dataesr/scientific-paragraphs-categorization huggingface dataset:
Adaptive Data Models for Object Collection NMAs should propose an adaptive data model to collect and monitor geographic objects. This data model should allow contributors to: • report updates or errors; • add new attributes and objects to existing class objects; • add new class objects identified by NMAs as out of the specifications of the authoritative topographic databases at present, but important for different applications; • add new class objects if it fits end user needs; • assess, as a mandatory requirement, the membership of an object to a class object type; • import data collected in-situ; and • ensure interoperability with the existing topographic data model in order to integrate significant and validated contributions directly into authoritative databases or to generate updates.
Additionally to what has been mentioned above, an intuitive contributor-interface can also play another very important role in the field of input data quality. While the basic principles of human-computer interaction (HCI) should be intact and meticulously followed, the contributor-interface could be the vehicle for implementing a number of elements in the protocol regarding the input of high-quality data (more information protocols for data capture can be found in Chapter 10 by  Minghini et al. , 2017). It is common for NMAs to have protocols in place that must be followed in order to achieve maximum homogeneity in the datasets produced. Volunteered content should also follow similar rules. Thus, the contributor-interface, which in this case serves as the data capturing layer, should be equipped with as many protocol elements as possible, balancing between high data integrity (and thus quality) and adequate freedom for the contributor.
g. the classification of certain land cover types may be difficult for contributors, or contributions from a particular area can be found to have different interpretations. These indicators can be useful for assessing data reliability. Methods to visualise quality, discussed in Chapter 9  (Skopeliti et al. , 2017) , can considerably enhance this step in the process. Some recommendations include: • Define a typology of quality and associate the indicators previously assessed to either qualitative or quantitative rankings; these rankings can be based on probabilistic, fuzzy or possibilistic approaches. • To optimise the use of resources and procedures, NMAs may perform validation only on volunteered data that are considered to be worth validating depending on the indicators obtained in the previous step that are considered relevant for each dataset.
Prior to the widespread diffusion of new Information and Communication Technologies (ICT), public participation was largely understood as a form of public commenting on already prepared plans, while emerging technologies have opened up new and innovative ways of realising the active involvement of the wider public in spatial planning  (Bizjak, 2012). Opportunities have arisen in different fields, e. g. improving the communication between authorities and citizens, providing more accurate and up-to-date databases on the current state of territorial conditions, and collecting the ideas and visons for future developments of different stakeholders  (Berntzen et al. , 2005; Brabham, 2009; Seltzer and Mahmoudi, 2013). As a dynamic and complex socio-technical process, spatial planning may entail multi-faceted paradigms originating in a variety of workflows in practice.
Indeed, SMGI is different from traditional common vector spatial datasets such as A-GI supplied by institutional SDIs, which exclusively feature spatial and thematic information: the SMGI data model features spatial, temporal and multimedia dimensions (i. e. image, text, video and audio), as well as a user dimension, including specific information about the user profiles. Furthermore, in certain cases, the SMGI data model also includes a preference dimension, i. e. SMGI appreciation expressed by the social network community by means of scores, stars or likes/dislikes, thus widely expanding the range of analytical opportunities for planners and analysts  (Campagna et al. , 2015). A comparison between the SMGI and traditional A-GI data models is shown in Figure  1. The general SMGI data model may foster advances in spatial planning methodologies and may be a valuable complement to traditional A-GI that can support several stages of the Geodesign process.
e. projects and policies) by a number of users (usually, but not necessarily, around 30). After the project and policy diagrams are collected (see Figure  5  for examples), the users can combine them in complex design syntheses that can be compared and evaluated against an impact model highlighting positive and negative impacts as well as costs (Figure  6 ). The platform also features a number of tools supporting negotiations so that the users participating in a workshop (which can be virtual and of same/different place/time types) can eventually find consensus on a common shared design. The data stored in the project geodatabase of geoidesignhub. com can be considered as a design stemming from VGI. In addition, the data feature SMGI characteristics for design diagrams, i. e. they have spatial, temporal, user and preference dimensions, which can be further used to analyse the overall design process and participant behaviours.
