dataesr/scientific-paragraphs-categorization huggingface dataset:
Gridded data All gridded EUPPBench data is provided on a regular grid of 0. 25°× 0. 25°corresponding roughly to a 25 km horizontal resolution at mid-latitude. As aforementioned, the forecasts and reforecasts are provided by the ECMWF forecasting model in operation at the moment of their issuance. They have both been re-gridded from the ECMWF original ensemble forecasts O640 (or O1280 for the deterministic forecasts) 2 grid to the regular grid using the ECMWF MIR interpolation package  (Maciel et al. , 2017) , provided automatically by the MARS archive system. This re-gridding was done to be in line with the resolution of the ERA5 reanalysis  (Hersbach et al. , 2020)  which provides the gridded observations of the EUPPBench dataset. We recognize that gridded observational datasets over the study domain exist for specific variables that are more accurate than ERA5.
For instance, in the case of precipitation-related variables (like the total precipitation contained in the dataset at hand), ERA5 has been shown to provide -compared to other datasets -a poor agreement with stations observations  (Zandler et al. , 2019) , mixed performances when used to derive hydrological products  (Hafizi and Sorman, 2022 ), yet, good results when using Perfect prog downscaling methods  (Horton, 2022). Notwithstanding, we emphasize that the goal of this gridded dataset is to provide a representative "truth" for the purpose of benchmarking of postprocessing methods. Additionally, the availability 1 Commonly abbreviated as "EFI". 2 The ensemble forecasts grid O640 has a horizontal resolution of 18 km while the deterministic forecasts grid O1280 has a 9 km horizontal resolution.
Parameter name Level Short name Units Temperature 850 t K U component of wind 700 u m s -1 V component of wind 700 v m s -1 Geopotential 500 z m 2 s -2 Specific humidity 700 q kg kg -1 Relative humidity 850 r % Remark: Only gridded observations (reanalysis) are available for these variables. The land usage is extracted from the CORINE2018 dataset (Copernicus  The EUPPBench dataset at station locations consists of the ECMWF forecasts and reforecasts at the grid point closest to the station locations and the associated observations, matched for each lead time. As shown in the Table  2 , there are mainly 5 variables currently available: 2 metre temperature (t2m), total cloud cover (tcc), visibility (vis), total precipitation (tp6) and 10 135 metre wind gust (10fg6). More observation variables will be added in subsequent versions of the dataset.
Static data and metadata In addition to the forecasts and reforecasts, auxiliary fields are provided, such as the land usage or the surface geopotential which is proportional to the model orography (see Figure  2 ). Table  5  synthesizes this part of the dataset. These constant fields have been extracted and are also provided in the stations metadata. year Dimension to identify the year in the past, year=1 means a forecast valid 20 years ago at the reforecast day and month, year=20 means a forecast valid one year before the reforecast date. Only valid for reforecasts.
step Step of the forecast (the lead time). surface Layer of the variable considered (here there is just one, at the surface). isobaricInhPa Pressure level in hectopascal (or millibar). valid_time Actual time and date of the corresponding forecast data. Remark: Bold metadata denotes dimensions indexing the datasets. Depending on the kind of dataset, dimensions and different information are embedded in the data: For gridded data, the metadata available in the forecast, reforecast and observation datasets are detailed in Table  6. For station data, the forecast and reforecast metadata are detailed are detailed in Table  7 , while the observation metadata are detailed in Table  8. For all data, attributes specifying the sources and the license are always present.
Postprocessing benchmark 145 To illustrate the usefulness of the EUPPBench dataset, a benchmark of several state-of-the-art postprocessing methods -and many of which are currently in operation in NMSs -was performed, along with some more recent and more advanced methods. This first exercise was based on a small subset of the dataset. Along the same line, the verification process of this benchmark also focused on some general aspects typically considered for operational postprocessing. In this section, we describe the general framework that we used to conduct this benchmark. The following sections will be devoted to the methods and the 150 results obtained. We begin by detailing the design of this experiment.
