dataesr/scientific-paragraphs-categorization huggingface dataset:
To further validate its suitability for the PCM used in this study, further validation has been carried out in this work. Field measured data (both indoor and outdoor data) were collected from a greenhouse (with a length of 27. 0 m, a width of 5. 8 m and a height of 2. 3 m) located in Beijing, China. Figure  2  shows both the predicted air temperature by EnergyPlus and the field measured temperature in the greenhouse (maximum error: 2. 6 °C; mean error: 0. 1 °C; standard deviation: 0. 7 °C)  [25]. The version of DesignBuilder used in this study is V4. 2, which adopts EnergyPlus 8. 1 as the engine for thermal dynamic simulations. The same as in EnergyPlus, in DesignBuilder, PCMs can be modeled using some general thermal properties, e. g. , thickness, conductivity and density, with some specific properties, i.
Weather Data Two sets of weather data have been used in the study. The first was obtained at a real weather station located in Nottinghamshire at the East Midlands of England. The second set of data was from the Prometheus Project  [29] , from which a series of design summer years (DSY) forming weather data usable by EnergyPlus/DesignBuilder have been developed. The files extracted include those for both current design applications (mean temperature between years 1961 and 1990) and future applications (predicted weather data for years 2030, 2050 and 2080). In the analysis carried out later, the former data were used as the main weather data. However, due to the lack of current data at other locations within the UK and also the data in the future, the Prometheus project data were adopted when analyzing the influence from climatic parameters (Section 3.
2). Additionally, the month of July has been chosen for the study as it was found to be the hottest month of the year from the real measured data, so it was considered as the most suitable for summer overheating analysis. Additionally, July is also an official reference month for summer weather data in the UK  [30]. Energies 2016, 9, 605 6 of 16 The Nottingham dataset was monitored at Sutton Bonnington (Nottinghamshire, UK) in 2013, after the UK had started to suffer regular overheating in summer. Figure  3  displays two sets of temperature data between 1 and 31 July. One of these is the recently measured weather data at Sutton Bonnington (regarded as the current weather data), and the other is the mean temperature data measured at Watnall (Nottinghamshire), between 1961 and 1990. The latter is regarded as historical weather data, and is accessible from the Prometheus project official webpage  [29].
Both Sutton Bonnington and Watnall are located at a rural site on the west side of Nottinghamshire (12 and 18 miles from the city center, respectively) and separated by approximately 15 miles. analyzing the influence from climatic parameters (Section 3. 2). Additionally, the month of July has been chosen for the study as it was found to be the hottest month of the year from the real measured data, so it was considered as the most suitable for summer overheating analysis. Additionally, July is also an official reference month for summer weather data in the UK  [30]. The Nottingham dataset was monitored at Sutton Bonnington (Nottinghamshire, UK) in 2013, after the UK had started to suffer regular overheating in summer. Figure  3  displays two sets of temperature data between 1 and 31 July. One of these is the recently measured weather data at Sutton Bonnington (regarded as the current weather data), and the other is the mean temperature data measured at Watnall (Nottinghamshire), between 1961 and 1990.
The weather data used in this section were downloaded from the Prometheus project official webpage  [29] , as they provide a comprehensive number of weather data around the whole UK. The data chosen for this analysis were those measured between 1961 and 1990, which were developed for current design applications. Figure  7  compares the predicted indoor air temperatures when placing the case study building without PCM at the above three locations respectively. From the comparison, it clearly shows that overheating is much more serious at Southampton than at Aberdeen. However, at Aberdeen, there are still some cases when the indoor air temperature is going beyond the indoor maximum comfort temperature (i. e. , 25 ˝C), which would cause thermal discomfort. However, the frequency was not very high so the overheating issue in the northern part of the UK may be solved by other passive cooling techniques such as natural ventilation, which requires no cost (or a much lower cost than PCM).
and Southampton (50°54′ N). They are typical cities in the northern, middle and southern parts of the UK, respectively, and, as can be seen in Figure  6 , belong to different summer mean temperature ranges in the UK. The weather data used in this section were downloaded from the Prometheus project official webpage  [29] , as they provide a comprehensive number of weather data around the whole UK. The data chosen for this analysis were those measured between 1961 and 1990, which were developed for current design applications. Figure  7  compares the predicted indoor air temperatures when placing the case study building without PCM at the above three locations respectively. From the comparison, it clearly shows that overheating is much more serious at Southampton than at Aberdeen. However, at Aberdeen, there are still some cases when the indoor air temperature is going beyond the indoor maximum comfort temperature (i.