For example, since the US Weather Service did not have enough resources to set up a countrywide meteorological measuring network, they made use of volunteers all over the country to help in the data collection. The resultant data were one of the most important long-term datasets in the history of North America and have been used for essential work within climate research, agriculture and development planning Fig. 1 : Categories of citizen science. Modified from  Grossberndt and Liu (2016). ' All rights reserved © Springer International Publishing Switzerland 2016'. (Vetter, 2011). This example shows that the collection of data over many decades has led to the compilation of long-term data series, which are extremely valuable for the work of modern science  (Miller-Rushing et al. , 2012). Another reason for the application of CS and other participatory approaches is to increase citizens' awareness of problems related to their immediate environment.
Furthermore, with an increasing number of CO-based initiatives, the EU H2020 Work Programme 2016-2017 (Topic in SC5-19-2017) issued a call for the coordination of citizens' observatories initiatives  (EC, 2016 (EC, -2017b) )  to create a CO knowledge base in Europe across disciplines in order to avoid duplication, ensure interoperability, create synergies and facilitate the gradual uptake of this knowledge base by environmental authorities. There are more existing and planned CO-related activities supported by the EC programmes and calls, for example:.
, 2011). One option to deal with this issue is to build networks with other existing projects or initiatives to use already existing datasets and combine them with newly obtained data  (Dickinson et al. , 2010). However, special attention must be paid to accuracy and uncertainty, especially when comparing crowdsourced with referenced data. The same applies for the interpretation of qualitative data; indicators such as 'quality of life' or 'wellbeing' should be developed together with more quantitative data. In addition, data security and privacy are important issues that require special attention. Especially when using smartphones and/or mobile sensing devices, it has to be ensured that the data from the volunteers are anonymised and treated according to national and international data protection laws and standards.
Katherine's driverless electric car takes her to work, where she is a spatial data quality expert at the National Mapping Agency (NMA) in her country. She is responsible for the quality assurance and quality control of the NMA's spatial databases. Today she is focused on doing some routine quality assurance on the main topographic database, which is a dynamically updated set of layers that takes in changes from a range of users, including citizens. She does some checks to ensure that the automated quality assurance procedures are filtering out data that do not meet the minimum requirements for the database and determines where to send field surveyors to confirm any critical changes. Today is Friday and Katherine is looking forward to attending a weekend mapping party, which will focus on helping another country build up their own, quality assured topographic database with seamless input from experts like her, interested citizens, businesses and non-governmental organisations on the ground.
Regarding the former, our generation has witnessed the appearance of mobile phones, which then evolved into smartphones and have now been transformed into location-capturing devices; when combined with web applications and social networking, the volumes of data created are immense. There are many examples of  Web-based applications, such as Facebook, Flickr, Foursquare, etc. , where the data come from the conscious use of these applications but the geographic information (GI) is generated implicitly by the users without the original aim of actually creating geospatial datasets. This can be distinguished from the proliferation of all kinds of sensors that passively collect spatial data, mostly in an urban context. From high-end sensors to do-it-yourself, low-cost devices based on hardware platforms such as Raspberry Pi and Arduino, the flow of sensor-recorded location data is expected to increase.
, 2010)  if we have low-cost air and noise sensors located on every street corner? Moreover, sensor-collected data will not suffer from some of the quality issues or biases that usually accompany human-collected VGI. Some technologies may, however, rely on VGI to function properly or to realise their full potential. Take, for example, smart thermostats, which are intended to learn over time and make adjustments that improve the efficiency of heating/cooling systems while maximising the comfort of users. Such connected devices or sensors of the IoT require some active human intervention and thus will always involve some form of VGI. Many more electronic devices of this nature are expected to emerge in the near future. Technological trends also cover advances in software and algorithms. It is likely that the technology for handling large and complex datasets will advance in ways that will more fully exploit the use of VGI.
