dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods The raw data describing French public procurement is publicly available online through a European Union (EU) service called TED, which we describe later. However, they exhibit a number of issues that must be solved before performing any analysis. Identifying and correcting these issues is far from being a trivial task. In their study of the whole TED dataset, Csáki & Prier  [5]  even stress that "given its size and complexity, this dataset is not for the faint of heart and [. ] no current quality frameworks appear readily able to help". In this section, we first describe the characteristics of the raw data, and summarize their main issues. Then, we present the processing that we propose to fix these issues.
TED Dataset The Tenders Electronic Daily (TED) is the online version of the EU official journal that is dedicated to the publication of the contract notices (i. e. calls for tenders) and award notices related to public procurement. These data are accessible on data. europa. eu, the official portal for European data, under two possible forms: directly through an API  [63] , and as aggregated CSV files  [40]. They cover a period ranging from 2006 to the present day. However, the notices published in the TED between 2006 and 2009 are both less complete and less reliable  [10]. The latter date also corresponds to a change in the database structure, that brought a number of new fields, among which crucial information such as the national identifier of the winner and the weight associated with the price criterion.
e. truly missing award notices). Out of the 410,283 award notices, 76,279 (19%) are not cited in any contract notice, without it being explicitly optional. Like before, this number includes actual errors, i. e. where the contract notice should be there, but also cases where it is optional, e. g. renewal of a framework agreement. For both types of notices, these values are in line with the findings of another study conducted on the whole TED dataset for a different period  [5]. It is worth stressing that award notices cover most of the fields present in the contract notices, and only a few important fields such as the contract duration and advertisement period are lost when their matching contract notices are missing. The second issue is joint agent description. In the TED table, each row represents a single lot, including dedicated fields to store the information describing the buyer (name, street, zipcode.
), and similarly for the winner. However, in practice, it is possible for a lot to be awarded by several buyers to several winners. This concerns 34,257 (2%) and 37,443 lots (3%), respectively. This problem was also mentioned in a previous study dealing with the whole TED dataset on a different period, although it did not quantify its extent  [4]. In this case, all the concerned buyers are described in the same field, under the form of a single character string, and the same holds for the winners. Some data entry clerks appear to use separators characters such as ---, but this is neither systematical nor consistent. In order to take advantage of these data, we need a separate representation of these agents. The third issue is name and address inconsistency. The same agent can appear under different names in the dataset, as these strings are not normalized: diacritics and punctuation signs are not used consistently, acronyms are used non-systematically in place (or in combination) of full names.
In the case of France, it is a 14-digit number called SIRET (Système Informatique pour le Répertoire des Entreprises sur le Territoire -Computer system for the national register of companies). It is filled in only 16. 4% of the lots for the buyers, and 2. 9% for winners. An examination of the whole TED dataset reveals that the same observation applies to the rest of the data, with 32. 5% and 12. 9% filled SIRETs for buyers and winners, respectively. On this point, France is among the worst 5 countries, overall. In DeCoMaP, we want to extract various types of procurement networks, considering buyers and winners as vertices. Therefore, correctly identifying all instances of the same agent is absolutely crucial. To solve this problem, we need to leverage all the agent-related information available in the TED: name, location, and activity domain.
Data Processing As illustrated in Figure  1 , our process includes three main steps. The first one consists in performing information extraction in order to split certain TED fields that contain heterogeneous information. The second aims at recovering the unique identifier that is missing for most agents described in the database. Finally, the third step concerns the matching and merging of distinct agent occurrences that correspond to the same unique agent. This section summarizes these steps, and their performance is assessed later in Section Technical Validation. As indicated before, the interested reader will find more details in our technical report  [35].
Database Initialization The goal of this first step is to split the single original TED CSV table into several separate database tables (cf. the Data Records section for a summary of these tables), in order to ease both the cleaning and usage of the data. This requires a series of operations, which are summarized below. We perform five operations to solve the unconstrained criterion description issue. First, we clean the weight fields by keeping only numerical values and separator strings that we manually identify beforehand. Second, we leverage these separators to split multiple criteria, both regarding their names and their weights. Third, when criterion names and weights are mixed in the same text field, we separate them. Fourth, we normalize the weights of each lot so that they sum to 100.