Building-Related Parameters Besides outdoor climatic conditions, many building-related parameters (e. g. , U-value, and building thermal capacity) may also influence the thermal performance of a building. This section, therefore, analyzed their influences on the PCM performance. The analysis was composed of three parts:  (1)  the location within the building where the PCM layer is added to;  (2)  various insulation levels of the external façade; and (3) whether the structure type is lightweight or heavyweight. In the following analysis, the weather data measured at Sutton Bonnington (Nottinghamshire) was used, as it reflects the current summer condition in the UK (at the East Midlands). Energies 2016, 9, 605 11 of 16.
Factors related to program management were assessed in the following areas: vaccination sensitization strategies and obstacles reported by parents, identification of obstacles to follow up and strategies to reach girls lost to follow-up, and difficulties related to supply chain management and data management. The number and type of national and international partners that supported the vaccination programs were also collected with respect to technical assistance, logistics, human resources and their training, financial support, and in-kind donations. Statistical analyses were descriptive, with variables expressed in percentages. Verbatim and qualitative data were collected during the site visits to the 10 institutions, and open-ended questions were included in the final online questionnaire.
Qualitative data were systematically analyzed. A directed content analysis approach was used to analyze the data and to identify the key themes. Two authors (JL, MHB) reviewed all qualitative comments extracted from the questionnaires and placed them into broader categories based on content and thematic saturation. Illustrative quotes have been included in this report along with information about the role that each survey participant played in the vaccination program. Key findings and quotes were compiled in a Microsoft Excel table and coded according to topic or theme. Responses were reported if at least two people gave a similar response. Final decisions on comments categories were discussed with a third author (EA).
5 %) did report difficulties with HPV vaccine supply chain management, including expiration of vaccine date (46. 2 %) and difficulties transporting vaccine to administration sites (38. 5 %) (Table  2 ). Data management and monitoring and evaluation systems were also investigated. Only one institution aggregated the data rather than collecting individual data for each girl vaccinated. Individual data were recorded in a registry and on individual records in 17 institutions (77. 3 %). Of those institutions with individual records, seven (31. 8 %) recorded the data in an electronic database. Overall, 82. 6 % of institutions managed their programs electronically. Sixteen institutions reported difficulties with the data management of vaccination programs, with late or incomplete vaccination reports listed as two of the most common challenges (Table  2 ).
BACKGROUND OF ATTENDEES The interest in the themes of automated generalisation and multiple resolution databases is increasing, as is evidenced by the number and background of participants. At the first NMA workshop, held in Barcelona in 2013  (Duchene et al. 2013) , there was a group of approximately 25 participants from 12 countries/regions, all from a regional or national mapping agency. This second NMA workshop was attended by a larger audience with diverse backgrounds: besides the 48 attendees from 17 NMAs, nine representatives from two software vendors were present, as well as four academics from different universities (see Figure  2 ). The workshop materials can be found here: http://generalisation. icaci. org/index. php/prevevents/11-previousevents-details/92-nma-symposium-2015-presentations Also the variation in lands of origin increased: attendants were from Belgium, Czech Republic, Denmark, Finland, France, Germany, Ireland, Israel, Latvia, the Netherlands, Norway, Poland, Spain, Sweden, Switzerland, Turkey and United Kingdom (see Figure  3 ).
STATE OF ART WITHIN NMA'S At the workshop, each NMA provided an abstract and presentation on the state of the art of "Automated Generalisation" within its own organisation. Details can be found in the corresponding abstracts and presentations:  (Augustýn 2015  Fifth, more and more NMAs have to cope with the incorporation of external datasets. Some of the NMAs do not have a full coverage in their source database, or policies are to combine data from multiple providers to one data set. This urges NMAs to look into efficient procedures for quality control and automation of manual procedures.
Progress: The progress in the implementation of automated procedures varies from NMA to NMA. Some NMAs have just started preparations and still have to face the challenge to create multi-scale data models and implement them in appropriate databases, e. g. relational or object oriented databases  (Augustýn 2015). Others have small-scale-generalisation processes implemented for years  (Kettunen et al. 2015; Lebiecki 2015) , and are now developing towards the design of a large scale architecture for generalisation. Novices in the field of automated generalisation, who have taken the hurdle of designing and implementing data models for their map data tend to start with automation of large scale databases (10K) to medium-scale databases (50k). Three NMAs have implemented fully automatic procedures  (Stoter et al.
Level of implementation of automated processes: The extent of implementation and thoroughness in implementation within NMAs varies. Most NMAs have implemented a certain form of automation in their workflows. The level of implementation varies from generalisation of certain features while still maintaining a manual workflow; semi-automated editing and generalisation to a fully automated procedure (like the aforementioned three examples). Most of the NMAs that have implemented semi-automated workflows planned to substitute these by fully automated workflows within the next two years  (2016) (2017) (2018). 5. Tailoring: While some NMAs use out-of-the-box tools only, most of them develop specific customisations of the out-of-thebox tools, often outsourced to the software provider. The main instruments to develop additional custom-designed workflows are geoprocessing tools and python scripts.
