dataesr/scientific-paragraphs-categorization huggingface dataset:
This will enable subsequent prioritization of actions to fill identified data-related gaps in line with needs of relevant stakeholders (Action Items 5 and 6). Another short-term goal to improve exposure data generation and uptake in Europe is to increase interoperability of exposure data by promoting harmonised terms and vocabularies (Action Item 2). This has already been initiated by numerous stakeholders. The glossary for exposure science developed by ISES Europe can serve as a starting point for data-related terminology  (Heinemeyer et al. , 2021) , but alignment of vocabularies across data repositories is also necessary. For example, the NORMAN Association considers StdInChIKeys to be the best alternative to the conventionally used chemical names and CAS registry Numbers. This work will also encourage journal requirements for data publication to follow more standard requirements for terminology and associated data/metadata  (Vines et al.
For example, data storage and related infrastructure for exchange and reuse of exposurerelated data has been initiated through the European Data Spaces, EOSC, and EU Open Data Portal as described in section 3. 3. In Europe cofunded partnerships such as HBM4EU and the European Partnership for the Assessment of Risks from Chemicals (PARC) are promoted to support collaboration between European Commission Services, EU Agencies and national public organisations that conduct risk assessment and/or regulatory activities, and the contributing research communities. The PARC partnership is a joint research and innovation programme under development to strengthen the scientific basis for chemical risk assessment in the EU by bringing risk assessors and managers together with scientists to accelerate method development, data and knowledge generation, and to facilitate the transition to next generation evidencebased risk assessment.
This meets many of the goals in our action plan. The PARC data collection will be interoperable with the main data repositories of EC Services and EU Agencies (IPCHEM and databases of ECHA, EFSA, and EEA) and PARC will develop tools and methods for combining and integrating heterogeneous data (e. g. , environmental exposure, human biomonitoring, and different types of (eco)toxicity data) from available databases (PARC, 2020). Additionally, EU-OSHA started a pilot study in six EU Member States to collect data on occupational exposure to cancer risk factors  (Riedmann et al. , 2018) , has published a data-driven methodology to assess exposure to dangerous substances in work places  (Basinas et al. , 2019) , and collaborates with the JRC to integrate national occupational exposure measurement data into IPCHEM.
The parallel development of such infrastructure and a common European platform for harmonised, regularly-updated dataset information, including newly discovered environmental pollutants, will enable prioritization of pollutants that may cause adverse environmental effects at the local, national, and European scale in support of European water and chemicals management policies  (Dulio et al. , 2020; Slobodnik et al. , 2019). However, it should be noted that these advances require not only conceptual and technical innovation in monitoring and assessment but may also demand better coordination of European chemical legislation  (Kortenkamp et al. , 2019) , changes to the Water Framework Directive  (European Commission, 2000) , and an increase in cooperation, and crosscompliance of chemicals and water regulations  (Brack et al.
(f) Stimulate and streamline cooperation between Commission Services, EU agencies (ECHA, EFSA, EEA), EU Member States authorities and regulatory bodies, citizen science initiatives and organizations, industry, and the broader exposure science community. This is of the utmost importance for building and progressively boosting a FAIR data culture enabling open science and enhancing exchange of data and information between different stakeholders and policy domains. In this context, interoperability and long-term sustainability of databases, hubs, and platforms is a key requirement that should be promoted and enforced. Further, to successfully operationalise this framework requires careful evaluation of the costs and tradeoffs in standardisation and harmonisation and to identify a suitable level and structure of exposure data necessary to support exposure and risk assessment processes and decisions within and across regulations.
In this context, we propose the framework be implemented: (a) Continuing to push for harmonisation and standardisation of data formats, vocabularies, etc. This is essential to minimize efforts across regulations based on a clear "common" understanding of data characteristics and requirements (i. e. avoiding double efforts). (b) Continuously reviewing and refining the decision contexts and related requirements for exposure data and their suitability to support these decisions. (c) With continued learning from successful models of data repositories development and applications in other science fields to guide efforts for harmonising exposure data and databases.
