dataesr/scientific-paragraphs-categorization huggingface dataset:
The OSM mapping party was designed and set up by a small number of active OSM contributors who were attending the conference  (Mooney et al. , 2015) ; their goal was to collect Points of Interest (POIs) that were missing in Como city. Around 40 participants (roughly 10% of the conference) attended and were taught how to collect the data using field papers, which are a specific service to print out OSM maps for annotation in the field. The POIs were then mapped in around 2. 5 hours. On the second day of the conference, there was a data upload session that showed the volunteers how to insert their data into the OSM database; this session was too short, so not all data were entered into the database during the event. However, the POIs were monitored after the event and showed an increased mapping over the summer, which is attributed largely to this particular mapping party as local OSM activity in the city is not large.
Positioning the Issues of Privacy, Ethics and Legality in VGI At the time of writing, the issues of privacy, ethics and legality in VGI have not received widespread or in-depth treatment by the research community. The exact nature of the VGI or data used and which use case it is applied to may help to determine which legal, ethical and privacy issues are most prominent. When information about individual citizens is transferred and presented within a geographic context, the resulting profile information could be both 'highly revelatory and involuntary'  (Scassa, 2013:5) , and this can raise important privacy and ethical issues. The ability for VGI data and information to be mashed up or integrated with other VGI datasets, proprietary datasets or other information sources means that new sources of data are created.
The privacy, ethics and legal issues that existed for the original VGI dataset may not have completely changed due to this transformative change. In this section, we provide a simple table  (Table 1 ) that situates privacy, ethics and legal issues for the principal actors involved in the collection, production and dissemination of VGI, namely citizens, national mapping agencies (NMAs), commercial companies, researchers and other entities such as small and medium-sized enterprises (SMEs). While this table is not a fully comprehensive overview of all of the possible actor interactions with privacy, ethics and legal issues, it will allow us to situate our discussions in the subsequent sections of this chapter. Each cell in the table provides a simple example of considerations that are made by the corresponding actor when producing, collecting, managing, using or disseminating VGI.
Who am I? I am … An example of a privacy issue An example of an ethical consideration An example of the legal issues involved An academic or researcher looking to use VGI data During the dissemination of research outputs, care must be taken not to expose the identities of or other private information related to the citizens who contributed to the VGI project. Patterns and inferences made about the contributors of the data must be carefully considered so as not to breach the privacy of those citizens. If carrying out an analysis/ survey of citizens involved in VGI, the consent process ensures that individuals are voluntarily participating in the research with full knowledge of relevant risks and benefits. Many geography-based research projects can mix and integrate multiple datasets for investigation.
An SME wishing to use VGI for an application or service that it is looking to develop and bring to market VGI as a product must be used in such a way that the producer's privacy is protected from any potential future commercial exploitation. Using the data 'as is' without any embellishment or corrections to the VGI data that are untrue or incorrect. Ensuring that the licences and terms of use of the VGI data are compatible with commercial development. In some open licences it might be necessary that changes made to the VGI dataset/ database by the SME also be made available under the same licence.
Summary of Ethical Issues As communicated by  Sula (2016) , the key ways to respect ethics in databased research include involving participants throughout the research process, avoiding collecting information that should remain private, notifying participants of their inclusion and providing them with options to correct or delete personal information, and using public channels to disseminate research, such as Open Data. Ethical research has the least possible impact on subjects, asking or collecting only as much as is needed to answer its questions. In the case of VGI research, the researchers involved may not know exactly what knowledge they are trying to extract or patterns they are trying to uncover; the data are being used in an exploratory way. In these circumstances, it seems nearly impossible to inform participants of all anticipated harms and benefits in advance.
Today, datasets collected through VGI and crowdsourced means have a potentially very long lifespan. Given the longevity of these datasets and their potential interoperability and integration with other datasets, researchers and scientists must, in general and where possible, avoid data with personally identifiable information or information that could later be used to identify participants in connection with other datasets, e. g. screennames, usernames, etc. The potential for unintended consequences are high, but entirely mitigated when no personally identifiable information is collected in the first place  (Sula, 2016). The integration of many datasets with each other creates a brand new dataset that is essentially an unknown quantity in terms of its ethical characteristics. In this situation the creators of these new datasets must be conscious of how the new dataset will be used, distributed, analysed and even itself potentially integrated with other datasets in the future.
