dataesr/scientific-paragraphs-categorization huggingface dataset:
COnClusIOns Although major progress has been made in recent years regarding clinical trial registration, we show here that the most frequently used CTRs are still lacking in completeness and consistency of information. In addition, the process of distinguishing commercial clinical trials from IICTs is not straightforward. Simplifying the identification process of IICTs through the databases would facilitate a direct and clear comparison of performance in each country in terms of the quality of the trials; it would also help to identify the possible funding sources available to investigators.
A use case from the Euro-Argo RI will be used to demonstrate how DRIP can automatically select and provision virtual resources, deploy services, and optimize the delivery of observation data to a number of destinations in the cloud. In the following section, we examine the scientific and technical background of this work in more detail. We describe our implemented system in Section 3 and our specific use case in more detail in Section 4. We evaluate our experiments in Section 5 and finally discuss our conclusions in Section 6.
A CASE STUDY IN EURO-ARGO RESEARCH INFRASTRUCTURE Environmental and earth science RIs support their target communities by acting as data hubs and publishers of scientific data. The Euro-Argo RI is a typical example, being the European contribution to the Argo programme ##. Argo monitors the world's oceans measuring temperature, salinity, pressure, etc, via the distributed deployment of robotic floats to create a roughly even network of data collecting nodes across the marine surface of the Earth. These floats periodically send data back via satellite to data assembly centers, which provide integrated, cleaned data products to various regional centers, archives, and research teams; all data are then made publicly available via a common portal within 24 hours of acquisition. Euro-Argo is now prototyping a subscription service for their data.
A data subscription service In the data subscription service being now developed in Euro-Argo, investigators subscribe to customized views on the Argo data using a dedicated data subscription service, selecting specific regions and time-spans, and choosing the frequency of updates. Tailored updates are then provided on schedule to investigators' private storage; Euro-Argo provides the infrastructure services needed for computing data products to match each subscription and then dispatches those products to their destinations. A functional depiction of the Euro-Argo data subscription service is illustrated by Figure  4 , with the details of the implementation architecture reserved for the next section. Data subscription is a good example of the kind of scalable, customized data service that RIs are now looking at as a way to better serve their communities, which requires some degree of optimization of the underlying distributed infrastructure.
As the volume of subscriptions and the customizability of subscriptions increase, so does the pressure on the underlying infrastructure providing the data, the bandwidth for transport, and the processing. At the same time however, there will be periods of low activity between rounds of updates. Thus, we need a scalable infrastructure to support the data subscription processing pipeline, so as to not unnecessarily tie up resources while still permitting acceptable QoS during peak periods. Beyond the parameters of this particular scenario, we also should consider how we manage the e-infrastructure used by RIs where there may be multiple data subscription pipelines for different kinds of data, or indeed how to easily configure and deploy new pipelines should other RIs want to replicate the Euro-Argo system for their own scientific data sets.
Service prototype We prototyped the data subscription service based on the architecture depicted in Figure  5. Currently, the resources from e-infrastructures such as EUDAT and EGI Federated Cloud (FedCloud) are used. Figure  5  shows the use-case scenario based on the use of EUDAT and EGI services. In The architecture of the Euro-Argo data subscription service with the Dynamic Real-Time Infrastructure Planner (DRIP). The subscription service invokes DRIP to plan, provision for, and deploy the subscription data processing pipeline. Subscriptions and processing are event driven, triggered by updates pushed to the B2SAFE data store. The deployment is scaled with demand FIGURE 6 Deadline aware autoscheduling flow. As soon as the global data assembly center pushes out new data, the process begins.
Input partitioning Before attempting to partition input parameters and distribute them to worker nodes, we must first identify which parameter is responsible for the most computing time when generating the data products. To do this, we generated a set of tasks on a region of randomly selected raw data requiring computing of all parameters. We performed 550 tasks spanning the Mediterranean Sea while requesting data in a time window from 1999 to 2007 and covering more than 400 possible parameters in the data products. We executed these tasks on identical VMs and measured their execution time to determine the correlation between area, time range, and the number of parameters with execution time. Additionally, we investigated the effect of end date on execution time, eg, whether execution time changes when processing three months of data leading up to 1999 rather than leading up to 2007 (indicating a general shift in the typical volume of data collected at different points in time).
