dataesr/scientific-paragraphs-categorization huggingface dataset:
Outcomes of this mixed-method approach will be threefold: (i) establish the meaning of mobility in terms of spatial uses and daily routines, and highlight cultural differences in perceptions of mobility/behaviors between three national fieldworks, and (ii) uncover the complex relations between subjective experiences in personenvironment relationships and observed (GPS and qualitative surveys) ageing, mobility, and health behaviors, and (iii) provide guidance for interpretation of quantitative results, including for non-significant but theoretically expected associations or even for eventually counter-intuitive findings. The qualitative analysis software QDA Miner© will be used to code the interview transcriptions. The Miles and Huberman (1984) qualitative analysis principles will be applied on qualitative data in connection with GPS tracking and/or quantitative surveys outputs (e.
Complementary environmental predictors derived from GIS In each setting, participant's place of residence as well as reported activity locations and GPS data will be linked to existing GIS platforms that contain rich information on urban contexts. Whereas a preliminary assessment has allowed identifying a series of database that are common to all settings, further integration work will be conducted to optimize similarities between derived variables. Whereas some data will be strictly identical in nature (road networks for example), others will possibly require re-compilation for comparability purposes (e. g. land use categorization for calculation of walkability indexes).
Statistical analyses We will develop quantitative models integrating the collected data on daily mobility, social networks, physical activity and perception of places into geographic information systems to develop new indicators and spacetime modeling of health and place relations in regards to healthy aging. Mobility indicators will include multiplace measures based on time-geography principles, activity space indicators such as convex hulls, networks of usual places measures, and evaluation of daily trips. All available locational information stemming from the VERI-TAS questionnaire or the GPS monitoring will be linked through GIS to the built environment, including opportunities and barriers for walking or social participation. The outcomes of interest will be analyzed either continuously, in categories, and as binary variables.
Discussion This protocol offers key data collection components to unravel the complex interactions between daily mobility, social networks, urban environments, and healthy aging. Relevant data collected includes information on older adults' regular destinations, social network structure and in-person encounters, daily mobility, physical activity and sedentary behaviour, and qualitative assessment of mobility experiences, on top of more classical selfreported health and perception questionnaires. With such rich data, and contrasted urban settings in different countries, the expected findings are potentially of interest to knowledge users shaping the city and other groups working on the improvement of older adults' health. This protocolwith slight variations accounting for local specificitiesis currently applied in Canada, Luxembourg, and France.
Acknowledgments The team would like to thank all past and future participants of this study. The study was funded in Canada by the  Canadian Institutes of Health Research (CIHR)  and the  Fonds Quebecois de la Recherche en Santé (FRQS) , in Luxembourg by the  RECORD GPS Study  which received funding from the  Fonds National de la Recherche (FNR), and in France by Ministère de l'écologie, du développement durable et de l'énergie (MEDDE) ,  Direction générale des infrastructures , des transports et de la mer (DGITM), and  Syndicat des Transports de l'Ile-de-France  (STIF). YK holds a CIHR Chair in Urban Interventions and Population Health.
Thirdly, the contrast between the United States and the European Union in their treatment of intellectual property in databases, with the United States retaining the integrity and scope of copyright  (Feist, 1991)  and the European Union developing sui generis provisions for databases is considered. In this instance, the United States, a more recent formation than many European countries, may have had a greater and more extensive historical sense. The presentation concludes by pointing to the value of a fully theoretical informed, ecumenical but controlled, framework for understanding information developments.
Methods The study area extends throughout the Iberian Peninsula and the Balearic Islands (Fig. 1 ). The method used to reconstruct past climate variables is based on the probability density functions (PDFs) of plant taxa identified in fossil pollen records, and it requires a georeferenced distribution of modern plant taxa and a database of modern climate variables. PDFs for each taxon were built relating the modern distributions in the climate space geographically. The raw fossil pollen data were gathered from author's contributions and from the European Pollen Database (www. europeanpollendatabase. net). Each selected site fits quality criteria regarding the number of radiometric dates (> 3 in each site) and a sampling resolution of at least 200 years. Table  1. Origin and description of the data sources of fossil pollen used to reconstruct the climate in the Iberian Peninsula.