Legal Issues Surrounding Data Licence Types The type of licence applied to VGI data for their subsequent dissemination has an important influence on their usage. There are three main types of open data licences: • Share alike licences, which require the derived datasets to be released with the same licence as the original one(s); the most famous such licence in the area of geographic information is the Open Database License (ODbL) used by OpenStreetMap (OSM). • Open licences, which allow any type of use provided the citation of the data provider is given; it allows, for instance, commercial use of derived datasets. An example of such a licence is the French 'Licence ouverte' , which is used to release governmental open data in France. • Limited use open licences, which limit the use of the dataset to personal use, or non-commercial use.
For instance, the IGN (the French mapping agency) releases its datasets openly for research and education purposes. The choice of a licence conveys a political or commercial strategy, and the strategies of these licences might not be compatible. So what happens when projects with different strategies plan to merge their datasets? And what happens when one or more of these datasets are from VGI? It is useful at this point to provide a real-world example. The most typical case regarding geographic information is the following: how is it possible to integrate non-ODbL open data into OSM? The case of the French national address dataset is interesting to study, as it plans to integrate data from the IGN, which is a governmental administration, the French Post Office company, which is a public limited company, and OSM (Figure  1 ).
All three already have address datasets updated by crowdsourcing communities. They also have different licensing strategies. OSM uses the ODbL while the French Post Office would prefer a licence that allows commercial use of derived datasets. Figure  1  shows a possible integration scenario for the architecture of the project and the licensing strategy. Two new datasets are created in this scenario: a common and central address dataset, and a copy of this dataset using the OSM technologies (in RDF format). The OSM-like copy is under the ODbL licence, which allows OSM contributions regarding addresses to be directly included, and the other way around. The common address dataset is under two licences: a limited open licence that only allows personal and non-commercial use of the data, and a charged licence for other uses.
The OSM-like dataset is only a partial copy, as the French Post Office does not want to release all the information of its dataset (e. g. the standardised spelling of addresses). A quality control step is included in the common dataset to improve contributions through both field survey (by mail carriers and IGN surveyors) and automatic tools. In this scenario, different access desks are proposed for citizens, derived from existing tools. The IGN desk, which fills the common address dataset, is dedicated to community-sourcing (from city administrations, firefighters, police officers, etc. ); the Post Office desk, which also fills the common address dataset, is dedicated to citizens and administrations that report updates on addresses; and the OSM desk is based on OSM software, such as iD 1 , and could fill both the common dataset and the OSM-like dataset.
This (potentially false) information could be used by an insurance company to raise insurance premiums. Then, from the VGI project coordinators' side, to what extent must a portal/project coordinator provide a disclaimer about legal aspects? Under what circumstances can a portal be held liable for omissions (e. g. damaged areas not mapped during a disaster), or mistakes (e. g. infrastructure shown to be intact that is actually broken, leading to inaccessibility) be challenged? In reality, there are no clear cut answers to these questions at this point in time. Christin et al. (2011)  indicate that the research community should provide open datasets that can serve as a baseline for performance, security and legal evaluation in order to begin addressing these critical issues.
Citizens often struggle to comprehend how these issues add to the importance of these data flows in relation to other social structures or issues. Hallinan et al. (2012:271)  go on to argue that due to the complexity of the issues of privacy, ethics and legality, 'it appears that the public are being forced to act in an environment they have little template for approaching'. The concepts of VGI and Open Data are still relatively new. Consequently, it will take time for citizens to become deeply familiar with the issues discussed above. Christin et al. (2011)  argue that at the moment, privacy research usually operates on either private or synthetic datasets. These datasets do not allow new mechanisms for privacy, ethical and legal considerations to be harmonised or benchmarked against. In any case,  Torra and Navarro-Arribas (2014:277)  indicate after their wide scale review of the issues of data privacy online that the development of methods to protect citizens 'has to take into account the specificities of the data involved'.
No two VGI datasets are the same; indeed, it can be the case that within a VGI dataset different objects might be collected by different citizens in different circumstances. VGI is an exciting and powerful source of geospatial data that is likely to continue growing. Understanding how to protect the citizen while enhancing their role in the production of VGI is a big research challenge for the next few years. Indeed this research issue has not really been tackled at all by the research community at this point in time. Protection of the citizen's privacy and ethical rights under suitable legal conditions is very important. However, the frameworks or structures developed to implement these protections must not place insurmountable barriers to citizen participation in VGI. The act of being involved in VGI as citizens should continue to be a leisure activity pursued by those motivated to volunteer.
