dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , crowdsourcing  [4] , citizen science  [5]  and user-generated content  [6] , to name but a few. Despite their differences, these terms are often used interchangeably to capture the same basic idea of citizen involvement in carrying out various activities relating to geographic information science. These activities can be driven by the needs of a second party such as a commercial company needing to outsource micro-tasks or by researchers who need large datasets collected that would otherwise not be possible using their own resources. Citizens may be motivated to contribute for a diverse set of reasons. The participants involved might, for instance, feel compelled to contribute by a collective cause such as contributing to an open map of the world through OpenStreetMap  [7]. Another motivation is simply the desire to share information more widely, by, for example, placing georeferenced photographs online via a site like Panoramio or georeferenced commentary captured via Twitter.
The first objective of this paper is, therefore, to present a compilation of terms, providing some basic definitions and their primary attributions. The terms are then categorized according to active and passive contributions and to separate out spatial from non-spatial examples of user-generated content (Section 2. 1). This is followed by an analysis of the appearance of these terms in the literature (Section 2. 2) and for extracting profiles from Google Trends (Section 2. 3) to examine their emergence over time in both the academic literature and more popular science outlets. The second objective of this paper is to better understand the current state of mapping and spatial data collection by citizens through a systematic review of different online initiatives (Section 3). A similar approach was undertaken within the VGI-net project  [14] , which was a collaborative undertaking between the University of California, Santa Barbara, Ohio State University and the University of Washington, to classify sites related to the collection of VGI in order to study VGI quality and develop methods for analyzing VGI.
The results were reported in Reference  [15]  and showed that most of the sites were local in extent, appearing after 2005 when Google released its application programming interface, which facilitated online mapping. Moreover, more than 60% of sites were developed in the private sector and purposes ranged from geovisualization to sharing of geographic content. Using the VGI-net inventory of sites as the starting point, a similar review was undertaken here. The sites that were currently still active were retained and new online initiatives were added, which were then evaluated using a much broader set of criteria than used in Reference  [15]. These include: the thematic area in which the initiative fell (Section 3. 1); the nature of the spatial data collected (Section 3. 2); the level of expertise and training needed (Section 3.
I Map Hacking/Map Hacks/Hackathons (and Appathons) (1999) The term "hacker" has been used to refer to someone who tries to break into a computer system. A more positive use of the term is someone who can devise a clever solution to a programming problem; someone who generally enjoys programming; or someone who can appreciate good "hacks"  [33]. The term "Map hacking" has been used quite specifically in relation to computer/video games in which a player executes a program that allows them to bypass obstacles or see more of what they should actually be allowed to see-essentially a type of cheating  [34]. However, a positive usage relates to creating creative and useful solutions with digital maps, e. g. , see the book called "Hacking Google Maps and Google Earth" or "Google Maps Hack" or "Mapping Hacks: Tips & Tools for Electronic Cartography".
search for the missing Malaysian Airplane (flight MH370); over two million people helped search for the missing aircraft by analyzing satellite images  [50]. The rest of the search terms from Table  1  were then put into the Google Trends application. Some search terms do not register a trend with sufficient search volume to generate a graph, including neogeography or terms that are generally more restricted to the academic literature. The term mashup(s) shows considerable search volume but is not displayed here, since mashup is a generic term for integrating data from different sources and can apply to non-geographic applications such as those connected with music or video and, therefore, goes beyond the realm of just spatial mashups that are relevant to this article. Terms such as GeoWeb and web mapping pre-date 2005, which is around the time when Google Trends started.
