2 SO 2- 4 ) and then condenses to form the sulfate aerosols, while the evolution of oxidizing gases is not considered.
5 d. As in  Bergström et al. (2012) , the organic matter to organic carbon ratio of emissions by mass is assumed to be 1. 3 for fossil fuel sources and 1. 7 for wood-burning sources. Note that different wildfire emissions were used here, i. e. , from FINN (the Fire INventory from NCAR version 15). The EMEP model runs were driven by 3-hourly meteorological data from the ECMWF IFS model at 0. 5 • × 0. 5 • resolution.
North American wildfire emissions are processed using the Canadian Forest Fire Emission Prediction System (CFFEPS) from satellite-detected fire hotspot data (MODIS, AVHRR, and VIIRS). CFFEPS consists of a fire growth model, a fire emissions model, and a thermodynamic-based model to predict the vertical penetration height of a smoke plume from fire energy (see  Chen et al. , 2019, for details). Biogenic emissions are calculated online in GEM-MACH based on the algorithm from BEIS version 3. 09 with BELD3-format vegetation land cover. Sea salt emissions are computed based on  Gong et al. (2003). The chemical lateral boundary conditions were from MOZART-4/GEOS-5  (Emmons et al. , 2010). The meteorology was initialized daily (at 00:00 UTC) using the Canadian Meteorological Centre's global objective analyses.
A10 GEOS-Chem GEOS-Chem is a global three-dimensional chemical transport model driven by assimilated meteorological observation from the Goddard Earth Observing System (GEOS) of the NASA Data Assimilation Office (DAO), which was first introduced in 2001  (Bey et al. , 2001). GEOS-Chem is a grid-independent model which operates on a 1-D column with default or user-specified horizontal grid points, vertical grid points, and time step. GEOS-Chem Classic can use archived GEOS meteorological data on a rectilinear latitudelongitude grid to compute horizontal and vertical transport and use Open-MP in parallelization. Two types of assimilated meteorological data from the NASA Global Modeling and Assimilation Office (GMAO) can be used to drive the offline mode of GEOS-Chem. The first type are operational data starting from 2012, the GEOS Forward Processing (GEOS-FP, the native resolution of which was 0.
A12 MATCH MATCH -Multiscale Atmospheric Transport and Chemistry  (Robertson et al. , 1999)  -is an offline, Eulerian, 3-D chemistry transport model developed at the Swedish Meteorological and Hydrological Institute. MATCH can be run on global to urban domains to study a range of atmospheric chemistry and air quality problems, but for this study model runs were performed for the 20-90 • N region focusing on long-transport to the Arctic. ERA-Interim reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used as meteorological input to the model. The 6-hourly data (3-hourly for precipitation) were extracted from the ECMWF archives on a 0. 75 • × 0. 75 • rotated latitude-longitude grid. The original data had 60 levels, but the 38 lowest levels, reaching about 16 km in the Arctic, were used in the model.
Image processing and analysis SMLM image stacks (tiff files) were pre-processed in order to remove background fluorescence using a quantile filter computed on a sliding window. Then, localisations were detected using the algorithm described in  [9] , based on a wavelet analysis. Subpixel localisation was performed using the radial symmetry center algorithm introduced in  [10]. Sample drift was corrected by subtracting the displacement that yielded the best correlation between densities of successive temporal slices grouping 10 000 localisations each. To isolate axons, we applied a Sato filter  [11]  with a width of 3 pixels on the logarithm of the pixel-wise mean intensity. Then, we used a local thresholding algorithm, provided by  [12]  to compute a mask over the image. All steps of the analysis were implemented in Python, the code is available at http://gitlab.