Fifth, in order to handle the diversity of criterion names due to this field being free text, we add a normalized version of these names, taking the form of one of six possible predefined classes (price, deadline, technical terms, environmental terms, social terms, others). The rest of the operations concerns the agents. We deal with the address inconsistency issue by removing all irrelevant information from the zipcode and city fields. In addition, we leverage Hexaposte, the French online postal service database  [22] , to fill missing zipcodes. We fix typographical inconsistencies in all address fields by removing all punctuation marks, collapsing consecutive space characters, and switching the remaining text to upper case. In order to deal with the confusion between postal and geographic addresses, we remove words related to the former (e.
Agent Identification The goal of this second step is to solve the missing agent identification issue, which is a major limitation of the data, as explained before. For this purpose, we leverage the SIRENE database  [18]  (Système National d'Identification et du Répertoire des Entreprises et de leurs Etablissements -National identification system for commercial entities and their facilities), which lists all economic agents participating in public procurement in France. It includes not only agents that are currently active, but also agents that are no longer active, since 1997. This database actually distinguishes two levels of economic agents: entities vs. facilities. Entities are companies, government agencies, departments, charities, institutions (legal entities) or people (natural persons) that have a legal existence and the ability to enter into agreements or contracts.
Agents from the TED correspond to facilities: ideally, we want to identify their SIRETs, but getting their SIRENs right is much better than nothing. To retrieve the SIRETs from SIRENE, we must match each concerned TED agent to a SIRENE entry. One could assume that using the sole agent name would be the best approach for this purpose. However, this is not that simple, as names can be quite different in both databases (e. g. full name in one vs. acronym in the other). Moreover, SIRENE typically proposes several alternative and/or former names for one agent. For this reason, we propose a filter-based approach illustrated in Figure  2 , that leverages all the information available in both datasets, i. e. the name and full address of the agents, and the date and activity domain of the lots. It is designed to handle the fact that, as explained before, these fields themselves are not always filled.
Agent Merging Our agent identification method is not able to retrieve all missing agent SIRETs, which means that a part of the TED dataset remains unidentified after the previous step. We assume that some of these occurrences represent the same agents as other successfully identified occurrences, or even other unidentified occurrences. The goal of this step is to group such occurrences under the same entries in our database, based on their similarity. We leverage the Dedupe library  [15]  in order to obtain a clustering of the agent occurrences, comparing them based on their name, address, city and zipcode. We then need to handle the resulting clusters of similar occurrences, of which we identified four types. First, some of the resulting clusters are singletons, meaning that we did not find any other similar occurrence.
Data Records The FOPPA database is publicly available online as a Zenodo 2  repository  [33]. It contains an SQL dump of the full database, as well as a CSV version of each table constituting this database. Figure  3  shows the dependencies between these tables under the form of an entity-relation diagram. They contain the following information: • Lots: the main table, each entry describes a lot from the TED dataset. • Agents: contains all the economic agents involved in these lots, as buyers (public authorities) or winners (suppliers). • Names: one agent can have several names, which are listed in this table. • LotBuyers & LotSuppliers: describes the involvement of agents in lots, respectively as buyers and suppliers. One agent can be simultaneously a buyer for a lot and a supplier for a different lot.
Technical Validation As explained in the Methods section, there are a number of issues with the raw TED data describing the French public procurement notices. We designed and applied several methods in order to solve most of these issues. In this section, we assess their performance. The operations described in the Database Initialization section are straightforward, so we focus on Agent Identification (retrieving the missing agent SIRETs) and Agent Merging (merging agent occurrences that correspond to the same entity).
Identification Method In this section, we focus on our method designed to retrieve the missing agent SIRETs. We assess its performance by comparing its output with two distinct ground truths. On the one hand, we leverage TED entries whose SIRET is already filled out in the TED. However, the agents concerned by these cases are mainly buyers, which suggests a potential bias. This is why, on the other hand, we consider a random sample of entries originally without SIRET, which we identify manually. In order to assess the performance of our method, we consider four different outcomes when matching the agents from our database to the entries of the SIRENE database: 1. Full SIRET : the matching between our agent and SIRENE is successful, and the method therefore correctly retrieves the whole SIRET, i.
Known SIRETs If we count the number of agents with a valid SIRET, we get 6,115 unique buyers (by opposition to occurrences) and 18,209 unique winners. We remove these SIRETs from the database and apply our method, to check whether it can recover them. The complete identification of the SIRET can be performed for 66. 6% of the unique buyers and 72. 4% of the unique winners. For buyers, we consider that a partial SIRET is also a good result, since procurement management is often centralized at the main entity. Summing up the two, we get 75. 7% of success for buyers and 81. 4% for winners. For this ground truth, the performance is a bit lower for the buyers, probably because they are the ones filling the award notices. When they bother filling their own information, they tend to get their SIRET right, but not pay too much attention to the rest of their fields.