However, there are at least three permanent sites that are noteworthy, i. e. , Ushahidi  [53] , which is a platform to allow people in affected areas to upload and view georeferenced information online, Tomnod  [54]  for crowdsourced damage mapping, and the humanitarian arm of OpenStreetMap  [55]. Although it is not possible to readily characterize sites by data volumes or number of transactions, there is a relationship between the way that the data are subsequently provided to the public (if access is open) and the amount of data collected. For example, the largest data volumes are often served using APIs (Application Programming Interfaces) as evidenced by sites such as OpenStreetMap, Geograph, Flickr and Twitter. Data volumes also tend to be higher in passively collected geographic information, notably, for instance, in relation to communications, location-based social media, or where sensors are used to collect data such as with transport, weather, hiking, or any site where there is a mobile application to facilitate data collection using mobile-phones or tablets, which is common in many ecology related applications.
Nature and Types of Crowdsourced Geographic Information If crowdsourced geographic information is taken to mean any data contributed by the crowd with a geographical reference that could potentially be mapped, the nature of the data can be characterized based on whether it falls into the territory of mapping agencies (or framework data) in the first dimension or axis as shown in Figure  6. Framework data are typically data that are collected by government agencies, which can be organized into the following themes: geodetic control, orthoimagery, elevation, transportation, hydrography, governmental units and cadaster, and which comprise the basic components of a spatial data infrastructure (SDI)  [15]. Depending on the country, these datasets may vary (e. g. , some countries do not have cadasters, while others may include a gazetteer as part of their SDI).
Data quality remains a topic of great interest and importance in this domain. In their work, the authors of  [64]  conclude that there is a trade-off between potentially improved data quality of crowdsourced geographic information and the requirement of facilitation and oversight which is resource intensive. Introducing overly burdensome structures to ensure quality could damage the potential contributions from related socially-conscious and citizen-focused data collection and mapping efforts. A review is provided in Reference  [65]  of the distinct types of citizen science projects and the expectations on the quality of the information they deal with, and in particular the quality of crowdsourced geographic information in those projects. They go on to propose an innovative model based on linguistic decision making for assessing the quality of a crowdsourced geographic information database created in citizen science projects.
There are examples where some minimal qualifications are required (e. g. , in some disaster mapping sites such as GEOCAN, a minimum number of years of remote sensing experience are required), which is checked in the registration process. However, the assignment of a reliability score to a user based on his/her experience, or to double-check any submission by a relative novice, does not seem to be commonly undertaken  [66]. The greatest evidence of quality control, however, was in sites related to ecology and weather, although map creation sites such as OpenStreetMap and Google MapMaker have a range of quality assurance measures including automated checking, peer review and use of multiple observations. Greater attention to quality was apparent for sites where the data are used for scientific research, with evidence of publications.
The literature review also indicates that the crowdsourcing of geospatial data is often most suited to complementing professional approaches and that research into conflation methods should be a key area of future research. Minimum data collection protocols that would facilitate the use of crowdsourced geographic information in a way that could complement authoritative data should be developed. Although the free tagging of OpenStreetMap could be viewed as an inclusive approach, it also means that there are inconsistencies in the way that the data are tagged, which limits their use for some applications (e. g. , land cover and land use mapping). Further work should also consider what conflationary methods have already been tested and where further developments need to take place. Data collection protocols could be built into the incentivisation strategy of crowdsourced geographic information sites, which is one further area of potential research.
Most of the sites focus only on collecting the data, which are then most often available through their website. It is rare that the projects also provide tools to easily and indiscriminately share the data. Even in those examples where services for sharing the data are provided, they are mostly in the form of "widgets" or "snippets" to integrate the data in a predefined form into another website, severely limiting the possibilities of data reuse (and thus the real power of crowdsourcing). The example of "real" sharing is obviously OpenStreetMap. Examples of predefined and limited sharing are Google MapMaker (only possible with the integration of Google Maps, which has server limitations) and Panoramio (only possible to include specific images). For those sites with large data volumes and demands for data, APIs are used to provide access.