VGI Quality Although VGI has been a growing phenomenon for over a decade now  (Capineri et al. , 2016; See et al. , 2016) , one of the major factors that hinders the more widespread diffusion and uptake of VGI is the lack of a robust and standardised way to evaluate data quality, as outlined in Chapter 7 by  Fonte et al. (2017). VGI could both facilitate and accelerate the transition to Smart Cities and Digital Earth if it were credible enough to trust and hence use in applications that require accurate GI. However, this quest for trust, fitness-for-purpose and usability of VGI data comes down to implementing or devising tangible ways of measuring and reporting VGI quality. Without concrete knowledge of the state of a VGI dataset, its use might end up being a leap of faith that no serious stakeholder is willing to take.
Yet if the quality requirements for VGI are too stringent in terms of data specifications, precision, update cycles, spatial coverage or metadata, then we may end up discouraging volunteers. At the same time, we need to avoid the situation whereby VGI is considered to be 'laypeople's data' of de-facto inferior quality, full of biases, with no metadata and only occasional respect for protocols and best practices; such a development would disrupt the momentum and the dynamic that VGI has developed so far and will mark this kind of data out as marginal or as a cheap and untrustworthy replacement for authoritative datasets. It is important to note that VGI is already sometimes as good as, if not superior to, authoritative data and can even exceed the quality requirements of NMAs for common mapping applications  (Olteanu-Raimond et al.
To address the availability of multiple sources that may potentially be useful, methodologies need to be developed to assist users in choosing the right dataset or the right combination of datasets for each application. Decisions such as these will be aided by the provision of information about the data, and hence metadata are likely to become increasingly important accompaniments of citizen-derived datasets. Given the huge amount of VGI foreseen in the future, it is likely that there will be a focus on the development of approaches that are more automated for the assessment of VGI quality; this development will be challenging given the greatly varied nature of the data, which can be unstructured and heterogeneous, but is nevertheless of high potential value.
Understanding the True Value of VGI Much of the literature on VGI is about understanding this phenomenon. The subjects examined range from the motivation behind volunteered contributions, the quality of the data obtained or the biases that VGI datasets might possess to the integration of VGI with other sources of data. Little has been written about the true value of VGI. By 'true value' , we refer to what VGI has offered not only to the geomatics domain but also to people and society as a whole. The bottom-up production of VGI has democratised the production and use of GI. VGI has changed a landscape where spatial data creation was once the responsibility and privilege of a few governmental agencies or large corporations (e. g. NMAs), and where the access to spatial information was limited and usually very expensive for the public.
As the ability to integrate and fuse together greater numbers of complex and disparate datasets increases, it is of crucial importance that the issue of data reuse be addressed. Data reuse also links to legal concerns; for example, if the VGI was acquired by digitising from a map or image without the relevant permissions, what are the implications for those that reuse the VGI? Equally important are possible cases of vandalism. Intentional deterioration of the quality of a VGI dataset or the insertion of false data could have considerable ramifications if the data are then used in decision-making or policy implementation. It is anticipated that in the future, as VGI gains momentum, there will be a need to better safeguard the integrity and objectivity of this data source.
Acknowledgements The ideas presented here evolved from work undertaken in the context of studies funded under the  CITI-SENSE  and hackAIR projects. CITI-SENSE was a collaborative project partly funded by the  EU   FP7-ENV-2012  under Grant Agreement No. 308524. hackAIR is supported through the  EU programme on 'Collective Awareness Platforms for Sustainability and Social Innovation'  and funded through the  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 688363. We would like to thank,  Steffen Fritz , the referee for this chapter, for his very valuable comments.
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Mammalian cancer cells or fibroblasts migrate orders of magnitude more slowly on similar surfaces, so increases in Rac1 signaling upon loss of CYRI-B may thus increase their speed. The gene encoding CYRI-B is located on chromosome 8q24, near Myc and is thus genetically co-amplified with the Myc oncogene in many cancers (C Bioportal database). Coupled with a potential role in invasion, which we highlight here (Supplementary Fig. 4 ), we predict an interesting role for CYRI in cancer. CYRI proteins represent new fundamental direct negative regulators of the Rac1 -Scar/WAVE pathway and the DUF1394 is here identified as a new module specifically binding to active Rac. Previous studies have identified negative regulation at the level of Arp2/3 complex and positive regulators of Scar/WAVE complex such as lamellipodin  40, 41  , but CYRI is the only locally acting negative regulator of Rac1-Scar/WAVE activation.