3A  and B ). The difficulty of separating the two populations depends on their relative proportions in the two datasets, and we see that both the amplitude of the witness function (Fig. 4C ) as well as the value of the test statistic (Fig. 4D ) decrease as the ratio is closer to 1:1. When the two sets are drawn from the same 50/50 distribution, the test does not, and should not, find significant differences between them. The other main factor determining the difficulty of detecting a difference, is the size of the datasets. Experimentally, changes in biological conditions lead not only to changes in the properties of the random walks. It also leads to changes in the total number of trajectories of a given type. This causes challenges in performing proper statistical testing. To quantitatively assess the effect of both the number of trajectories and the relative proportions belong to different random walk classes, we conducted numerical experiments where we varied these two parameters systematically (Fig.
S1  how kernel bandwidth and shape affect the power of the test. If the kernel bandwidth is too small, this weakens the test by making it too sensitive to noise. Conversely, if its bandwidth is too large, this prevents the test from detecting subtle changes. We tested the effect of the kernel characteristics in the same setting as illustrated in Figs. 4  and S2A , with N = 200 trajectories in each set and comparing sets with 70% fBM / 30% sBM and 30% fBM / 70% sBM. We observed that a Gaussian kernel with radius σ equal to the median pairwise distance in the dataset (i. e. σ between 1. 5 and 2) yields a near-optimal test, in agreement with earlier findings  [4]. Finally, while we have here focused on optimizing type II error while kernel can be adjusted to control either type I or II error while optimising the other  [35].
As illustrated on Fig. 6 , going from a region of the latent space to an intelligible trait of trajectories is rather intuitive, hence the interest of this method to orient further analysis and build biologically relevant hypotheses. Beyond the automation of the analysis procedure between biological conditions, our approach is well suited for exploratory data analysis. The capacity to project individual, differently sized trajectories into finite sized vectors makes it possible to study precise sub-cellular compartments or organelles in a standardised form, and thus allows to test statistical differences between these regions. Hence, recorded single molecule data can be searched in order to detect and characterise regions of the cell that have different statistical properties. This exploration can be done even in regions with different trajectory densities, as is the case for α-synuclein at synapses versus axonal domains.
Acknowledgments We thank  Alexandre Blanc ,  Michael Lelek ,  Sylvain Prigent ,  Mohamed El Beheiry ,  Srini Turaga ,  Raphael Voituriez & Bassam Hajj  for helpful discussions. Fumihiro Niwa  is thanked for technical help. This study was funded by the  Institut Pasteur, L'Agence Nationale de la Recherche  ( TRamWAy ,  ANR-17-CE23-0016  to JBM), the  INCEPTION  project ( PIA/ANR-16-CONV-0005 , OG), and the " Investissements d'avenir" programme  under the management of  Agence Nationale de la Recherche , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ) to JBM & CLV. The funding sources had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Background: The need to more collaboratively measure the impact of health research and to do so from multidimensional perspectives has been acknowledged. A scorecard was developed as part of the Collective Research Impact Framework (CRIF), to engage stakeholders in the assessment of the impacts of health research and innovations. The purpose of this study was to describe the developmental process of the MULTI-ACT Master Scorecard (MSC) and how it can be used as a workable tool for collectively assessing future responsible research and innovation measures. Methods: An extensive review of the health research impact literature and of multistakeholder initiatives resulted in a database of 1556 impact indicators. The MSC was then cocreated by engaging key stakeholders and conducting semi-structured interviews of experts in the field.
The MSC was accomplished as part of the multistakeholder engagement process of the MULTI-ACT project. First, we built an initial database of the health research impact indicators through a meta-review and systematic literature review to explore the existing health research impact frameworks and indicators already being used in the health sector and in multistakeholder organizations. Second, we conducted indepth analysis and classification of the indicators to understand their underlying rationale in terms of the setting in which they were proposed and to evaluate their alignment with the CRIF. Third, as the engagement of various stakeholders lies at the core of RRI and the framework, we identified key stakeholders to adopt a multistakeholder cocreation approach for gathering their perceptions to refine the selection of the most suitable indicators.