Experiment design The postprocessing benchmark at hand considers the correction of the ensemble forecasts of the 2 meter temperature at the nearest forecast grid point from every station available in the dataset, spanning several European countries and the whole EUPPBench area. We note that this area includes orographically difficult regions, nearly-flat plains, and also stations close to 155 the sea or located on islands. Discrepancies may therefore occur between forecast and observations due to poor observational representativity at the scale of the model or to challenges in the model representation of a wide range of physical processes. We note also that due to the coarse nature of the gridded forecast dataset, the forecast grid points are not evenly situated with respect to the stations they represent, with sometimes huge differences in elevation or situation (e.
step Step of the forecast (the lead time). time Forecast or reforecast date (reforecasts are only issued on Mondays and Thursdays). Remark: Bold metadata denotes dimensions indexing the datasets. Within this simplified benchmark exercise, the only predictor that could be used to perform the postprocessing is the temperature at 2 meter itself. Additionally the use of the (static) metadata was allowed and some methods used latitude and longitude, elevation, land use, model orography, and also the day of the year. The 11-member reforecasts produced during the 2017-2018 period were considered as training data while the 51-member forecasts for the same period was used as test data for verification. This setup introduced some challenges for the implementation of some of the postprocessing methods described below.
To avoid a potential overlap between the reforecasts and the forecasts, the forecast from 2017 that were included in the reforecasts of 2018 have been removed from the training dataset. Since the ECMWF reforecasts date are being produced each Monday and Thursday, the reforecasts (per lead time) for 2017 do not overlap with those of 2018. However, one notable difference between the training data and the test data is the number of ensemble members: ensemble forecasts contain 51 members while ensemble reforecasts include 11 members. Also, note that in both cases, the ECMWF control run forecast is included in the ensemble (as the 0-th ensemble member). The high-resolution deterministic forecast runs were not used nor postprocessed in the current benchmark.
Postprocessing methods Along with the dataset and verification framework described above, the present work further includes a collection of forecasts of exemplary postprocessing methods along with corresponding code for their implementation. Note that with providing forecasts of a selected set of methods, we do not intend to provide a comprehensive or systematic comparison to establish the "best" approach, but rather aim to present an overview of both commonly used and more advanced methods ranging from approaches from statistics to machine learning. Those can be used in subsequent research for developing extensions to existing approaches and for comparing novel methods to established baselines. Short descriptions of the methods available in the present benchmark are provided below, verification results are presented in Section 5.
Ensemble model output statistics (EMOS) EMOS is a parametric postprocessing method introduced in  Gneiting et al. (2005). The temperature observations are modelled by a Gaussian distribution. The location (µ) and scale (σ) parameters of the forecast distribution can be described by two linear regression equations via   et al. (2017). The implemented EMOS version is based on the R-package crch  (Messner et al. , 2016)  with maximum likelihood estimation. 51 equidistant quantiles between 1 and 99 % of the distribution are drawn to match the amount of members from the raw ECMWF forecasts, which were needed for verification. EMOS is applied separately to every station and lead time. y(t) ∼ N (µ, σ)      µ(t) = β EMOS 0 + f EMOS 1 (doy) + β EMOS 1 µ ens (t) log(σ) = γ EMOS 0 + g EMOS 1 (doy) + γ EMOS 1 log(σ ens (t)), (2).
Distributional regression network (DRN) Rasp and  Lerch (2018)  first proposed the use of neural networks (NNs) for probabilistic ensemble postprocessing. In a nutshell, their DRN approach extends the EMOS framework by replacing pre-specified link functions with a NN connecting inputs and distribution parameters, enabling flexible nonlinear dependencies to be learned in a data-driven way. The parameters of a suitable parametric distribution are obtained as the output of the NN, which may utilize arbitrary predictors as inputs, including additional meteorological variables from the NWP system and station information. In our implementation for EUPPBench, we closely follow  Rasp and Lerch (2018) , and assume a Gaussian predictive distribution. We fit a single DRN model per lead time jointly for all stations, and encode the station identifier and land-use via embedding layers to make the model locally adaptive.