Introduction and Background Quality is a key component of any dataset. Decisions on using a spatial dataset for a certain purpose are heavily based on quality measures such as positional accuracy, thematic quality, completeness and usability. This also applies to Volunteered Geographic Information (VGI), a new and growing source of data, contributed by citizens, that can take many different forms, e. g. geotagged photographs through sites such as Panoramio and Flickr, online maps such as OpenStreetMap (OSM) and Wikimapia, and 3D VGI such as OSM-3D and OSM2World. For a more detailed overview of the diverse range of current VGI data sources, see Chapter 2  (See et al. , 2017). A set of elements is specified in the ISO 19157 standard for spatial data quality  (ISO, 2013). This framework adequately serves communities such as National Mapping Agencies (NMAs), which have professional staff following rigorous protocols and multiple quality control processes so as to produce high-quality products of a minimum acceptable specification.
, 2014). These comparisons are based on the belief that authoritative data are always of a minimum, acceptable quality and created according to high standards and that it is thus reasonable to assume that authoritative data can play the role of reference datasets during a quality evaluation process of VGI datasets. In these studies, a number of methods are used, e. g. data matching, generalisation evaluation, etc. , that consider different elements of data quality such as positional or thematic accuracy. However, the application of these methods is not always possible, because of limited data availability, licence restrictions or the lack of access to costly authoritative datasets. Moreover, as VGI datasets are often richer than their authoritative counterparts, and will only continue to increase in richness, the use of authoritative data as a reference dataset for quality evaluation may no longer be the most valid choice.
In some parts of the world, VGI is more complete and more accurate than authoritative datasets  (Neis et al. , 2011; Vandecasteele and Devillers, 2015) , which poses challenges to the assessment of VGI data quality. This chapter provides a review of data quality indicators for geographic information that are part of the ISO 19157 (2013) standard, of how these have been used to evaluate the data quality of VGI in the past and of other approaches that could be used. Additional indicators that have been proposed for VGI in particular are also presented, as well as initiatives to develop quality assessment frameworks combining several quality measures and indicators.
Measures and Indicators to Assess VGI Quality ISO 19157 is the latest release (2013) of a data quality standard among the internationally known standards for describing spatial data quality, e. g. the International Cartographic Association (ICA), Federal Geographic Data Committee (FGDC) and Committee on Standardization (CEN) standards. It attempts to define a set of measures for evaluating and reporting data quality. The conceptual model for geodata quality as specified in ISO 19157 represents data quality by a series of data quality elements, e. g. positional accuracy. Each data quality element is then further described by measures that allow the data quality to be evaluated, and the results of the evaluation can be documented and reported to any interested party. The ISO 19157 standard does not attempt to define any minimum acceptable levels of quality for spatial data, and it considers only conventional datasets without proposing any data quality elements or measures specific to VGI.
ISO Quality Measures Applicable to VGI The first five spatial data quality elements of ISO 19157 (Sections 2. 1. 1 to 2. 1. 5) are focused on the quality of the product from a producer's point of view, or on what is termed the 'internal quality' of a dataset  (Devillers and Jeansoulin, 2006). The sixth spatial data quality element (Section 2. 1. 6) is focused on the user needs and requirements and is referred to as the 'external quality' of a dataset  (Devillers and Jeansoulin, 2006). Thus there may be situations where the internal quality is high (i. e. it is produced according to a set of specifications) but the external quality poor (i. e. it does not fulfil a particular purpose from a user's perspective). The same will apply to VGI, so the fact that a VGI dataset is created according to some initial specifications does not necessarily mean that it can be used to cover all or any requirements stated by potential end users.
For a set of OSM road features compared to the UK's Ordnance Survey data, the average errors identified were 5. 8m  (Haklay, 2010 ) -a distance unlikely to be seriously problematic for most land cover maps, but one which could cause small or narrow features (ponds, hedges, riparian habitats, etc. ) to be missed or misplaced. Canavosio-Zuzelski et al. (  2013 ) performed a positional accuracy assessment of OSM as part of a vector adjustment correction. However, in this case, rather than accepting official survey data as truth, both official data and OSM data were assessed against independent stereo imagery, which means the technique can be applied to other national agency and topographic datasets and has the potential to identify areas where the VGI surpasses the accepted dataset. Thus the authors were able to assess OSM against USGS (United States Geological Survey) and TIGER (Topologically Integrated Geographic Encoding and Referencing) road data on a more-or-less equal footing -albeit for a very small area for which the aerial imagery was available.
Thematic Accuracy Thematic accuracy refers to the accuracy of classes or thematic tags associated with specific locations or objects placed in geographic space, such as classes assigned to pixels in a land cover map or tags assigned to a vector-encoded entity, e. g. a highway, river, building or green area. The assessment of thematic accuracy in VGI may be performed using a traditional approach, where the information is compared to reference data, e. g. satellite imagery or authoritative data, by experts. For instance,  Estima and Painho (2013; 2015)  and Jokar Arsanjani et al. (2015b) investigated the thematic accuracy of the classification of OSM features using the Corine Land Cover database and the pan-European GMESUA dataset as authoritative reference data, respectively. However, the assessment of the thematic accuracy of VGI raises new challenges, due to the lack of strict specifications, the characteristics of the contributors and contributions, and the type of thematic information at stake.