Discussion The results presented here demonstrate that a linear partitioning strategy can provide nonlinear variations in speed and efficiency. This can be attributed to an unequal load distribution, where some workers were assigned far smaller loads than others, despite the data being split 'evenly' across a certain dimension. In the case of the Euro-Argo data set used for this experiment, this is because more recent data samples contain more data than older samples (due to improvements in data acquisition over time), which explains why the logarithmic partitioning performed better. However, the recorded speedup can be improved further if the partitioning is calibrated based on the actual end date selected for a sample. Moreover, a more linear speedup could be achieved if the partitioning is performed based on all input dimensions, rather than just one.
CONCLUSION AND FUTURE WORK In this paper, we have presented our DRIP microservice suite for optimizing the runtime QoS provided by a data service deployed dynamically on a virtualized e-infrastructure, with a particular focus on time-critical constraints such as deadlines for delivering data to a distributed set of targets. We demonstrated how DRIP can be used to automatically select and provision infrastructure resources, deploy services, and optimize the runtime quality for a data subscription service based on a study case involving the Euro-Argo research infrastructure. We demonstrated how to select an optimal strategy for partitioning the input tasks into workers using a modicum of expert knowledge concerning the specifics of an application. The results clearly show the value of integrated systems such as DRIP for dynamic optimization of data services in research support environments and how, with further investigation and development, they might be used for a number of similar applications cases involving distributed services and large, dynamic data sets.
Motivated by the aforementioned problem facing garment industry, this research, aims to forecast garment sales by exploiting twitter data and investigating its influence of twitter as a social media on garment sales. Focusing into line, this study presents a hybrid sales forecast model "Social Media Based Forecasting (SMBF)" which is a fuzzy time series forecasting model that maps historical information of the sales and the "Impact of sentiments from social media"(Fuzzy Sentiment Impact on Sales [FSIS] model) on garment sales. This research uses real twitter data and historical sales data for an Italian fashion brand, and applied developed model to examine its forecast performance. We believe that this paper is the first endeavor to use sales forecast model on social media data in a fashion industry.
Research Framework Research outline is shown in Figure  1 , which includes sales prediction exploiting social media data and sales data in the following steps: I. Data Collection-To accomplish the task, Twitter and sales data spanning six months were collected from Italian fashion brand. Figure  1  Research framework. III. Examine correlation-Extracted sentiments from earlier stage were aggregated weekly and the relationship between the sentiments of the current week and sales of the upcoming week was examined. This step was carried out to investigate relationship between tweets and sales. IV. SMBF Model-After exploring the relationship between tweet sentiments and sales, this model is designed to forecast garment sales based on historical sales and sentiments from social media. To attain this, FSIS model was built to identify the uncertainty of sentiments in terms of corrective coefficient (V s ), which represents the sales variation factor with actual sales and this is implemented on Exponential Forecast (EF) to predict the final forecast from SMBF model.
Data Collection Data used in this work was collected for one of the Fashion apparel brands, spanning time period of six months from April 2018 and September 2018. This study uses two data forms, first, Twitter data, which were collected using tweet API by creating tweet app to get authentication key  [22]. Open source tool "Rstudio" and its package "TwitterR" were used to request API to Twitter using Oauth access  [20]. Semantic keywords with hashtag "Brand name" were applied on tweet retrieval string. Collected tweets attributes is shown in the Table  1  and total number of tweets collected for six months interval was 838 as shown in Table  2. In the next step, sales data for the same interval of time as the Twitter data was collected, which accounts for the daily sales of garments.
Twitter Data Preprocessing Twitter data collected in previous step was in an unstructured form as it contained many duplicates, urls, punctuation, etc. It was necessary to clean and normalize tweet for analysis. As the first step of preprocessing, manual screening of tweets were performed to confirm whether or not it belongs to the fashion apparel brand that we targeted. While exploring the tweets, synonyms of selected brand name were found in some tweets. Moreover, some of the tweets were also related to politics, games and some other issues, and therefore such tweets were removed. Secondly, as only text is required for sentiment analysis, first attribute shown in the Table  1  was chosen. Text mining was applied to remove duplicates, whitespaces, hashtags, urls, stop words, retweet entities and numbers  [20].