False Negatives False negatives correspond to agents incorrectly placed in different clusters. In order to assess this type of error, we cannot use the same data as for the false positives, because the forms of these agents do not exhibit enough diversity. Instead, we constitute a more appropriate ground truth by sampling  agents that appear several times under different forms. Our sample contains 5,020 occurrences, that correspond to 377 unique agents. Each one of the 377 corresponding SIRETs appears between 1 and 538 times in the sample. For the sake of evaluation, we ignore these agents during the identification step, which is only applied to the rest of the data. We then perform the clustering step on the whole dataset, including the sample. Finally, we assess the false negatives produced during this last step by studying how the sampled agents are distributed over the clusters.
Overall Assessment Overall, our method allows significantly improving the quality of the data. Before its application, the SIRET is filled for only 16. 4% and 2. 9% of the unique buyers and winners, respectively. Afterwards, the completion rates for the same fields are 97. 5% and 86. 2%. Assuming that the results obtained on the different samples for agent identification and merging are representative of the whole dataset, we can estimate the performance for agent occurrences, at each step of the process. Table  5  shows the evolution of the number of agent occurrences, depending on their identification status (correct, incorrect, none), after the main steps of our pipeline. As a baseline, we originally have 2,761,930 agent occurrences before any processing, as identified through exact matching.
The first row (After Separation) shows the situation after having separated multiple agent occurrences (cf. Section Database Initialization). At this stage, all the present identifiers are correct, but this concerns a very small part of the now 2,878,683 agent occurrences. The second row (After Normalization) shows the number of agents after having normalized their names. This step matches occurrences that correspond to the same entity, some of which have a SIRET, thereby allowing to identify an additional 14% of the total occurrences. The third row (After Identification) focuses on the retrieval of missing identifiers by leveraging the national database. This step clearly has the strongest effect, with an additional 70% of identified agent occurrences. However, this does not go without the introduction of some incorrect SIRETs among them.
Usage Notes As explained before, FOPPA was developed in the framework of the DeCoMaP project with two goals in mind: perform a descriptive analysis of public procurement data in order to better understand its functioning and identify problems or anomalies; and train and test an automatic method able to detect frauds. Obviously, FOPPA can therefore be used for these tasks. It is worth stressing that the fraud detection method proposed in the context of DeCoMaP relies on a graph-based model of public procurement, in which vertices represent buyer and winners, and edges the contracts involving them  [31, 32]. To build such graphs, it is necessary to be able to identify economic agents in a unique way. This is the reason why we spent a lot of time and efforts fixing the missing SIRETs problem in the raw data.
It is also possible to build red flags by combining standard indicators extracted from public databases like FOPPA, with others based on data that are not publicly available, as Decarolis & Giorgiantonio do with information describing police and judiciary operative practices  [6]. Beside fraud detection, Klingler stresses the need for regulatory impact assessments to evaluate the economic consequences of public procurement regulation  [21]. But such impact studies cannot be performed without a database that can be relied upon. It should be stressed that, as most of our efforts have mainly focused on the identification of buyers and suppliers, some limitations of the original data are still present in FOPPA. First, due to the incompleteness of the raw TED data, many notices are missing from FOPPA: 39% of contract notices and 19% of award notices miss their counterparts.
These numbers should be downplayed, though, as they include two cases that are unfortunately indistinguishable in the TED: not only errors (notices that should be in the dataset), but also licit cases (optional notices). Also, most of the contract information appears in both types of notices, the award notices being the most informative overall. As mentioned in the Main Issues section, this is a well-known problem of the TED  [5, 9] , whose solving would require leveraging a more complete alternative data source, which as far as we know, is not publicly available. The second limitation concerns the incompleteness of certain important fields describing the lots. The contract duration and advertisement period are missing in 39% and 25% of the lots, respectively. This is mainly due to the missing contract notice issue, as both of these fields do not appear in the award notices.
The number of tenders, which is used to define certain red flags, is missing for 31% of the lots. Finally, the contract price, a crucial piece of information, is missing in 31% of the lots. Moreover, the prices indicated in the rest of the lots are known to be unreliable, as mentioned in several reports on TED data quality  [1, 11]. This is also a problem with the raw data, that would require an alternative data source to be solved. A preexisting database called Opentender [14] and developed in the framework of the Digiwhist  [7]  project, is comparable to FOPPA in the sense that it mainly focuses on public procurement notices published on the TED. However, Opentender is much more ambitious in terms of coverage, as it contains data describing 25 European countries, and includes individuals in addition to institutions and companies.