Source is either the European Pollen Database (EPD) or author contribution. Longitude and latitudes correspond to the centroid of the nearest cell to the site and altitude as extracted from WorldClim data set, all at 5 spatial resolution. Each site has information about the number of 14 C dates available, the temporal range covered (see also Fig. 1  for the spatial distribution) and the respective biome following the classification of  Olson et al. (2001)  Using these criteria we selected a total of 31 records which cover different time spans between 15 000 and 3000 yr BP (Table  1 ; Fig. 1 ). Although having the LGM as a lower limit would have provided interesting data, the availability of sites for the spatial interpolation is very limited before the Holocene. Thus, we focused on the 15 000 years when there are still 10 sites available that are necessary for a reliable spatial data interpolation (Table  1 ; Fig.
Data sources The current distribution data for 246 taxa were obtained by georeferencing the Atlas of Florae Europaeae  (Jalas and Suominen, 1972 , 1973 , 1976 , 1979 , 1980 , 1983 , 1986 , 1989 , 1991 , 1994; Jalas et al. , 1996 Jalas et al. , , 1999;; Laurent et al. , 2004). We gathered additional occurrence data for the Mediterranean flora from the Global Biodiversity Information Facility data portal (http://www. gbif. org/). These data were checked and corrected by removing species' presences from botanical and herbaria collections and/or observations with lower spatial resolution than 30 (∼ 55 km). The final taxa list and the assignment of pollen taxa to and modern taxa distributions is given in Table  S1. The georeferenced geographical distributions were rescaled to the resolution of 30 (∼ 55 km).
The global observed climate data  for January minimum temperature (T jan ), July maximum temperature (T jul ) and annual precipitation (P ann ) data were obtained from WorldClim database  (Hijmans et al. , 2005, www. worldclim. org) with 5 resolution (∼ 10 km). The climate data were downscaled to the same spatial resolution of the plant distribution data by aggregating the mean value to the resolution of 30. All computing was performed using R  (R Development Core Team, 2012)  with the package rgdal  (Keitt et al. , 2012).
In order to evaluate the robustness of the reconstruction method, we have compared modern reconstructed and observed climate data  from WorldClim database. For reconstructing climate from pollen data, we have used all samples available within the last 500 years. Climate values were averaged for all sites with more than one sample. The correlation between the two data sets was tested using a Pearson's correlation score. To provide the significance of the correlation value, a set of 999 replicates were performed where the observed climate variable was shuffled without repetition. Although this evaluation does not take into account neither the climate oscillations during the last 500 years nor the human disturbances, it still provides a broad evaluation of the reconstruction method because (1) it depicts per site the relationship between observed climate data with reconstructed values and (2) the slope direction of the regression and the related correlation signal indicate that the reconstruction is spatially coherent.
Climate stability was computed for each variable as the mean absolute deviance from the current climate as available in WorldClim data set. All analysis were performed using the R Project for Statistical Computing (R Development Core Team, 2012) with packages fields  (Furrer et al. , 2012) , rgdal  (Keitt et al. , 2012) , gstat  (Pebesma, 2004) , and fda  (Ramsay et al. , 2012). The climate reconstructions were performed with R scripts developed by the authors and available at request.
Data availability The distribution data for Mediterranean species  (Legind, 2011)  were downloaded from GBIF portal (http://www. gbif. org). The raw fossil pollen data  (Leydet, 2011)  is accessible for most used sites at the European Pollen database (http://www. europeanpollendatabase. net). For the remaining sites, data is available by request to the author of the respective publication (see Table  1  for a description of the data sets and sources). The climate data  (Hijmans et al. , 2011 ) is accessible at the WorldClim website (www. worldclim. org). The output reconstructions, anomalies and variance maps are available in NetCDF format as supplementary material.
