dataesr/scientific-paragraphs-categorization huggingface dataset:
trentino. it more than a year ago, with an open license for ensuring free and unlimited use and reuse of data, representing the engagement of about 60 provincial departments. The catalogue is clustered into 13 broad categories, each consisting of a number of datasets, which are represented as one or more resources that are easily accessible and downloadable often in CSV and/or JSON format, and occasionally in XML. Each dataset can be mapped to DCAT 8 , a vocabulary designed for describing catalogues and datasets thereof for increasing interoperability. Just to cite a few of the published resources of high importance, there are provincial budget and cadastre. As shown by the result published by Dati. Gov. It 9 , as we write, the Province of Trento is the most performing catalogue in Italy in terms of number of published dataset, which confirm that the 3 http://www.
provincia. tn. it/progetto_open_data (in Italian) 4 "Libro bianco per il riutilizzo dell'informazione del settore pubblico", EVPSI Project (2012), http://www. evpsi. org/evpsifiles/bianco_beta. pdf; 5 CKAN Platform, http://ckan. org 6 Ckan API Client, git. io/ckan-api-client 7 Jackan, https://github. com/opendatatrentino/jackan 8 http://www. w3. org/TR/vocab-dcat/ 9 http://www. dati. gov. it/content/infografica approach of involving directly the people working in the PA with the creation of a concrete data culture is a successful approach.
Entity Type Generation This Section depicts an entity centric approach for modelling OGD. It describes the generation of entity types according to the data published in government data catalogues. An entity type (also called as eType) is the class of an entity that has the right amount of attributes and relations for forming the foundation of creating entities with their non-trivial details intended for an application  (Giunchiglia, 2012; Maltese, 2012; Farazi, 2008). Some examples of entity types are person, location, organization and facility. An entity is a real world physical or abstract or digital thing that can be referred with a name  (Giunchiglia, 2012). For example, Dante Alighieri (person), Trento (location), University of Trento (organization) and Trento Railway Station (facility) are entities.
In the context of this work, we have been dealing with the catalogue published by the PAT. As shown in Figure  1 , we divided the entity type development in three macro-phases -datasets survey, attributes survey and producing entity types -each of them with different macro-steps. Modelling starts with the dataset identification step which relies on the scenario or task at hand. For example, our scenario involves points of interest which include datasets representing among others refreshment facility (such as restaurant, pizzeria, bar, etc. ), recreational facility (such as ski lift, sports ground, museum, etc. ) and transportation facility (such as bus stop, railway station, cable car stop, etc. ). In the resource analysis step a rigorous study on the structure and content of the corresponding file(s) is performed to understand the relevant resources for singling them out.
The attribute analysis proceeds through examining the attributes of the already selected resources. With attributes, it means column headers in the CSV files, properties of objects in the JSON files and sub-tags under repeated object tags in XML. Attribute values are also analyzed in terms of availability (e. g. , always, frequently, sometimes and never present) and quality (e. g. , complete data with no or occasional syntax error, partial data with or without error and relational data with or without disambiguated reference) of the data. In the attribute extraction step, we differentiate between the kinds of attributes according to the data encoded in them. Some attributes are used for encoding data and some others for managing data (e. g. , identifiers internal to the resource used as primary keys).
Through semantification, we facilitate the integration of loosely coupled data. In the case of unavailability of a mandatory attribute in the possible resources, we signal it to the data provider as pro-sumers, see  (Charalabidis, 2014)  and do not allow the creation of the corresponding entity unless all necessary data are present. This is how we can improve the vertical completeness. Successive attribute value disambiguation step employs Natural Language Processing techniques for enriching dataset content by linking names to known entities (such as Dante Alighieri, Florence) and words to concepts (such as male, city). In Figure  5  we see a screenshot of long text that has been automatically enriched. OpenDataRise will show in red elements that still require manual intervention from the user.
Open Data Rise Within entity alignment step the framework considers rows in the dataset as entities, i. e. real instances. The goal of this step is to schedule changes to entity storage to be committed in the next step. Such changes can be either update of existing matching entities or creation of new entities with values from the source dataset. CC: Creative Commons License, 2014. In the last entity import step, the user can indicate the license of entities to import and other metadata to publish to CKAN. Updates and insertions are then committed to entity storage and a new semantified resource is published on CKAN. The resource will contain the provided metadata and a reference to imported entities in the entity storage.
Open Big Data Nowadays big volumes of data are processed at an increasing rate, creating additional hidden information. This information represents a central aspect in the definition of Big Data that can be defined as Value. In fact, according to some industry analysts, dealing with Big Data means facing the following aspects: Volume (huge amount of data generated or data intensity that must be ingested, analyzed, and managed to make decisions based on complete data analysis), Velocity (the speed at which data must be processed), Variety (the different types and sources of data that must be analyzed and the complexity of each and the whole), Variability (intended as the inherent "fuzziness" of data, in terms of its meaning or context) and indeed, last but not least, Value. Since the public sector is increasing the quantity of data available to the public through many open data initiatives, we expect that in the near future also collected data by these initiatives will thrive by the adoption of BigData technologies to gather useful information from published data.