Acknowledgments: The authors would like to acknowledge the support and contribution of  COST Action TD1202 "Mapping and Citizen Sensor " (http://www. citizen-sensor-cost. eu) and partial support from the  EU- funded  ERC   CrowdLand  project (No. 617754 ). Bin Jiang 's work is partially supported by  special fund of Key Laboratory of Eco Planning & Green Building, Ministry of Education  ( Tsinghua University ), China. The authors would also like to acknowledge  COST Action IC1203  "ENERGIC", in particular  Cristina Capineri  and  Sofia Basiouka , for development of a joint glossary to which the terms in Table 1 were originally contributed. A much condensed and modified version of Table 1 will be published in [92].
Microscopy and cell counting Images were acquired by using a Stereo Microscope Olympus SZX16 equipped with digital camera DP73, or a Zeiss Axio Imager Z2 microscope with structured illumination (ApoTome) equipped with Zeiss AxioCam MRm camera and processed using Axiovision software, or with a confocal laser scanning microscope Leica TCS SP5X equipped with a light laser, a 405 nm diode for ultra-violet excitation, and 2 HyD detectors. Gross histology and scoring of infected brain areas were performed with binocular stereo microscope. When a given brain region of interest (ROI) displayed GFP staining indicative of active RCMV infection, it was scored as a positive event and used to compute the infection index, meant to indicate the frequency of infection of a given brain area, independently of the number of GFP-labeled cells in this area.
For cell counting analyses, at least three adjacent brain sections were analyzed throughout the entire z-dimension for each sample using confocal microscopy. Images were acquired (1024 x 1024 pixels) using the 40x oil-immersion objective (NA 1. 30) and were composed of three channels corresponding to GFP (488 nm), Iba1 (568 nm) and ED1 (633 nm). Cells were counted manually in each brain with Image J Software  [12]  and the associated Cell Counter plugin at E17 and E20, and with Cell Profiler cell image analysis software (Broad Institute Cambridge, MA, USA) at P1. For each channel of stack images, mean image intensity was measured using "MeasureImageIntensity" module and cells were identified with "IdentifyPrimar-yObjects" module, in which Min and Max typical diameter of objects was set between 20 and 80 pixel units, respectively, and threshold was corrected by the mean intensity measured previously.
Then, using mask images generated with the aforementioned module, colocalization between channels was evaluated with "RelateObjects" and "MaskObjects" in order to determine the number of overlapping objects. Finally, data were exported to a spreadsheet containing the number of quantified cells for each staining and of colocalizations. This pipeline was validated on sample images before applying it to the whole set of experimental pictures. Activation index of microglia in a given ROI was defined as the ratio of the number of Iba1 + Ed1 + cells to the total number of Iba1 + cells. Morphology index of microglia in a given ROI was calculated as the ratio of the number of amoeboid activated, Iba1 + cells versus total number of Iba1 + cells.
at 4°C to prevent against nonspecific binding. Cells were then stained with antibodies against combinations of cell surface markers (Table A in S1 File) in PBS 1% FCS 2 mM EDTA for 45 min. at 4°C, washed twice in FACS buffer, and finally incubated in FACS buffer containg 2% (v/v) PFA. An average of 1. 3 x10 5 living singlet cells were analyzed per embryo equivalent on a BD LSRFortessa (BD Bioscience) cell cytometer and raw data were analyzed using FACSDiva V8. 0 software (BD Biosciences).
Time course of RCMV infection in the rat fetal brain The kinetics of infection by GFP-expressing recombinant RCMV within the fetal brain was analyzed after icv injection at E15 by detection of green fluorescent protein (GFP) in the infected cells (Fig 1 As expected and also recently shown in the case of its murine CMV counterpart  [9] , RCMV was increasingly detected in the infected brains after icv injection. At E16 (i. e. 24h after icv injection), no GFP was detected (n = 8 embryonic brains) (data not shown). At E17, only a few GFP-positive (GFP + ) cells were counted (n = 11 embryonic brains), and GFP Most RCMV-infected cells are of the myeloid or lymphoid lineages ; as E17 nearly corresponds to the end of the period when radial glia-guided neuronal migration occurs (E12-E18), lamination may only be altered unfrequently and at very localized areas in the present model.