A number of strategies have been proposed and evaluated for getting the best value out of contracted labellers, and in particular for trading off the value of new information about unlabelled entities against the value of reinforcing or correcting information about entities that have been labelled repeatedly  (Ipeirotis et al. , 2014). This corresponds to the use of additional quality indicators, which are further addressed in Section 2. 2. One consideration when deciding between accuracy improvement and new data acquisition must be the possible impact of errors when a dataset is used in the real world -a balancing act similar to the calculation of ROC (Receiver Operating Characteristic) curves or sensitivity/specificity calculations for classifiers and prediction algorithms. The problem of risk and liability, when considered in the VGI world, is usually sidestepped through the use of disclaimers, but if VGI begins to seriously underpin Spatial Data Infrastructures (SDIs) -see Chapter 12  (Demetriou et al.
Completeness Completeness refers to the presence or absence of features, of their attributes and of relationships compared to the product's specification; it is divided into a) commission, which explains excess data presence in a dataset, and b) omission, which explains data absence from a dataset. Completeness is of major concern/ importance in VGI, since many volunteered datasets are demonstrably biased towards particular spatial regions (see e. g. Haklay, 2010) , but also towards certain features that are easier to measure or towards themes or 'pet features'  (Bégin et al. , 2013)  that are of particular interest to the contributing individual, or even motivated by accessibility or digital inclusion  (Zielstra and Zipf, 2010). This reliance on the motivation of individual volunteers will determine the resolution, homogeneity, representativity and domain consistency of the resulting data.
Where a principled sampling strategy can be imposed on volunteers, e. g. a probabilistic schema or the systematic, even grid of the Degree Confluence Project, the volunteered data have the potential to be more broadly applicable, but the value of the data will depend on the coverage by volunteers, meaning that many platforms must actively direct users to the desired locations, trading off potentially rich information elsewhere against an even placement of observations. The lack of specifications and the nature of VGI makes, in some cases, the assessment of completeness a complex process, which cannot rely only on direct unit-based comparisons, and instead requires the development of new approaches. Moreover, in many areas, the number of digitised VGI features may exceed that found in an authoritative dataset  (Neis et al.
, 2011) , making a simple comparison of feature counts inappropriate, and requiring a subtler consideration of commission and omission  (Jackson et al. , 2013). Koukoletsos et al. (2012)  present a method that holds promise for such contexts, combining geometric and attribute constraints to match road segments in OSM with those found in an authoritative dataset, and to achieve a tile-by-tile completeness assessment. In another study,  Hecht et al. (2013)  proposed an object-based approach to assess the completeness of building footprints. Haklay (2010)  identified a bias in UK OSM data coverage towards more affluent areas, and relates this to the fact that socially marginal (and less-mapped) areas may be the very locations where charities and agencies requiring free data are operating. Brovelli et al.
(2017)  developed a web application to compare OSM road data with authoritative road data, enabling the assessment of completeness and positional accuracy of OSM data. Ciepłuch et al. (2010)  also compared the spatial coverage of OSM to that of Google Maps and Bing Maps, and identified regions with different levels of coverage in the three datasets. Globally, this bias is being somewhat redressed by the volunteers' own efforts to improve coverage, and by focused initiatives such as KompetisiOSM in Indonesia 2 , but it remains the case that coverage is extremely heterogeneous in VGI, both spatially and thematically, and that the absence of information in an area makes it difficult to draw robust conclusions about trends. Brunsdon and Comber (2012)  specifically addressed the lack of experimental design in a volunteered dataset recording the first flowering date of lilacs in the USA by applying random coefficient modelling and bootstrapping approaches to tease out more reliable information on phenological trends.
Temporal Quality Temporal quality refers to the quality of the temporal attributes, such as date of collection, date of publication, update frequency, last update or temporal validity (also referred to as currency), and also to relationships between the temporal validity of features. Currency is one aspect of traditional data quality where VGI can be expected to surpass authoritative data, especially in dynamically changing environments, given the large numbers of citizens who are acting as sensors at any one time. However, there is often a trade-off between currency and other facets of data quality. The issue of representativeness becomes even more vexed when the spatial domain is extended to the spatio-temporal domain, and, unless a temporal sampling scheme is also imposed upon contributors, the density and coverage of a VGI dataset over a small time range can be very limited.