Acknowledgements The review of the manuscript by  Dr Antonio Franco  ( EC, DG JRC ) and  Dr Alessandro Carletti  ( EC, DG JRC ), is gratefully acknowledged. This study was executed by the  ISES Europe working group  on "Data repositories & analytics", kicked-off during the 3 rd European Exposure Science Strategy Workshop organised and supported by the  European Commission 's  Joint Research Centre on 30/31 Jan 2020 in Ispra, Italy  ( JRC  project  no 30032: DTHC -Digital Transformation of Health and Care ). The study was financially supported by the  "Safe and Efficient Chemistry by Design (SafeChem) " project funded by the  Swedish Foundation for Strategic Environmental Research  (grant no. DIA 2018/ 11 ), by the  SPRINT  project (grant no. 862568 ) funded under the European Union's  Horizon 2020 Research and Innovation program , and by the  PARC  project (grant no.
org/communitie s/efsa-kj ECHA REACH European Existing data • Stressor properties database Chemicals Agency repository (physiochemical (ECHA) properties, fate, human toxicity, ecotoxicity) • Stressor use data (exposure, emissions, use patterns, use tonnage bands) NORMAN NORMAN Existing data • Monitoring data Database Association repository (environment, System (NDS) biota) https://www. • Stressor properties norman-networ (physicochemical k. com/nds properties, ecotoxicity) • Stressor use data (use category) World United Nations Existing data • Monitoring data Environment Environment repository (environment, Situation Room Programme biota) https://wesr. (UNEP) • Receptor unep. org information (demography/ lifestyle, physical/ technical environment characteristics) Exposome- International Existing data • Monitoring data Explorer Agency for repository (human) https://exposo Research on • Receptor me-explorer.
uc. pt Human Exposure European Centre Existing data • Stressor tools (fate/ Assessment for Ecotoxicology repository exposure, Tools Database and Toxicology of estimation tools) (heatDB) Chemicals https://ecetoc. (ECETOC) org/tools/ec etoc-heat-db EXPOsOMICS Thirteen of the Data- • Monitoring data https://exp world's leading generation (air pollutants, osomics-project. organizations in initiative water eu the field contaminants exposure data) linked to receptor information (biochemical and molecular changes via 'omics' techniques) Health and Sorbonne Data- • Monitoring data Environment- Université generation (Environmental, wide (France) initiative human exposure Associations data) based on Large • Receptor population information (socio- Surveys economic, (HEALS) biomarker and https://cordis.
principles and a framework for data sharing and use/reuse among Establish a Increased data Increase uptake of Data providers, framework for transparency and new exposure science Exposure stakeholders while respecting protection of data sharing availability, data into policy scientists, intellectual property rights and personal Increased funding bodies (sensitive) data. automation in data management 8 Pooling together and sharing best practices for Pooling together Enhanced data Increase uptake of Practitioners, data integration and interpretation, standardised and sharing best storage and related new exposure science Data providers, and harmonised datasets and data formats used practices infrastructure, data into policy Exposure in connection with tools and models and Increased data scientists performance indicators that are used in: policy integration making, exposure and health risk assessments, surveillance and monitoring activities, evaluation of intervention measures and health information systems.
Therefore, quality control is very crucial to enhance the data analysis and the relevance of the noise maps. Most quality control methods require a reference database to train the models. In the context of NC, this reference data can be produced during specifically organized events (NC party), during which contributors are specifically trained to collect measurements. Nevertheless, these data are not sufficient in number to create a big enough reference database, and it is still necessary to complete them. Other communities around the world use NC, and one may want to integrate the data they collected into the learning database. In order to achieve this, one must detect these data within the mass of available data. As these events are generally characterized by a higher density of measurements in space and time, in this paper we propose to apply a classical clustering method, called DBSCAN, to identify them in the NC database.
e. , a measuring instruments). This idea of using smartphones as acoustic sensors and citizens as contributors emerged at the end of the 2000s with the increasing capabilities of smartphones to perform environmental acoustic measurements  [3]. It was followed by several works that have given rise to specific noise and soundscape crowd-sourcing-type applications and platforms (e. g. , Ear-Phone, NoiseSPY, NoiseTube applications  [4] [5] [6] ) and, particularly, the NoiseCapture (NC) approach  [7, 8] , a part of the Noise-Planet project  [9]. The NC approach consists of measuring noise and additional information along a path and then sharing data with the community (Figure  1 ). The noise data acquired by volunteers (i. e. , the "user" or the "contributor") from all over the world are then stored in a community database.
This database contains the measurement path (a "track" in the NC vocabulary, which is made of "measuring points"), standardized noise indicators, a description of the user perception of noise sources (using "Tags") and of the soundscape quality (using a pleasantness scale), and other useful information, such as the date and time of the measurements, the GPS localization and accuracy, and the speed of the user during measurement. Representation of a NoiseCapture (NC) measurement. After manually activating the measurement from a starting point, the user can move along a path of their choice or stay at the same location. An acoustic measurement takes place every second, which allows us to calculate noise indicators, such as 1 s equivalent sound level and spectrum. Other information, such as the date and time of the measurements, the GPS location of the measurement point, its accuracy, and its speed are recorded at the same time.