CRPS (K) Spread-error-ratio 1 0 0 1 0 0 0 3 0 0 0 1 0 0 1 0 0 0 3 0 0 0 1 0 0 1 0 0 0 3 0 0 0 1 0 0 1 0 0 0 3 0 0 0 1 0 0 1 0 0 0 3 0 0 0 1 0 0 1 0 0 0 3 0 0 0 1 0 0 1 0 0 0 3 0 0 0 1 0 0 1 0 0 0 3 0 0 0 1 0 0 1 0 0 0 3 0 0 0 While the authors constructed and performed this benchmark, some lessons were learned along the way: -As much as possible, avoid to maintain an archive or a database of scores for the experiments. Instead compute verification results for the experiments on the fly and only store the summary results. This has the advantage that you can easily add (or remove) scores, summaries of scores, without going through the complex process necessary to update an archive (with new submissions and additional scores). -Either be very strict about the format of submitted predictions, or use software that is aware of the NetCDF data model and that can handle slight inconsistencies (e.
g. re-ordering of dimensions or dimension values) -Quality control is imperative: while the verification results generally quickly indicate whether there are any major issues with the submitted predictions, issues may already arise earlier than that (making a verification impossible). Catching these errors and establishing a feedback loop with the submitters is important. One way to solve this with NetCDF format is to check the NetCDF header of the submission for format compliance. These points are important for the next EUPP projects, which will aim to harness the full potential of this dataset, by postprocessing other, less predictable variables (e. g. rainfall, radiation), on station and gridded data, and by allowing many predictors instead of only the target variable itself, as it is the case in the setup of the methods presented here.
Subdaily station observations have been provided by many National Meteorological Services (NMS) participating in the construction of this dataset, and a big part of the station data can be considered as open data (see section 7). The observations of 234 stations cover the entire 22-year time period 1997-2018 necessary to match the reforecasts and forecasts. The elevation of these stations varies from a few meters below the sea level up to 3562 meters for the Jungfraujoch station in Switzerland. These 130 stations constitute the most authoritative sources of information about weather and climate provided in each of the involved countries, being constantly monitored and the quality of the data being checked.
https://doi. org/10. 5194/essd-2022-465 Preprint. Discussion started: 12 January 2023 c Author(s) 2023. CC BY 4. 0 License. locations within the test dataset (2017-2018) are compared here with observations. We use two metrics that quantify the forecast quality, or in other words, how well the ensemble forecast matches the observations: the Bias and the Continuous Ranked Probability Score (CRPS) Hersbach (2000). Bias addresses the skill of the ensemble average and corresponds to the average differences between forecast and observation. CRPS, on the other hand, addresses the probabilistic skill by comparing the Cumulative Distribution Functions (CDF) of the forecast against the corresponding observation. The calibration of the ensemble is analyzed via a spread/skill score equal to the ratio of the average ensemble standard deviation divided by the rootmean squared error of the ensemble mean.
As a reference forecasting dataset the raw IFS ensemble at the nearest forecast grid point from every station is used with an additional lapse-rate correction of 6. 5°C/km to account for the difference between the IFS orography and the station elevation. Some results are also presented conditioned on the station elevation to detect remnant orographic influences. The verification results for the different postprocessing methods are obtained after performing quality-control tests on the initial data to detect for possible inconsistencies, unrealistic values and missing data. Missing postprocessed predictions for individual time steps and locations in the test set are replaced by the direct model output (DMO). Postprocessing methods with.
https://doi. org/10. 5194/essd-2022-465 Preprint. Discussion started: 12 January 2023 c Author(s) 2023. CC BY 4. 0 License. 4. 1 Accounting for systematic and representativeness errors (ASRE) ASRE postprocessing tackles systematic and representativeness errors in two independent steps. A local bias correction approach is applied to correct for systematic errors. For each station and each lead time, the averaged difference between reforecasts and observations in the training dataset is computed and removed from the forecast in the validation dataset. The difference averaging is performed over all training dates centered around the forecast validity date within a window of ± 30 days.