The dissemination of textual personal information has become a key driver for innovation and value creation. However, due to the possible content of sensitive information, this data must be anonymized, which can reduce its usefulness for secondary uses. One of the most used techniques to anonymize data is generalization. However, its effectiveness can be hampered by the Value Generalization Hierarchies (VGHs) used to dictate the anonymization of data, as poorlyspecified VGHs can reduce the usefulness of the resulting data. To tackle this problem, we propose a metric for evaluating the quality of textual VGHs used in anonymization. Our evaluation approach considers the semantic properties of VGHs and exploits information from the input datasets to predict with higher accuracy (compared to existing approaches) the potential effectiveness of VGHs for anonymizing data.
For example, individuals may suffer from discrimination or identity theft. Likewise, organizations may suffer from negative publicity, fines or other sanctions  [18]. Hence, this data must be anonymized before being shared for analysis. Privacy-Preserving Data Publishing (PPDP) provides methods for publishing data without compromising the confidentiality of individuals, while trying to retain the utility of the data for a variety of tasks (e. g. , to feed data mining models, perform query answering, create decisionsupport systems)  [4]. Since all the potential usage scenarios for the data are commonly unknown at the time of publication, the produced anonymization solution should be useful enough to be adequately exploited by multiple data recipients. One widely-used technique of anonymization is generalization, which consists in replacing the original values of an attribute in a dataset with others that are less precise but semantically consistent  [21].
The idea is that the original data loses its specificity, which reduces the probability of re-identifying the individuals contained in the published datasets. For example, one could generalize the terms "oncology clinic" and "cosmetic surgery hospital" to "medical institution" to protect the sensitive whereabouts of a person. A common prerequisite of generalization algorithms is the use of Value Generalization Hierarchies (VGHs) to drive the anonymization. VGHs are tree-like structures that contain the original values of an attribute and their set of candidate generalizations, which constitute the anonymization solution space for the attribute. VGHs are usually created and evaluated by the data publishers (i. e. , anyone involved in the dissemination of data in a safe and useful manner; hereinafter referred as users).
Contributions. Considering these challenges, our research has centered on developing techniques to evaluate in an objective, quantifiable, and automatic way, the quality of textual data VGHs with the aim of improving their effectiveness for anonymizing data. In  [5]  we introduced the Generalization Semantic Loss metric (s-GSL), which captures the quality of a VGH with respect to its semantic consistency and taxonomy. However, s-GSL is based on a static strategy. That is, it assumes that, for a given set of domain values, there is an optimum "one-size-fits-all" VGH that would suit any input dataset containing such values. Hence, it does not exploit information from the input datasets. This makes s-GSL sensitive to data sparseness, which reduces its accuracy and limits its applicability. Also, s-GSL lacks a qualitative interpretation.
In this paper we extend our previous work  [5]  by proposing an enhanced VGH quality metric (d-GSL) which, based on a dynamic evaluation scheme, exploits the frequency distribution of the input datasets. In this manner, d-GSL can predict with higher accuracy the effectiveness that VGHs will have in anonymization. This enables users to select the best VGH (among all candidates) for a particular dataset. Hence, the utility of the anonymized datasets will be better preserved without sacrificing the privacy goal. We also experimentally show how d-GSL enhances the process of VGH evaluation. Finally, we propose a rating scale to help users to classify the VGHs based on their quality (w. r. t. d-GSL) into categories. Each category includes an interval and a qualitative descriptor to offer practitioners an intuitive interpretation of the d-GSL metric.
II. BACKGROUND AND RELATED WORK The collection and dissemination of large amounts of personal information have allowed organizations to benefit from the exploitation of this data. Therefore, a lot of effort has been invested into studying PPDP techniques. In a typical PPDP scenario  [4] , a trusted organization collects data about individuals for their own business reasons. However, this data may be shared in a sanitized form with third parties or in the public domain under various circumstances (e. g. , commercial use, research, legal reasons). The essence of PPDP is to release anonymized datasets that have good utility for a variety of tasks. Generalization is a commonly used technique in PPDP to achieve privacy. One advantage of generalization is that (unlike perturbation techniques that apply noise to data) it preserves the truthfulness of the data.