At the end of a track, the user stops the measurement and is invited to provide additional information about the presence of specific sound sources (tags) during the measurement, the measurement conditions, his own perception of the quality of the sound environment (i. e. , the pleasantness), and other additional information. If the user has given his consent, the anonymous data are transferred to a remote server, controlled and then integrated into an open database, which is available for free. In addition, the raw data from the entire community are aggregated into a hexagonal based noise map and displayed on a public web page  [10] , as shown in this figure. Graphical contents of this figure are part of the Noise-Planet project website  [11] , licensed under CC BY-NC-SA 4. 0. This data can be relevant for later evaluation of the ambient noise through specific processing, for example, to generate noise maps that could be useful for communities to implement action plans in order to reduce the noise exposure of citizens or to protect quiet environments.
In this way, the use of NC maps would be consistent with the European directive 2002/49/EC except that they simultaneously integrate the multiplicity of noise sources encountered in the environment. In comparison with conventional noise maps, for example, focusing on traffic noise only, those produced with NC data could be closer to reality. Since the launch of the application on 29 August 2017, a large amount of data has been made available, offering the possibility to analyze the data over a large spatial area and a long period of time. This database is distributed under the Open Data License  [9]  and updated every day at 3:30 am (local time in Paris, France). At this date, the database represents the equivalent of more than 1103 days of 1 s measurements (more than 382,000 tracks and 93 million measurement points) spread over more than 200 countries and collected by over 92,000 different contributors.
Data Quality Control and the Need for a Reference Database This amount of data, however, needs to be put into perspective, firstly with regard to the surface of the planet (150 million km 2 for the terrestrial surface) and the time elapsed from the launch of the application (1737 days by 31 May 2022); finally, the density of points in space and over time remains globally very low (less than 0. 4 measurement points per 1000 km 2 by day) except in urban areas where the density of measurement may be more important. In addition, the quality of the collected data may sometimes be questioned. A recent analysis  [8]  showed that data may suffer from several problems due to the technical performance of the smartphone, respect for a relevant measurement protocol, the lack of smartphone calibration, the misuse of the application, and insufficient GPS accuracy.
The evaluation of the quality of the collected data is therefore essential to characterize the relevance of the noise maps and the analysis of the sound environment that will be produced. For example, it is important to identify wrong or incomplete data, as well as anomalies in the NC database. which is more generally called data Quality Control (QC). For this purpose, among all the possible approaches, those based on machine learning techniques have been widely used in many fields of interest  [12] , in particular, when considering crowd-sourced data  [13] [14] [15] [16]. Three main methodologies can thus be used in machine learning: the supervised, the semi-supervised, and the unsupervised ones, depending on whether they use a large amount of labelled data, a small amount, or none, respectively.
Objective of the Paper The objective of the present paper is therefore to propose a method to identify in the NC database events similar to the NC party, i. e. , a set of data collected by multiple users, produced over limited spatial extent and temporal period, and most of the time with a higher spatial and temporal density of measurement points. More explicitly, regrouping data with similar spatial and temporal characteristics is known as spatial clustering and temporal clustering  [17]. Note that in the following, the paper focuses only on spatial clustering, with the temporal parameters being considered as a filter for the data (this will be presented later in the document). Many clustering methods have been proposed in the literature. Among them, the DBSCAN approach seems well-suited to our problem and is considered in the present paper.
Thus, the originality of the proposed work lies mainly in the use and validation of this method for the needs of our problem and not in the development of a specific clustering method. Some generalities on spatial clustering methods are presented in Section 2. Then, in Section 3, the DBSCAN method is detailed and tested on known NC parties in order to evaluate its performance and to identify the most appropriate processing parameters. Finally, the DBSCAN method is applied in Section 4, firstly to a few countries in order to identify the typology of the obtained clusters and secondly to the entire NC database. Lastly, Section 5 concludes this study, showing the interest of clustering methods to identify data that could be integrated into a reference database.
Clustering analysis can also be applied to the environmental measurement data (e. g. , temperature  [25, 26]  and humidity  [25, 27] ). Clustering is also widely used in geospatial analysis  [28]. In the environmental acoustic field, clustering has already been applied to analyze the results of the "Think About Sound" smartphone application, using the experience sampling methods (ESM)  [29]. Clustering was also used to group similar urban soundscapes using the fuzzy ants rule and K-means method  [30, 31]  or to monitor road traffic noise by using temporal clustering methods  [32, 33]. Nevertheless, the last clustering methods were applied on the basis of the information contained in the collected data, and, to our knowledge, no methodology has been applied to spatially clustering the data as we plan to do with the NC data.