As part of OGD initiative of the PAT, we then focus on the problem of data explosion and the consequent need of having fast and scalable solutions for storage and analysis. We estimate the trend for growth will be up to hundred times per year, easily reaching the order of TB of data in few years from now. For instance the Trentino portal already have sensors based datasets, such as weather, traffic sensors, real time energy consumption and few others that, they alone already provide few GB of data per day if collected. Due to this very nature, they pose challenge in using traditional relational database management systems to handle them and at the same time appear as a problem to be dealt with the Big Data technologies such as Apache Hadoop The interest here is focused on producing a Big Data platform directly integrated with the Open Data portal and able to provide useful analytics, historical analysis in reasonable time.
Applications Entities generated from open data as well as open big data exploitation resulted to the development of applications which often appear as innovations to the citizens. This is because they offer services that are either novel or come up with better results in comparison with the contemporary ones. Moreover, open data propel innovations that help devising novel applications and services  (Chan, 2013). In line with this consideration, as shown in Figure  6  and Figure  7 , we developed an application running on top of entities helps finding points of interest including restaurant, pizzeria and bar with opening hours and bus stop, cable car stop and railway station with timetable and ski lift, ski rental and ski school with timetable. Figure  6 : Faceted navigation for finding points of interest Figure  6  sketches faceted navigation.
Conclusion In this paper we depicted the approach we implemented in the publication of Open Data in the Province of Trento. The result that we have already obtained is promising and we show that the complement of the data culture along with a direct technical and legal support to PA employees allow a faster diffusion and sustainability in the process of opening data within the public administration. Furthermore, we proposed an approach for generating entity leveraging open government data grounded on entity based infrastructure that by design facilitates consistency in data representation and management. As such enabling the re-use of public sector information. We also address the forthcoming data explosion issue by investigating and integrating since the beginning BigData Technologies. The entity centric data representation and the infrastructure as a whole can be considered as an input to the W3C Data on the Web Best Practices Working Group to provide guidance to data publishers.
PrecisePrimer is a web-based primer design software made to assist experimentalists in any repetitive primer design task such as preparing, cloning and shuffling DNA libraries. Unlike other popular primer design tools, it is conceived to generate primer libraries with popular PCR polymerase buffers proposed as pre-set options. PrecisePrimer is also meant to design primers in batches, such as for DNA libraries creation of DNA shuffling experiments and to have the simplest interface possible. It integrates the most up-to-date melting temperature algorithms validated with experimental data, and cross validated with other computational tools. We generated a library of primers for the extraction and cloning of 61 genes from yeast DNA genomic extract using default parameters. All primer pairs efficiently amplified their target without any optimization of the PCR conditions.
INTRODUCTION Polymerase Chain Reaction (PCR) combined with molecular cloning has become one of the keystones in biology today. Deoxyribonucleic acid (DNA) constructions are especially important in systems biology to study the properties and interactions of molecular components in a cell. The technique is also a prerequisite for the entire field of synthetic biology in order to create new functions or chemicals in living organisms. In the 1990s, molecular cloning was a tedious procedure. PCR was something difficult to perform because polymerases had not been much optimized yet and custom primers were expensive. In this context, the very popular Primer3 program  (1)  was developed to help the experimentalist in finding the best and shortest primers with the good annealing temperature and Guanine/Cytosine (GC) content to amplify a gene of interest.
The algorithms from this software have been implemented in many other primer design tools dedicated to specific tasks such as designing primers in batches (2) designing specific primer pairs at genome scale  (3) (4) (5) , designing probes for real-time PCR  (6) (7) (8)  and detecting splicing variants  (9) (10) (11)  or Single Nucleotide Polymorphisms (SNPs)  (12, 13)  in different organisms. Similarly, a few private companies developed primer library design tools, such as Illumina DesignStudio  (14) , Agilent SureDesign  (15)  and Life Technologies Ion Am-pliSeq Designer  (16)  for next generation sequencing target enrichment. Starting from the 2000s, the development of high-fidelity polymerases combined with high-performance cloning techniques began to change the practices in molecular biology.
All of these tools, however, do not offer a primer designer integrating a proper melting temperature calculator capable of handling new generation highfidelity polymerases except for j5. Single-nucleotide precision cloning poses new constraints for primer design and leaves little room for primer sequence optimization, some work has been done to incorporate position constraints in the Primer3 software in a new version called Primer3Plus  (25)  with the function 'pick cloning primers'. However, this W206 Nucleic Acids Research, 2014, Vol. 42, Web Server issue software sometimes refuses to design the primer at the desired position unless the constraints are completely released. Hillson et al. (23)  noticed this problem and implemented an algorithm in j5 to progressively release the constraints of Primer3 each time it refuses to design the desired primer.