This property is achieved by using VGHs. An example VGH is shown in Fig. 1. The leaves at level 0 (L0) represent the original distinct values of an attribute in the dataset. The ancestors at upper levels (L1 to L3) correspond to the candidate values used for the generalizations. The root node (at L3) corresponds to the maximum generalization (or full suppression) of a value. VGHs are usually specified for the quasiidentifier (QID) attributes of a dataset, which are those that can be linked to external information and lead to the reidentification of people in published anonymized datasets. Although the aim of PPDP is to share anonymized data for legitimate (non-privacy-violating) purposes, a key assumption in this area is that attackers can also be found among the data recipients, who will intend to uncover sensitive information about individuals.
B. Static vs. Dynamic Selection of VGHs In the traditional anonymization process, the design and selection of VGHs are performed the first time that a dataset needs to be published by an organization. If a new dataset that belongs to the same domain (which has already been modeled) requires anonymization, the selected VGH usually remains static. This is because, as discussed in Section I, the complexity and cost of the VGH creation process can be significant, and thus may be unsuitable for an organization to carry it out periodically. Thus, the static VGH (which was the best according to the evaluation process) is used to perform the anonymization of subsequent datasets; assuming that such VGH will be the best regardless of the input dataset. However, using a single static VGH for all anonymizations may not be the best strategy in PPDP.
C. Dynamic VGH Evaluation Scheme Overview In our work, the quality of the VGHs is represented by a score, which dynamically adjusts to the distribution of the input datasets. This strategy would allow a more accurate evaluation of the VGHs and thus, a better prediction of their effectiveness to conduct the data anonymization. Fig. 2  depicts the contextual view of our solution in PPDP: (1) Organizations collect personal information and are required to share it under different circumstances. However, these datasets must be anonymized before being disseminated. (2) The users choose the QIDs to be generalized from the datasets. (3) For each QID, users create a set of candidate VGHs modeling the given domain and evaluate them based on their own knowledge and experience. In the traditional anonymization process, these tasks are performed manually, so they are time-consuming and error-prone.
Considering this, users need to have an efficient and effective manner of assessing candidate VGHs to decide which VGH best fits each input dataset. (4) Our proposed approach is a dynamic VGH evaluation scheme which captures, in a score denoted as d-GSL, the degree of data semantics that each VGH loses in their specification. The lower the d-GSL score, the less information loss incurred in the VGH. Our solution integrates knowledge bases and semantic similarity metrics. The knowledge base is implemented in the form of ontologies, which act as a gold standard in which the domain expert knowledge is reflected. Ontologies often represent the consensus opinion of a panel of experts thus, we mitigate the risk of having partial interpretations and single judgments over the domains represented in the VGHs.
The semantic content of the ontologies is exploited by semantic similarity metrics to measure the proximity between the original values of a dataset and its possible generalizations; resembling human behavior regarding the judgment of similarity between two terms. (5)  The output of our approach is one d-GSL score per evaluated VGH, which allows the users to compare the quality of the candidate VGHs and select the best for each data scenario (e. g. , those ranked #1). Our solution also defines a rating scale in which the d-GSL score can be mapped to a qualitative category to ease the interpretation of d-GSL for practitioners. (6) After evaluation, the best VGHs (in terms of d-GSL) can then be used to feed the algorithm that anonymizes the data with more guarantees that the chosen VGHs will help to better preserve the semantics of the original data, hence, retain the data usefulness.