Incidentally, more advanced programming languages (such as ArcObjects or C++) are used to tailor generalisation processes. 6. Issues: Several NNMAs are facing obstacles to explore, develop or implement automated generalisation. For instance, NMAs lack the required amount of staff and budget resources. Also the role of several NMAs has changed from data collector into data distributor. This causes several NMAs to sincerely reflect upon their existing workflows, upon the use of external source data, quality demands, update cycles and processing units. It is not said these reflections necessarily cause the replacement of existing workflows (sometimes a considerable amount of time and budget has been spent and redesign does not improve the end results per sé), but it opens up doors to other solutions.
Objects in the most detailed map should always be identifiable. However UID's should not be created for derived small scale data, because: 1) given the current hardware and software environments, it is much easier to reprocess a derived dataset completely instead of managing the complex lifecycle information; 2) some argued that the analysis with generalised data should be discouraged, because generalisation decreases the quality of the data which takes away one of the requirements for UID's; 3) traceability is difficult to maintain and implement at a conceptual level specifically within multi-scale production workflows where objects are aggregated, typified, selected and deleted and in some cases also enlarged and displaced. To meet both points of view, it was suggested to use the URI for objects and to consider geometrical representations at several scales as attributes.
This sounded as an interesting suggestion, but then the question was: What is an object? Do we mean the object in real life or do we mean an object stored in the database? If the latter, how should an object be defined? And how to handle relationships between objects in the case of n-1 or m-n transformations? Uniformity or a general definition of objects appeared to be an illusion, since an object definition is dependent on the use case, user context and related target scale. Since a "one size fits all" solution was not an option, it was proposed to consider groups of users who can agree upon object definitions. These objects could be provided an URI or UID for the given context. This raised a new question: If we are to do this, are the members of the group willing to pay for these efforts of NMAs, since it will take a considerable amount of time and effort to first agree on these definitions and secondly to implement generalisation processes accordingly.
Fourth, Volunteered Geographical Information -initiatives such as Open Street Maps and commercial solutions like Google Maps, Bing Maps challenge the original tasks of governmental topographical datasets in the 21st century. ; Baella et al. 2015; Çelik & Simav 2015; Curtinot 2015; Faerch-Jensen et al. 2015; Fechir 2015; Frick & Johansson 2015; Goldner 2015; Haug 2015; Howland & Walters 2015; Käuferle et al. 2015; Kettunen et al. 2015; Lebiecki 2015; Madden 2015; Urbanke 2015; Wehrhan 2015). The paragraphs below provide a summary of the most significant items: (1) What are the drivers for studying and implementing automated generalisation? (2) How is the issue of generalisation approached? (3) How are the individual NMAs progressing in the field of automated generalisation? (4) Which level of automation is achieved in the generalisation process? (5) How much tailoring of out-of-the-box software is applied and at what expenses? (6) What are the open issues currently being met and/or addressed? 1.
The abrupt removal of subsidies is likely to lead to social unrest, as was the case during the spring of 1989 in the impoverished and neglected southern parts of Jordan, as well as in 1998 and 2002 in Ma'an, and in 2012 with huge demonstrations. Moreover, income inequality has risen in the last period, with a growing contrast between the Amman governorate and the rest of the country. This inequality is as high as in the occupied Palestinian territory, and bigger than in Israel which comparably proves less uneven (Fig. 6. 2). In Jordan, analysis at a more local scale worsens the picture: the income rise seems concentrated in the western Amman city, where most of the well-off neighbourhoods are situated (Fig. 6. 3). Map 6. 4 shows the added value of the pioneer ITAN database, which allows international comparison at a local scale.
A lesson derived from the Anima database on FDI used in the ITAN project, is that inflows do not only go to the richest areas. Indeed the large cities appear as major targets, but the geography is much wider spread than one could have thought. This means two things: (i) attracting foreign investment has become a shared goal and practise in these countries, confirming the Mediterranean partner countries' general openness to international exchange; (ii) FDIs are more inclusive than feared. In these countries, a tough debate has emerged between those in favour of international, namely European, influence upon their economy, and opponents saying that such FDI create too low a number of jobs and have no actual spill over effect on SMEs and territories. At least according to Map 6. 8, one can say that this territorial argument is not obvious.
Moreover, FDI amounts have fluctuated at the mercy of a small number of major transactions typically associated with privatisations. Only Turkey has successfully leveraged its FDI inflows as a virtuous circle of technological intensification on the part of investors to increase the export competitiveness of their operations in Turkey. In the other Mediterranean partner economies, FDI is directed excessively into tourism, real estate or one-off (by their nature) privatisation transactions and has not had the catalytic effect experienced in countries that support FDI inflows with strong savings and production-oriented economies. These figures help in understanding the overall low connection between Europe and its neighbours, as shown in Chap. 2. The Anima database confirms the previous statement derived from UNCTAD data.