1) https://doi. org/10. 5194/essd-2022-465 Preprint. Discussion started: 12 January 2023 c Author(s) 2023. CC BY 4. 0 License. by optimizing the CRPS, separately for each station and for each lead time. T ens m (t) = T ens m (t)µ ens (t) here denotes the deviation of the member m from the ensemble mean. The results were obtained with the Pythie package (Demaeyer, 2022a), training on the 11 members of the training dataset to obtain the coefficients α MBM , β MBM and γ MBM , and then using them to correct the 51 members forecasts of the test dataset. One of the main advantages of MBM postprocessing is that -by designit preserves correlations in the forecasts.
As discussed in  [43] , building the ranking using anti-lex-cel takes O(2 2|N | ) in the worst case (and when all norms are beneficial). Nonetheless, in  [43]  we show that it is possible to avoid the explicit computation of a whole ranking over norm systems. Indeed, in  [43]  we show how to cast the problem of selecting the most preferred norm system as an optimisation problem that can be encoded as a BIP (Binary Integer Program). This BIP only employs |N | decision variables and can be solved with the aid of standard BIP solvers (e. g. CPLEX  7  or Gurobi 8  ). We propose following the same approach here. Figure  1  shows the steps of our computational approach to compute the most value-aligned norm system. First, given a set of norms and a value system as input, we apply MC-lexcel to obtain a norm ranking.
Acknowledgements Research supported by projects: COREDEM ( H2020-785907 );  Crowd4SDG  ( H2020-872944 );  CI-SUSTAIN  ( PID2019-104156GB-I00 ); TAILOR ( H2020-952215 );  COMRDI18-1-0010-02 ;  2021 SGR 00754 ;  2021 SGR 000313 ;  THEMIS  ( ANR-20-CE23-0018 ); " FairTransNLP-Language: Analysing toxicity and stereotypes in language for unbiased, fair and transparent systems " ( PID2021-124361OB-C33 ) funded by  MCIN/AEI/10. 13039/501100011033/FEDER , UE ;"AUTODEMO: The stealth side of participatory democracy: process preferences towards automated decision-making" code  SR21-00329 ; " VAE: Value-Awareness Engineering "  TED2021-131295B-C31  funded by  MCIN/AEI /10. 13039/501100011033  and by the  European Union NextGenerationEU/PRTR ; and  VALAWAI  project, funded by the  European Commission  under Grant # 101070930.
It can be accessed freely on the URL reducirriesgosenquito. com. This article is based on the reflections of the authors (Tomorrow's Cities researchers, Sindicato Audiovisual, and FMC) on the process of creating the three interrelated museum exhibits and the online platform. Additionally, to understand whether our outputs enabled a greater understanding of DRR in the general population of Quito, we collected evaluation data documenting this interdisciplinary and collective process both in the museums and through the online platform contributing to secondary school education following the Monitoring, Evaluation and Learning methodology to apply critical thinking in designing, implementing and evaluating research projects that seek social impact and change  [42]. Visitor surveys were conducted in all three museums where the exhibition took place (104 respondents in the City Museum, 32 in the MIC, and 93 in the Carmen Alto Museum).
Interdisciplinary research to convey the complexity of risk causes, drivers and trajectories Both the museum exhibits and the digital platform "Reducir Riesgos en Quito reflected these multiple opportunities for deepening knowledge about DRR, discussing risk and proposing ways to avoid building more risk in the city we live in from an interdisciplinary standpoint. We conveyed the results of the research done in the forensic analysis of multi-hazard risk in Quito using history the social and the physical sciences. This research including multi-hazard assessments, historical event databases, historical research on testimonies of past events and their representation in art, video, photographs and literature, the history of DRR related policies, images of urban expansion seen in historical urban maps, historical satellite images, theories of the earth including plate tectonics, historical newspapers, political and socio-economic analyses of historical urban development, etc.