D. Computing the Dynamic GSL Score In order to assess the quality of a VGH in terms of its semantics preservation and the input dataset, we evaluate the degree of information loss that could result from the transformations of the original values in the input dataset. To perform this, we first measure the semantic distance between the original values at the leaf nodes L = {l 1 , l 2 ,. , l n } and their corresponding candidate generalizations at the ancestor nodes of each level i of the VGH A i = {a i 1 , a i 2 ,. , a i k }. To consider also the dataset distribution, we consider the frequency of occurrence of the original values F = {f 1 , f 2 ,. , f n }. The semantic loss caused by the generalization of a value to its ancestor in level i is given by (1): D i j = f j • SemDist(l j , a i ) (1) where l j is the j-th leaf node of the VGH, f j is the number of times that the l j node value appears in the input dataset, and a i is the ancestor of l j in level i.
4b). This is useful because under some schemes of anonymization, the generalization occurs at the domain level, that is, all the original values end at the same level of the VGH (e. g. , full-domain generalization  [4] ). G(s 1 ,. , s n ) = ⎧ ⎪ ⎪ ⎨ ⎪ ⎪ ⎩ max i∈[i,n] s i , if G is maximum n i=1 s i n i=1 f i , if G is average (4a) (4b) Example 2. To illustrate the benefits of leveraging data semantics for VGH evaluation, let us consider a dataset where the selected QID to anonymize is occupation. The user has defined (based on her own knowledge) the VGH shown in Fig. 1  to perform the generalization of the QID. Ideally, more general terms are located at higher levels in the VGH and more specialized terms are lower in the VGH. However, there are imprecisions in the VGH which might not be easy to identify at first sight.
For example, the ancestor terms specified for "optometrist" are semantically incon- sistent. This is because although those terms refer to people involved in eye caring, "optometrist" is not an "eye-doctor" or "doctor". These type of issues can be easily detected by inspecting those transformations using our semanticbased approach. For example, the semantic loss caused by the transformations of "optometrist" to "eye-doctor/doctor" (computed with Eq. 1) are 0. 3846 and 0. 3333, respectively; which indicate a high loss. In contrast, the ancestors specified for "veterinarian" are correct, as the transformations to "medical practitioner/doctor" incur lower losses, which are 0. 0769 and 0. 037, respectively. Example 3. To illustrate the benefits of evaluating VGHs using a dynamic scheme (such as d-GSL) over a static one (such as s-GSL), let us consider three datasets (DS) containing 100 records that need to be anonymized.
These DS belong to the same domain but have different frequency distributions (as shown in the first column of Table  III ): DS1 has a uniform distribution of 10% of each term; DS2 has 90% of "optometrist" terms (poorly-specified branch) and 10% divided among the rest; DS3 has 50% of "veterinarian" terms (well-specified branch), 40% of "optometrist", and 10% divided among the rest. Next, consider that we first evaluate the quality of the VGH using s-GSL. In this case, we obtain the same quality score independently of the input dataset (as seen in the s-GSL column). In contrast, when using the d-GSL metric (computed with Eq. 2), we obtain different scores per dataset, as d-GSL considers the impact of the data distribution to evaluate the quality of the VGH. For DS1, both quality scores yield the same result as the impact of having a single instance of each term is the same as having n instances.
For DS2, d-GSL showed an increment in the loss incurred by the VGH, as in this scenario 90% of the data falls into a branch where the losses are high. Finally for DS3, d-GSL showed a small increment as the data is distributed between well-defined and poorly-specified branches. Having obtained different d-GSL scores for the same VGH shows the importance of considering information from the input datasets in the VGH evaluation process. Moreover, it shows that our dynamic scheme can capture more accurately the different dataset scenarios compared to the static scheme. As shown by the above examples, our approach brings multiple benefits to the users in the evaluation of VGHs, such as: (1) Inconsistent specifications introduced in the VGH can be easily identified (e. g. , misclassifications, redundancies); (2) A clearer differentiation can be made between terms that look similar; (3) Users do not have to depend on the limited availability of knowledge engineers and the associated cost; (4) Users mitigate the risk of relying on the subjective judgment of a single individual expert as the knowledge base is represented by consensual ontologies often created by a panel of experts.