Acknowledgments We thank  F. Michel  at  inMAGIC (INMED Imaging Centre ),  F. Bader  and  S. Corby  (INMED animal core facilities),  L Castelein  for technical support,  H Child  ( Oxford University, UK ) for his help as a trainee via the  BIOTRAIL international student exchange program , and all the administrative staff at INMED. We also thank  R. Charrel  ( EPV UMR_D190 ,  Aix-Marseille University, France ) for his advices at the onset of the project,  G. Grauls  ( Department of Medical Microbiology, Maastricht University, The Netherlands ) for the gift of anti-R44 producing hybridoma,  A. Fontbonne  ( UMR 7260 ,  Aix-Marseille University, France ) for his help in inner ear analysis, and  C. Sapet  and  O. Zelphati  at  OZ Biosciences (Marseille, France ) for their kind help and support.
This work was supported by  INSERM  ( Institut National de la Santé et de la Recherche Médicale ), by  ANR  ( Agence Nationale de la Recherche)  grant  EPILAND  ( ANR-2010-BLAN-1405 01 ), by  FRM  ( Fondation pour la Recherche Médicale)  grant (" Physiology and pathology of brain development ", call 2012), and by the  PACA (Provence-Alpes-Côte d'Azur) Regional Council. RC was a recipient of an  INSERM/PACA  ( Institut National de la Santé et de la Recherche Médicale / Provence-CD45 low/int ,  Alpes-Côte d'Azur )PhD fellowship and a recipient of a FRM ( Fondation pour la Recherche Médicale)  PhD fellowship ( FDT20140930813 ). TS was supported by the  Deutsche Forschungsgemeinschaft  ( SFB796 ).
Within the COST action EMBOS (European Marine Biodiversity Observatory System) the degree and variation of the diversity and densities of soft-bottom communities from the lower intertidal or the shallow subtidal was measured at twenty-eight marine sites along the European coastline (Baltic, Atlantic, Mediterranean) using jointly-agreed and harmonised protocols, tools and indicators. The hypothesis tested was that the diversity for all taxonomic groups would decrease with increasing latitude. The EMBOS system delivered accurate and comparable data on the diversity and densities of the soft sediment macrozoobenthic community over a large-scale gradient along the European coastline. In contrast to general biogeographic theory, species diversity showed no linear relationship with latitude, yet a bell-shaped relation was found.
, 2010; Magni et al. , 2013; Zettler et al. , 2014). Determining the patterns of biodiversity of benthic organisms and the factors that may explain them requires an integrated research strategy, which is beyond the tradition, capabilities and scales of classic research  (Heip & Hummel, 2000). The first ideas for such integrated research, involving a large-scale pan-European network of marine observatories, have been formulated during the FP5 (EC 5 th Framework Programme) project BIOMARE (Implementation and networking of large scale, long term marine biodiversity research in Europe), which was initiated by the European Network of Marine Research Stations (MARS). The subsequent FP6 Network of Excellence MarBEF (Marine Biodiversity and Ecosystem Functioning) adopted, and focused on, the integration of datasets and joint research  (Escaravage et al.
Because of the harmonised set-up of the EMBOS Pilots we can address these problems and compare data on a wide range of organisms from a wide latitudinal range. Moreover, since we also included environmental data (temperature, salinity, wave height, organic and mud content of the sediment) in our analyses we can elucidate whether the trends in diversity might be explained by these factors regardless of latitude. The results for the hard bottom benthic communities along the European coastline are reported in  Kotta et al. (2016, same volume)  focussing on relationships between cover, diversity and environmental factors, in Dal  Bello et al. (2016, same volume)  with regard to scale-specific variability in community diversity and abundance, and in  Puente et al. (2016, same volume)  focussing on the role of physical variables in biodiversity patterns of intertidal macroalgae.