For this reason, it required combining a number of very heterogeneous data sources besides the TED. Constituting such a database is a very difficult task, and in practice it is impossible to tailor the proper processing required by each considered source. Consequently, the quality of the French data in Opentender does not reach the level obtained in FOPPA. When focusing on the notices present in both databases, we identified four issues in Opentender compared to FOPPA. We summarize them here, but the interested reader will find a detailed description in our technical report  [35]. First, 14% of the lots present in FOPPA (and the TED) are missing in Opentender. Second, Opentender contains roughly twice as many unique agents as FOPPA, because many similar (but not identical) occurrences are considered as distinct, when they actually represent the same entity.
It should be noted that the identification of unique agents was not as critical for the Digiwhist project as it is for DeCoMaP. Third, the contracts not awarded to any supplier are processed like the successful ones, resulting in more than 4% of spurious agents among Opentender winners (often called "Infructeux", French for "Unsuccessful"). Fourth, the award criteria are missing in 36% of the considered notices, when they are present in the TED and FOPPA. This highlights the importance of the efforts we put in adapting our processing to the specific case of the French notices. It is important to stress that our work is meant to be used by others. The FOPPA data could be integrated to Opentender or any other similar database. The data processing method that we propose is generic enough to be adapted to other similar national data sources in a relatively straightforward way, allowing to deal with the remaining member states.
An interesting characteristic of FOPPA is that, thanks to our efforts, a large part of the economic agents it contains are identified by their SIRET number. This identifier is ubiquitous in French data, both in open and commercial sources, and therefore allows connecting FOPPA to a number of existing databases, thereby opening new perspectives. First, one can retrieve a number of additional details concerning the agents in the SIRENE database  [18]  itself (e. g. activity domain, number of employees) or in its extensions such as the geolocated version  [19]  (GPS coordinates). Second, certain types of agents have their own databases containing type-specific characteristics, e. g. high schools [36], parkings  [30] , cinemas  [28] , operators committed to organic farming  [2]. Third, some available databases reflect the functioning of the economic agents, e.
Magnetic measurements were performed in the laboratory following two steps. Prior to the deployment, a blank correction was achieved. For that, an Isothermal Remanent Magnetization (IRM; see e. g. ,  Tauxe et al. , 2018  for further physical explanations) was imparted at 100, 300, and 1000 milli-Tesla to all pristine filters and few leaves collected from each Hedera Helix pot associated to their respective sample holders (8-cm 3 plastic cubes), with the impulse magnetizer ASC Model IM-10-30 from ASC Scientific, and then measured with a 2G-SQUID cryogenic magnetometer. Note that the Hedera Helix pot plants were bought the same day in the same nursery. Before the blank measurement and their deployment in the street, we took care to clean all the leaves with purified water. Because we are not able to magnetize the samples without magnetizing their plastic cubes, these steps are required and allow to subtract in data reduction the signal of potential magnetic impurities which may be present in the pristine filters, plant leaves, and in the sample holders.
The academics quickly realized that ATMO-Occitanie did not have sufficient resources to develop PM metrology at a street level, even though these data are of real interest to the community. Indeed, the population wants to know the quality of the air they breathe in their living spaces. We observe the democratization of lowcost sensors, easy to deploy everywhere. Our research project BREATHE is at the core of this ecosystem of measurements and citizen counter-expertise. ATMO-Occitanie is also at the core of this issue as it is the official agency whose measurements are directly considered by urban governances. One of our hypothesis is that the institutionalization of ATMO-Occitanie has been one of the obstacles to the BREATHE project. The public institution (the metropolis and the city of Montpellier) had already contracted with ATMO-Occitanie, which could be the reason of the failure to establish a collaboration agreement with the metropolis and the city of Montpellier.
Acknowledgments: The authors thank all the participants in this study, but also Renaud Barres the director of the  Council for Architecture ,  Urban Planning , and the  Environment (CAUE34)  for its support by participating in the project and by provisioning its premises for several meetings. Our thanks also to  Florence Brau , and  Isabelle Touzard  vice-chairs at the metropolis council in charge of research and environment, respectively, who supported our project from its conception to its finalization through this article. For reasons of scale of delegation, they cannot be co-authors as planned at the beginning. We also extend thanks to  Alexandre Gouget , from the  Town Hall  in getting access to the Arceaux aqueduct and  Jean-François Ritz ,  CNRS  research director for helping us to implement the anemometer on the aqueduct.