• P (X) is the prior probability of class. • P (Y) is the prior probability of predictor. For the mathematical formulation of this research task Twitter dataset T is considered, which is a set of tweets t and can be represented as T = {t 1 , t 2 , t 3 … … t n }. It was converted into corpus using text mining as explained in the Section 3. 3. This corpus is a structured form of text, and is applicable for mathematical operations. Hence, each tweet could be considered as a set of words, i. e. ,t = {w 1 , w 2 , w 3 … … w n }. Similarly, Lexical labeled dictionary or dataset L  [24]  is composed of two attributes: words ( W ′ ) and sentiment class (C), and it is represented as L = {W ′ , C}, where, W ′ = w ′ 1 , w ′ 2 , … w ′ n is the set of words in lexicon dictionary and C = {c 1 , c 2 , c 3 … … c n } is labeled sentiment class in lexicon dictionary.
The sample of the lexicon dataset is shown in Table  3. Lexicon dictionary is labeled with two classes such as "Positive" and "Negative. " For each given tweet t in Twitter dataset T, NB computes posterior probability of classes of sentiments, c ∈ C, where C = {positive, negative} and assigns the predicted class Ĉ , which has the maximum posterior probability to the word of a given tweet. Accordingly, NB algorithm is represented in Eq. (  1 ) can be rewritten as shown in Eq. (  2 ). Ĉ = argmax c∈C P ( c|t ) = argmax c∈C P ( t|c ) P (c) P (t) (2) Further, from Eq. (  2 ), denominator is dropped as P(t) will remain same for all sentiment class and it will have an impact on "argmax"  [26]. Therefore, it can be transformed as shown in Eq. (  3 ). Therefore, Eq. (  3 ) can be rewritten as below Ĉ = argmax c∈C P ( t|c ) P = argmax c∈C P ( w 1 , w 2 , w 3 … ….
w n |c ) P (c) (3) Ĉ = argmax c∈C P (c) ∏ w∈t P ( w|c ) (4) This model follows the assumptions: position of the features (words) are not taken into account and the feature probabilities P ( w n |C n ) are independent of the given class c ∈ C  [26]. "Prior" and "Likelihood" probability values are calculated for the occurrence of the most probable class and score is assigned. Furthermore, formalization of the above equations was implemented for the ease of applying it in programming language illustrated in the Figure  2. It is assumed that the algorithm will calculate the maximum posterior probability for the occurrence of a word in a given tweet that is present in a lexical dataset. Therefore, P (c) is calculated, which is a prior probability of sentiment class and it can be calculated using the below Eq.
The common way to combat this problem is to write the probabilities in "log-probabilities. " This transformation is considered due to number of reasons  [27] ; • The value of probability lies between 0 and 1, whereas log-probability can lie between -∞ and 0. • The log-probabilities are easily comparable between smaller and larger numbers, that argmax does in probabilities for example m < n, then ln (m) < ln (n). • Arithmetic works well, i. e. , logarithm of the multiplication of the variable is the addition of the logarithms, i. e. , ln (m. n) = ln (m) + ln (n) Logarithmic representation of the Eq. (  4 ) is represented below comprehensively with the final Eq. (  9 ). Ĉ = ln [ count (w, c) + 1 ∑ w ′ ∈W count ( w ′ , c ) + 1 ] + [ Nc N ] (9) Algorithm presented in Figure  2  assigns score to a word according to its frequency that matches with the words in a lexical dataset.
Best Fit Tweet Greater than 1 Positive Less than 1 Negative Equal to  1  Neutral Figure  2  Algorithm for assigning score to the word class. = Smoothing constant 0 ≤  ≤ 1 A t = Actual data F t = Forecast data for time t Sales data of fashion garment industry was aggregated weekly and an EF was performed. "Corrective Coefficient (V s )" was calculated, which represents the variation in sales as shown in Eq. (  13 ). V s is used as an output variable for FSIS, mapping the impact of tweets optimizing the predicting ability of the forecast model. Corrective Coefficient (V s ) = Actual Sales -EF Sales Forecast EF Sales Forecast (13).
(c) Knowledge base-It has a database of fuzzy set and defined rules. "Facts" are symbolized through linguistic variables and reasoning is followed by defined if-then rules. (d) Inference Engine-Rule inference is managed by inference engine, where human knowledge can be combined without any difficulty using linguistic rules. (e) Defuzzification-Aggregation of all fuzzy sets to give the single output to draw a conclusion. Centroid method is used for obtaining the crisp number as shown in Eq. (  15 ). x * = ∫  P (x). xdx ∫ 𝜇 P (x). dx (15) Model design of FSIS involves above steps and this is explained in Section 3. 6. 2 with input parameters as sentiments and output parameter as corrective coefficient as explained in the Section 3. 5, and corresponding defined membership functions.