• Fuzzy Clustering is another form of data clustering in which each datum can be included into several clusters. As a possible approach, the Fuzzy C-means method, which is the most widely used, is quite similar to the K-means clustering method. • Density-Based Clustering methods propose to group data by considering the density of data. Then, each cluster is built by considering regions with a high density of data. • Lastly, the Model-Based Clustering method groups data by considering that they are generated by probability distributions and that each cluster represents one given distribution. In the framework of the NC data, it is clear that the fuzzy clustering method cannot be used in the present study since each measurement point can be associated with only one event. Considering the model-based clustering methods, since the data are not associated, a priori, with any distribution model, the method seems inapplicable.
The method first randomly selects a point in the database. Then, it selects all the points within a circle of radius Eps. If the number of those points is greater than or equal to MinPts, then they are integrated into this cluster. At the next step, each integrated point becomes the center of a circle of radius Eps of a new iteration. When the number of points within one of these circles is less than MinPts, then the procedure stops for the corresponding center. When the search becomes unsuccessful for all the current points in the cluster, the cluster stops growing. The procedure then starts again by randomly selecting a new point in the database outside of any cluster already formed as the new center of a possible cluster. When all the points in the database have been processed, the procedure stops and eventually leads to the creation of a finite number of clusters, or none.
As mentioned before, all the points that are not part of a cluster are considered "noise". In agreement with the NC database model that is used in the present study (cf. Section 3. 2), the DBSCAN method has been implemented using the ST_ClusterDBSCAN() function available in PostGIS, a spatial database extender for the PostgreSQL objectrelational database  [37]. This function is a 2D implementation of the DBSCAN algorithm (the spatial clustering of the NC database is carried out on the 2D coordinates without the elevation). This function takes three inputs, geom as a geometry data type, Eps as a float data type, and MinPts as an integer data type, and returns one identifier per found cluster, as an output. The results are saved in a table called NoiseCapture_cluster, which contains the measurement point identification pk_point of coordinates the_geom and cluster identification c_id.
NoiseCapture Database The NC measurement locations are stored in WGS 84 (World Geodetic System 1984) format  [38] , which is a horizontal component of a 3D system used, for example, by the GPS satellite navigation system. In other words, the type of the coordinates is geographic. This encoding uses "degree" as a measure distance, while the ST_ClusterDBSCAN() function only takes coordinates as geometry data type. Therefore, a transformation to the metric projection EPSG:3857 (Pseudo-Mercator)  [39]  is required before applying the algorithm. This projection can only be used for data between 85. 06°S and 85. 06°N, which is in agreement with the NC database (NC data are bounded between 74. 49°S and 78. 73°N). The transformation was performed using the PostGIS function ST_Transform  [40]. The DBSCAN algorithm was used on a 3-year extraction of the NC database, from 29 August 2017 to 28 August 2020, starting from the official release version "28" of the NC smartphone application  [41].
Any measurement point in the NC database without geolocalization (may be a full track or only a part of a track) was removed before processing by the DBSCAN method. Using this NC database extraction, all the data can be organized into a relational database, which can be manipulated using GIS tools. In order to find the optimal parameters for DBSCAN, we use official NC party events as reference data. The objective of the first part of the study is to find the parameters of the method that allow us to find a maximum number of NC parties in the form of clusters, or at least, the best detection rate. During the corresponding 3-year period, 27 NC parties were planned  [8]  (Table  1 ), but one of them (i. e. , NC party number 31 with code name CICAM) was not performed. The training data set has 26 NC parties, spread over 3 years.
Filtering Variables In addition to the two parameters Eps and MinPts, one can also consider supplementary parameters in the DBSCAN methodology in order to reduce the amount of data to consider by pre-filtering the NC database. The corresponding filtering variables may have an impact on the computational duration of the process or can contribute to increasing the quality of the clustering results: Time window The temporal dimension is not considered in the classical DBSCAN method, which implies that in the context of NC all data are considered simultaneously, just spatially, without any consideration of date. It can then be difficult to identify NC events of relatively short duration if the corresponding measurement points are "drowned" in the mass of data that can be collected progressively (i.
GPS accuracy The accuracy of the DBSCAN method is necessarily based on the accuracy of the location of the measurement points; if the points are poorly located, then their membership in a cluster may be questioned. In the NC application, the localization of the measurement is based on the GPS system of the smartphone. In some cases, the measurement points may not be located at all; in this case, as mentioned before, the corresponding measurement points were removed from the database. For the remaining points, the associated location uncertainty can reach several tens of meters. The variable related to the accuracy of localization can also be an important element in the quality of the clusters obtained by the DBSCAN method. The method will therefore also be tested by filtering the data on the GPS accuracy values.