, 2006). At the end of summer 1991, the 5300-year-old Tyrolean Iceman mummy emerged from the ablating ice field of the Tisenjoch, a saddle at 3210 m near the Italian-Austrian border in the eastern Alps  (Seidler et al. , 1992). The excellent state of preservation of the Tyrolean Iceman provides strong evidence for the minimum coverage of this ice field at this time, which has only recently been surpassed. This discovery suggests that past atmospheric temperatures characterizing warm phases such as the Roman (250 BC-400 AD) and the Medieval (950-1250 AD) periods may have never exceeded that of the current time in this sector of the Alps  (Baroni and Orombelli, 1996). However, a recent chironomid-based summer air temperature reconstruction from Lake Silvaplana, in the nearby Upper Engadine (Switzerland), documented that the 20th century and the Medieval period (from 1030 AD, start of the dataset, until 1260 AD) were both 1 • C warmer than the modern climate reference period  in the eastern Alps  (Larocque-Tobler et al.
Bedrock topography Ground penetrating radar (GPR) was used to determine the bedrock topography (e. g. Binder et al. , 2009; Moran et al. , 2000)  and to infer information about possible englacial features (e. g. Blindow and Thyssen, 1986; Konrad et al. , 2013). GPR profiles of 50 MHz were collected with a GSSI SIR 3000 system during July 2013. This spatial survey focused on a region of 50 × 50 m, including the four 2011 boreholes with an inter-profile distance of 4 m (Fig. 1c ). Based on the available continuous snow/firn/ice density data from a snow pit and from borehole no. 2 (BH2), a 1-D velocity function was derived from the correlation between snow/ice density and dielectric permittivity by  Kovacs et al. (1995). The twoway travel times (TWTs) chosen were converted to depth with the 1-D velocity function, and interpolated to continuous surfaces.
e. increased potential for contamination due to a higher number of steps during sample processing). 14 C analyses were conducted using the compact radiocarbon Accelerator Mass Spectrometer (AMS) system "MI-CADAS" at the University of Bern (LARA laboratory). For details about sample preparation, WIOC separation, blank correction and calculation of F 14 C, see  Uglietti et al. (2016). The conventional 14 C ages were calibrated using OxCal v4. 2. 4 software (Bronk Ramsey and Lee, 2013) with the IntCal13 calibration curve  (Reimer et al. , 2013). Dates are provided as radiocarbon ages (yrs BP), calibrated radiocarbon ages (yrs cal BP) or as years before the date of sampling, with 2012 being the closest approximation of a full year (yrs b2012 = 2012-1950 + yrs cal BP). Average values (µ) are presented with a 1 sigma (σ ) uncertainty.
DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe , a Python package for DREAMTools evaluating DREAM challenge scoring metrics.
Amendments from Version 1 One issue about the first version of the paper was related to the installation of the software itself. Indeed, dependencies on several (established) Python scientific software required long compilation time and sometimes compilation failure. This is a general issue but following comments and discussions with the referees, we decided to explore a solution based on Anaconda (http://anaconda. org) and we updated the software accordingly. As compared to the previous version, we now provide pre-compiled versions of DREAMTools for Linux and MacOSX platforms. We do so via the bioconda channel (https://bioconda. github. io/) of Anaconda.
Scoring methods developed by challenge organizers are reported in the publications that describe the challenges, but the corresponding code is typically provided only in pseudo-code or at best as a script in an arbitrary language (R, Python, Perl. ) and syntax by different developers leading to a set of heterogeneous code. In addition, templates and gold standards need to be retrieved manually. All of these factors present obstacles to maximize the scientific value of DREAM challenges as a framework for a posteriori evaluation of a method's performance in comparison with those used in the challenges. Similarly, reuse of scoring code for future challenges becomes complicated when at all possible. To facilitate the a posteriori use of the challenges resources by the scientific community, we have gathered DREAM scoring functions within a single software called DREAMTools that provides a single entry point to the DREAM scoring functions.
We also provide a standalone executable for end-users and the ability to share and re-use existing code within a common framework to ease the development of new scoring functions for future challenges. DREAMTools does not provide code to generate the data or to manage leaderboards (which happens within Synapse), but focuses on the scoring functions. Note that organizers interested in setting up automatic scoring and publishing of leaderboards should instead refer to the section "Create a Scoring application" from the Synapse project 2453886. Currently, DREAMTools includes about 80% of the past challenges. For a few challenges where integration in DREAMTools was not possible, references to external resources are provided. Here, we first describe the framework used in DREAMTools software from the point of view of both an organizer/developer and an end-user (see Figure  1 ).