Primer design algorithm All the parameters described in the previous section are fed into the PrecisePrimer program. The primer design algorithm walks from the beginning and the end of each provided DNA sequences and analyses all the primer possibilities inside the tolerance window centred on the specified optimal melting temperature. Then, the program ranks the solutions and chooses the best primer pairs according to the constraints. It finally returns the pair of optimal primers, flanked by the 5 -extensions provided by the user. The program work-flow and algorithm are illustrated in Figure  1.
CONCLUSION PrecisePrimer is the first easy-to-use and experimentally verified batch primer design software offering simultaneously single-nucleotide precision, custom overhangs and preset buffers. It can be used for any restriction-digest, GoldenGate, Gibson or Ligation Independent Cloning (LIC) with the flexibility of a manual primer design and the possibility to deal with batches of sequences. The melting temperature calculation has been given particular attention to be compatible with high-fidelity polymerases, to comply accurately with experimental melting temperature data and to have improved thermodynamics in the context of PCR extraction on genomic DNA template. The performance of this primer designer software has been demonstrated experimentally by amplifying 61 DNA fragments from yeast genomic extract.
Reactor Technology (INR) at the Karlsruhe Institute of Technology (KIT) is investigating the application of the meso-and microscale analysis for the prediction of local safety parameters for light water reactors (LWR). By applying codes like CFD (computational fluid dynamics) and SP3 (simplified transport) reactor dynamics it is possible to describe the underlying phenomena in a more accurate manner than by the nodal/coarse 1D thermal hydraulic coupled codes. By coupling the transport (SP3) based neutron kinetics (NK) code DYN3D with NEPTUNE-CFD, within a parallel MPI-environment, the NHESDYN platform is created. The newly developed system will allow high fidelity simulations of LWR fuel assemblies and cores. In NHESDYN, a heat conduction solver, SYRTHES, is coupled to NEPTUNE-CFD. The driver module of NHESDYN controls the sequence of execution of the solvers as well as the communication between the solvers based on MPI.
This new trend permits a better assessment of the safety margins and contributes to reduction of the conservatism in the applied methodologies for core analysis. The availability of high performance computers allows an increase of the spatial resolution of the thermal hydraulics (TH) and neutron physical phenomena at very detailed scales paving the way for the possibility to directly predict local safety parameters  [4, 5, 7]. In this paper, the novel system NHESDYN (NEPTUNE-SYRTHES-DYN3-SP3) is presented. It consists of the coupling of the neutron kinetic code DYN3D-SP3  [8] , the CFD solver NEPTUNE-CFD  [9] , and the heat conduction solver SYRTHES  [10]. The main goal of this paper is to present the proof of the concept for the implemented coupling approach meaning that the MPI implementation of the communication between the involved solvers, the update of the cross-sections based on the thermal hydraulic local parameters, is working appropriately and that the new code is predicting physical sound results.
The far goal of this development is to have a coupled system able to describe both the thermal hydraulic and neutronic phenomena in LWR cores with fewer approximations than the legacy coupled codes such as RELAP5/PARCS  [11]  or ATHLET/DYN3D  [11]. Since the detailed description of the thermal hydraulics phenomena at macro-and mesolevel by the CFD codes can be very time consuming, though all CFD codes are parallelized, NHESDYN is developed within a MPI-environment. Similar efforts are being followed in different places combining different code versions and types, for example, ANSYS/PARCS  [11] , MCNP/STAR-CD  [12] , or DYN3D/ANSYS CFX  [13]. In this paper, the main features of NHESDYN to solve one single pin problem are presented since the application of NHESDYN to solve pin clusters, fuel assemblies, or even a reactor core is very challenging and too time consuming at the moment.
Coupling Methodology By coupling NEPTUNE-CFD with the heat conduction solver SYRTHES and the NK code DNY3D-SP3, a new code named NHESDYN was created. In this coupling approach, the codes are externally coupled using the Message Passing Interface (MPI) standards for the communication between them. In Figure  1 , a simplified communication scheme to understand the main information exchange during a time step is shown. This exchange has several stages. Finally, the power calculated by the NK code is passed over to the SYRTHES code to restart again the process. This methodology is repeated at each time step. The codes involved have to wait for the information generated by its predecessor to start the calculation and each code is normally executed one time. This kind of time hierarchy in the coupled solutions is called explicit.