The preliminary workshops conducted with the pilot school enabled Sindicato Audiovisual to adapt the design the content of the platform according to students' expectations and language. For example, the design of character of Andesita took into consideration the students' input, and their familiarity with animated content (both Asian anime and American cartoons) and Youtubers. Following workshop conversations as the platform was being built, Sindicato Audiovisual adapted the structure of the platform to the students' needs, keeping the non-linear and exploratory elements, but adding a menu and other tools to simplify the learning experience. We also used surveys to incite reflection about political action, and we presented "future scenarios from historical examples," such as utopian (or dystopian) situations like a fictional eruption of Cotopaxi volcano in 2042, based on data from the 1877 eruption and population projections (Fig.
With the rapid accumulation of biodiversity data, data integration has emerged as a hot topic in soil ecology. Data integration has indeed the potential to advance our knowledge of global patterns in soil biodiversity by facilitating large-scale meta-analytical studies of soil ecosystems. However, ecologists are still poorly equipped when it comes to integrating disparate datasets. In recent years, knowledge graphs have emerged as a powerful tool for integrating large amounts of distributed heterogeneous data while making these data more easily interpretable by humans and computers. This paper presents a practical approach to constructing a biodiversity knowledge graph from heterogeneous and distributed (semi-)structured data sources. To illustrate our approach, we integrate several datasets on the trophic ecology of soil organisms into a trophic knowledge graph and show how both explicit and implicit information can be retrieved from the graph to support multi-trophic studies.
Introduction In recent years, a number of initiatives aiming at collecting new soil biodiversity data or assembling existing datasets have emerged, resulting in a rapid accumulation of data in soil ecology  [1]. Because of the enormous phylogenetic, taxonomic and functional diversity of soil organisms, datasets are often collected by individual scientists or small project teams from different communities or disciplines to answer precise research questions. These datasets are typically small, with a limited spatial/temporal/taxonomic coverage, and are formatted according to the project needs, with little or no concern for data standardization  [2]. This causes datasets to be heterogeneous in semantics (differences in terminologies, meaning or interpretation of data in different disciplines or research contexts), schema (differences in data structures and formats) and syntax (differences in models or languages).
In addition, datasets are widely distributed: they reside on diverse locations, e. g. files or databases on the local network or published on the web, and are accessible using different interfaces, e. g. , files downloads, database queries or web APIs. Integrating these 'long-tail data' dispersed across different datasets could help address research questions at larger scales  [3]. Data integration is of growing interest in the ecological domain, with much effort directed towards the creation of standard terminologies for describing, sharing and facilitating the aggregation of biodiversity data, e. g. organismal trait data  [4] [5] [6] [7] , into large open databases. Recent initiatives in trait-based ecology have targeted specific taxonomic groups, e. g. ants  [8] , spiders  [9] , soil invertebrates  [10, 11] , fungi  [12, 13] , plants  [14].
Although these databases have made aggregated data more readily accessible to scientists, they are not yet interoperable. The difficulty of integrating data distributed across heterogeneous sources remains. As a result, integrative analyses of soil communities that span several taxonomic groups and integrate multitrophic interactions are scarcesee Ref. [15]  for an examplealthough essential to improve our understanding of the links between soil biodiversity and ecosystem functioning  [16]. Here, we address the problem of semantic data integration in the biodiversity science domain. Data integration is defined in Ref. [17]  as the process of combining data residing at different sources, and providing the user with a unified view of these data. (Fig. 1 ). As a result, the user has the ability to seamlessly manipulate data from multiple sources, regardless of the original format or location of the data.
These data are instances of the general concepts (classes) defined in the ontology. For example, if we were to define a class 'Article author' in a hypothetical ontology to describe the concept of 'people who write scientific papers', then 'Nicolas Le Guillarme' and 'Wilfried Thuiller' would be two instances of this class. In the context of semantic data integration, the data layer of a KG is populated with instance data from multiple sources. The ontology is used to link these disparate datasets at the schema-level, acting as a mediator for reconciling the structural and semantic heterogeneities between data sources. KGs have a number of advantages over other types of databases, such as relational ones. Their graph structure allows for efficient querying, intuitive visualization, and analysis using graph algorithms or relational machine learning  [18].