Initial collection of impact indicators: meta-review and systematic literature reviews The analysis of the academic and nonacademic literature on health research impact and multistakeholder initiatives informed the development of an initial database of health research impact indicators. The extensive body of literature on impact frameworks suggests various dimensions, measures and indicators for evaluating health research. Hence, we conducted a preliminary review of the reviews (i. e. a meta-review), which focused on analysing previous health research impact frameworks  [30, 31, 36]   Therefore, in addition to the meta-review, we also conducted a literature review on academic and nonacademic research impact studies (not limited to health research) that focused on multistakeholder initiatives between 1992 and 2017.
The search yielded 76 articles and reports. For further analysis, we included only the documents that suggested either specific indicators or frameworks for measuring the multifaceted research impact. The final sample consisted of 19 documents for exploring the indicators in more detail. We specifically collected proposed indicators in documents  [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56]  that used the established or hybrid forms of the frameworks identified in our meta-review. The selection of documents was combined with an additional systematic literature review in the Scopus database to gather other specific impact indicators used in practice in health sector organizations and the pharmaceutical industry. This review aimed to identify the commonly used indicators of health sector performance measurement, paying special attention to organizations and the indicators related to measuring the impact of research and innovation activities.
Indicator analysis for building the database The 1556 indicators used or suggested for measuring the impact of health research that were identified during the first phase were analysed and combined into a database. Some of the proposed indicators were common or similar among the analyses sources, hence indicating their high relevance for the assessment of health research. All indicators were evaluated on the basis of their meaning and purpose within the framework or initiative in which they were embedded, as well as the intrinsic characteristics of the context for which they were proposed. This analysis allowed us to assess whether and how the indicators' underlying rationale was in line with the CRIF philosophy. The payback model taxonomy  [37]  was chosen as a backbone for the indicator classification, as it is a widely recognized framework for measuring the impact of health research  [59, 60]  and its categories were easily associated with the CRIF dimensions.
Additionally, the MSC is integrated into the MULTI-ACT Digital Toolbox (available at https:// toolb ox. multi act. eu) together with other CRIF elements. This online platform and the manual (https:// toolb ox. multi act. eu/ node/ 775) provide more information and examples of the use of the framework and the MSC indicators (see Additional file 2 for details). 1  One specific feature of the MSC is that the PRD enables the science of patient input, that is, when data on people with a disease are used (active and passive contribution) to evaluate the impact of research and innovation. The PRD includes indicators that are reported by patients, family members and caregivers, addressing functional or psychosocial reported aspects such as PROs and indicators to evaluate the return on engagement. The PROs are investigated as indicators that can measure the outcomes that matter most to patients, and they keep patients and stakeholders engaged along the research and innovation continuum.
Instead of providing a "score" or "ranking", the MSC provides information for and feedback from all stakeholders. The final output will help initiatives evaluate their RRI actions and thus understand the multiple impacts that research may produce. The managerial implication of the study is that potential MSC users such as research organizations, institutions and research funders can apply it during different phases and to serve different purposes (see Table  3 ): at the beginning of the project for planning, during the project for monitoring, and at the end for assessing outcomes. The MULTI-ACT digital toolbox is freely accessible and functional online (available at https:// toolb ox. multi act. eu). The development of the MSC has some limitations. First, the selection of its indicators may have been related to conceptualization, that is, data collection methods and operationalization of the impact, which is often a very limiting and complex process.
, and we determined the seven most frequently cited frameworks (i. e. payback model, expected monetary value, Research Impact Framework [RIF], Research Excellence Framework [REF], logic models, Canadian Academy of Health Sciences model, Research Impact Model)that propose indicators and measurement dimensions of health research assessment. The suitability of these frameworks for assessing the impact of multistakeholder initiatives was somewhat limited (e. g. lack of patient engagement and stakeholder participation in defining the indicators, limited coverage of multidimensional impacts or implementation in practice). Digital Toolbox: validated and tested online tool for applying the framework Master Scorecard: 5 CRIF dimensions, 53 measurement aspects, 125 impact indicators Expert consultation and engagement: Semi-structured interviews and focus groups Indicator (n=1556) analysis for building the indicator database: context evaluation, classification, groupingInitial collection of research impact indicators: meta-review and systematic literature reviews.