General Coupling Approach. A general overview of the data flow between the codes and how it was implemented is described here. First of all, the first step for the communication among the codes is the creation of the MPI groups. A general scheme concerning the group distribution of the different codes is shown in Figure  2. This scheme defines the direct dependencies between the two TH codes involved and the NK code with the main program. The amount of information to deal with is high and to work with three different outputs is tedious. To unify the information provided by the codes an extra program is created; this is the main program (NHESDYN). It is programmed in FORTRAN and it was especially developed to control the time synchronization for the coupled solution and to monitor the progress of the coupled simulations.
NHESDYN generates DYN3D-SP3 as a subprocess (spawn operation) and creates a point to point communication with NEPTUNE-CFD. At the same time the NK code creates a point to point communication with SYRTHES. In the MPI, the point to point communication is a client and a server. In Figure  2 , the TH tools are considered like a single code. These codes were already coupled by the code developers (EDF and CEA) and the communication between them is done directly with no MPI interaction, for example, to solve the conjugated heat transfer. Additional information is shown in Figure  2  concerning the kind of information transferred (densities, temperatures, etc. ) which is classified by tags. In Figure  3 , a simplified flow chart to understand the function of each module in the coupled solution is given.
The NK/TH tools follow a specific order in the information flow while the main code (NHESDYN) controls the times and performs the convergence loops during some stages of the simulation. Considering that the NK code has solved the previous time step, the information (volumetric heat flux) is sent to the SYRTHES code. Then, the TH step is calculated and the conjugated heat transfer through the solid-liquid interface is solved. The information generated takes two different ways. The information from SYRTHES (Doppler temperature) can be transferred directly to DYN3D-SP3 because both are included in the same MPI group. The second way is followed by the information from NEPTUNE-CFD (moderator temperatures and densities) which is sent to the main code. Here, if it applies some operations are performed (e.
Execution of the Coupled Code NHESDYN. In Figure  4 , the different steps performed during the coupled solution are illustrated. The coupled simulation is started after a converged thermal hydraulic stand-alone NEPTUNE-CFD/SYRTHES run is performed. Since no steady option for NEPTUNE-CFD exists, a null transient simulation is performed for constant boundary conditions. The thermal hydraulic time step is used later on as a common time step for the other solvers during the iterative coupling approach. During the first iteration, NEPTUNE-CFD and SYRTHES provide the initial conditions for the neutron kinetic solver (DYN3D). With the TH information DYN3D calculates the power distribution. This loop runs till the predefined convergence criteria are achieved. DYN3D-SP3 first performs a steady state run and then the transient calculation is initiated.
Then, DYN3D SP3 updates the cross-section (Step 2) and calculates the power which is passed over first to the SYRTHES solver (Step 3). In Step 4, the TH calculation is  done; both TH codes advance one time step. For the next step, the convergence loop is followed. In Step 5, the information calculated by the TH codes is sent first to the main program. T 0 T 1 T 2 T n NEPTUNE CFD DYN3D-SP3 ϱ, T ϱ, T ϱ, T ϱ, T ϱ, T, D ϱ, T, D In case of a coupled steady state simulation, the convergence loop is executed after all the information has been collected by the main code. If the convergence criteria are achieved, then the TH parameters are sent to the NK code (Step 6). Step 7 follows which is analog to Step 2. Due to the fact that there are three solvers coupled to each other solving the fluid dynamics (NEPTUNE-CFD), heat conduction and transfer (SYRTHES), and neutronic (DYN3D-SP3), the coupled system may need a lot of iterations to converge since the TH parameters are very sensitive to any power changes.
Since SYRTHES uses the same time step as the other code solvers but it is not mandatory to do so, this time step can be modified to speed up the convergence. Hence, high values for the time step of SYRTHES (e. g. , 10 4 seconds) were selected in order to neglect the thermal inertia from the energy equation during the heat conduction solution. By this way, the temperature changes in the fuel will rapidly accommodate to the new power distribution. As soon as the NK code changes to a transient simulation mode, the time step of SYRTHES will depend on the NEPTUNE CFD solver, which defines the time step for all involved codes. During the coupled simulation, the NK code waits until the thermal hydraulic solver finalizes the simulation. The following thermal hydraulic variables are monitored and their relative changes will be used as convergence criteria: Doppler temperature, moderator temperature, and moderator density.
Both the TH and the NK solvers are executed in transient mode. To assure that the effects of the thermal inertia are accurately described, SYRTHES uses the same time step as the other solvers. In Figure  5 , the different steps of the time advancement of the involved codes are indicated. After the cross-sections have been updated in Step 7, the power is predicted by DYN3D-SP3 and it is transferred to the SYRTHES code (Step 8). With the refreshed power distribution the TH code advances one time step (Step 9). Then, the TH feedback parameters are sent to the main program (Step 10) which passes the information to the NK solver (Step 11) without any convergence loop. Based on this new information, this solver can again update the crosssections before solving the neutron transport problem for a new time step (  Step 12).