The residues of the sieved samples were stained with Rose Bengal and preserved in 96% ethanol or 4% formaldehyde solution buffered with borax or hexamethylene tetramine. Thus, at each station a total of nine replicates per level were taken. Replicates at the three plots were taken in sediment of comparable granulometry, whereby the median grain size was in between 0. 06 and 0. 2 mm (sandy silt). The metadata describing the sampling campaign can be accessed at http://lifewww-00. her. hcmr. gr:8080/medobis/resource. do?r=embos_2014.
To estimate the variations of sea surface temperature (SST) and significant wave height (Hs), data from 1985 to 2013 were acquired by the Instituto de Hidráulica Ambiental de la Universidad de Cantabria (Fundación IH, Santander, Spain) from reanalysis sources at the nearest coastal point with information to the reference points. SST values were supplied, with daily temporal resolution, by the Operational Sea surface Temperature and sea-Ice concentration Analysis (OSTIA) dataset, which is under MyOcean2 project by UK-Met Office (NASA)  (Stark et al. , 2007). Specifically, the Group for High Resolution Sea Surface Temperature (GHRSST) L4 Gap-free gridded products have been used, with a spatial resolution of 0. 05º. The wave data used in this work come from the Global Ocean Wave reanalysis database (GOW), reflecting wave height in the open coastal zone  (Reguero et al.
, 2012). Hourly significant wave height data were extracted with a spatial resolution of 0. 125º for all sites. Salinity was obtained from in situ measurements provided by the World Ocean Database (WOD) of the National Oceanic and Atmospheric Administration (NOAA)-NESDIS National Oceanographic Data Centre (NODC)  (Levitus et al. , 2013). The salinity profiles used in this study were acquired between 1985 and 2014, and due to the sparse available data, for each station the salinity value was calculated as the average of all data points within a circle of 0. 4º radius in the first 40 m around the reference points.
Statistical analysis Differences in macrozoobenthic assemblages between regions and/or subregions were analyzed using a non-metric multidimensional-scaling (nmMDS) ordination model based on the Bray-Curtis dissimilarity matrix (Clarke & Warwick, 2001) calculated on the mean values among the three replicate samples within each plot. A one-way ANOSIM randomization/permutation test was used to check for the significance of differences among regions (i. e. Baltic, Atlantic and Mediterranean regions) in the ordination model  (Clarke & Warwick, 2001). A one-way ANOSIM was also used to check for the significance of differences among subregions (see Tables  2a  and 2b  for respectively the identification of the regions and the sub-regions). All data were square-root transformed prior to the analyses, to minimise the effect of dominant species  (Clarke & Warwick, 2001).
One of the plots from Cyprus (Softades-Larnaca1) was excluded from the analyses as no fauna was found in each of the three replicates. MDS 2D representations were considered acceptable when the stress factor did not transgress the value of 0. 2  (Clarke & Warwick, 2001). nmMDS and ANOSIM were conducted with the PRIMER v6. 1. 12 package  (Clarke & Gorley, 2006). Hierarchical clustering and ordination methods were performed to identify, analyse, and compare the multivariate pattern of soft sediment macrozoobenthic assemblages based on diversity and density descriptors (number of species (S), total density (n), Margalef species richness (d = (S-1)/ln(n)), Shannon diversity (H' = -∑pi(ln(pi)) and Pielou evenness (J' = (H'/ln(S)) per sample with pi being the proportion of species i in the sample) and environmental and geographic location characteristics.
Multivariate analyses based on community descriptors were used as an initial analysis solely based on densities for each of the species resulted in poor relations with environmental descriptors because the locations to compare at European scale each had a rather unique species composition with few species in common. A Principal Component Analysis (PCA), an indirect gradient analysis (i. e. ordination with optimal gradient distribution solely based on 'species' data, with plotting of environmental and location specific characteristics afterwards to identify potential relations), was performed on 'ln(x+1)'-transformed data, using the CANOCO for Windows version 4. 5 software package  (Ter Braak & Smilauer, 1998). A Detrended Correspondence Analysis (DCA) showed that the data had a short gradient length, which allowed the use of a linear ordination method.