Binding (arrow) was quantified relative to the input using densitometry (E). F -SPR binding curves of the interaction between purified recombinant Rac1 Q61L and CYRI-B-RBD. Left: GST-CYRI-B was immobilized on anti-GST surface vs increasing concentrations of Rac1 Q61L. Right: His-Rac1 was immobilized on NTA surface vs increasing concentrations of CYRI-B RBD. Analyte injections were performed at five different concentrations and steady state analyses used Biacore evaluation software 3. 0, fitting to a simple 1:1 binding model. A) Representative phase contrast pictures from an under agarose chemotaxis assay of Ax3 (WT) and Ax3-derived cell lines migrating towards a folate gradient, as illustrated. Representative DIC pictures from the same experiment are shown next to each panel. Cells were rescued using a restriction enzyme mediated integration (REMI) strategy (see Methods).
UniProt accession numbers are reported. Color code represents the number of entries with identical amino acid at each position. B) Co-evolution of Arp2/3 (green), Scar/WAVE (brown) and CYRI (orange) across the 4 main superfamilies of the eukaryotic tree  1. An incomplete circle corresponds to the absence of some subunits from the complex as in  2 #2 #1 Ctr CYRI-B % FRET efficiency E F R a c 1 W T R a c 1 Q 6 1 L R h o A Q 6 3 L C d c 4 2 Q 6 1 L GST-tagged Input GST R a c 1 W T R a c 1 Q 6 1 L R h o A Q 6 3 L C d c 4 2 Q 6 1 L GST-tagged GST R h o A Q 6 3 L GST-tagged R h o A W T Input mTq2-sREACH.
Alignment and phylogenetic tree Protein sequences were obtained from Uniprot http://www. uniprot. org/ and aligned using MacVector software. The phylogenetic tree was constructed based on the major eukaryotic superclasses as previously defined  1  and based on previous identification of Arp2/3 complex and Scar/WAVE complex sequences in selected organisms representing branches of this tree  2, 7. Searches were performed for the presence of CYRI using a BLAST homology search on the NCBI website https://blast. ncbi. nlm. nih. gov/Blast. cgi. Dictyostelium or human or a closer relative from the tree were searched against the complete translated genome of open reading frames from these organisms. HMM logo was generated by feeding the Pfam seed database of the DUF1394 domain into Skylign  8. Mammalian cell lines and growth conditions CHL1, B16F1, HEK293T, COS-7 cells were maintained in 10% FBS and 2mM L-Glutamine supplemented Dulbecco's Modified Eagle's Medium.
Using GF buffer (200mM NaCl, 30mM Tris-HCl pH7. 5, 3mM betamercaptoethanol) complemented with 5mM MgCl2 and 0. 5% of surfactant P20 as running buffer, GST-tagged proteins were immobilised at 22°C onto CM5 sensor chip functionalized with anti-GSTand reached ~320 RU. Same procedure was used for His-tagged protein onto NTA sensor chip and reached 650 RU. All immobilisation steps were done at a flow rate of 10μL/min. Serial dilution of each analyte was injected across a reference flow cell and the flow cell containing the ligand at a flow rate of 30μL/min. Data were solvent corrected, reference subtracted, quality controlled and evaluated using the Biacore T200 evaluation software. Affinity was determined by curve fitting a 1:1 binding model.
Random Migration Assay for Mammalian Cells 6-well glass bottom plates were coated overnight at 4 degrees with 10μg/mL of collagen I or fibronectin diluted in PBS. Coated dishes were washed in PBS. 1x10 5 cells were plated and imaged every 10 min for 17 h using a Nikon TE2000 microscope, PlanFluor 10x/0. 30 objective and equipped with a heated CO2 chamber. Images were analysed using Fiji software  16  (ImageJ v1. 48t). Individual cells were tracked using the mTrackJ plugin, and spider plots were generated using the chemotaxis and migration tool plugin (v. 1. 01).