Defining universal and fuzzy set for sentiments for FSIS For formalizing and modeling twitter data with garment sales performance, we used intelligent fuzzy technique as discussed in the Section 3. as V s (Corrective coefficient). Triangular membership function is assigned to input variables in the range between [0 1] and output variable in the range [-  25 25]. Aggregated weekly sentiment data were used for all three input variables to feed into model. The key objective in modelling sentiments, viz. , S p (Positive); S n (Negative); and S ne (Neutral)retrieved from tweets with fuzzy inference is to capture human emotions on social media weekly and to forecast the sales variation based on these consumer emotions. Human emotions are uncertain and they change from time to time. So, for example, maybe in one week, sentiment "Positive" could be high and it could lead to a positive effect on sales of upcoming week.
Tweet Classification Tweets were assigned sentiment class as explained in Section 3. 4. Logarithmic values of "Prior" and "Likelihood" were calculated of the lexicon dataset  [24]  and it is shown in the Table  9. The total count of the words labeled "Positive" is 2312 and "Negative" is 3324 in lexical dataset. (To see in detail) Detailed mechanism (of how the) of algorithm is explained in section 3. 4. (Let us take one) Consider, for example, in a tweet, "style made genuine matching lining," the word "genuine" exists in the lexical database as "Positive, " therefore, the score for this positive word in a tweet will be calculated using Eq. (  10 ). As there is only one positive word that matches with the words in lexical dictionary and not with the negative word in a tweet, the score for the"Positive" class will be calculated by using logarithmic value in Table  9  as "0.
DISCUSSION This study is conducted on real tweets and sales data of fashion brand for spring and summer assortment. When collected tweets were analyzed after cleaning, the number of tweets was reduced to only 37. 5% of the actual tweets in the collected data, which indicates that this brand is in establishing phase on social media. Data pre-processing of tweets was done in two ways: manually; and using text mining to ensure that the collected tweets belonged to the chosen brand. As the enhanced version of lexicon dataset (NRC lexicon) was used, NB classifier classified tweets quite satisfactorily. Most of the tweets were classified as "Positive" and "Neutral, " which represents the popularity of the brand amongst its consumers. This research investigated and established the relationship between the social media data and sales data.
(4) The Newton algorithm stops when the norm of the residual divided by the norm of the external forces vector is smaller than a user-provided tolerance, denoted HFM Newton. In Equation (  2 ), f , T N , u k and y from (4) are known quantities and contain the time-dependency of the solution. Notice that the computation of the functions u k , y → σ (u k ), y and u k , y → K (u k ), y requires solving ordinary differential equations, whose complexity depends on the behavior law modeling the considered material. In our application, the quantities of interest are not the displacement fields u, but rather the dual quantities stress tensor field σ and cumulated plasticity field, denoted p. The finite element software used to generate the high-fidelity solutions u is Zebulon, which contains a Domain Decomposition solver able to solve large scale problems, and the behavior laws are computed using Z-mat; both solvers belong to the Z-set suite  [36].
If the dispersion around the learning data is small for certain values E p µ , then adding 3 times the standard deviation will not change very much the prediction, whereas for values with large dispersions of the learning data, this correction aims to provide an error indicator larger than the error. We use the GaussianProcessRegressor python class from scikit-learn  [39]. Notice that although some operations in computational complexity dependent on N are carried-out, we are still in the offline stage, and they are much faster than the resolutions of the large size systems of nonlinear equations  (2). If the offline stage is correctly carried-out and since E p µ is highly correlated with the error, only small values for E p µ are expected to be computed. Hence, in order to train the Gaussian process regressor correctly for larger values of the error, the reduced Newton algorithm (  5 ) is solved with a large tolerance ROM Newton = 0.
Acknowledgements Discussion panel:  François Alla ,  Pierre Arwidson ,  Pierre Blaise ,  Christopher Bonell ,  Isabelle Boutron ,  Linda Cambon ,  Rona Campbell ,  Patrizia Carrieri ,  Franck Chauvin ,  François Dabis ,  Nancy Edwards ,  Christine Ferron ,  Marie-Renée Guevel ,  Nadir Kellou ,  Joëlle Kivits ,  Antony Lacouture ,  Thierry Lang ,  Susan Michie ,  Laëtitia Minary ,  Graham Moore ,  Grégory Ninot ,  Kareen Nour ,  Jeanine Pommier ,  Louise Potvin ,  Lehana Thabane.
Community biological databases provide an important online resource for both public and private data, analysis tools and community engagement. These sites house genomic, transcriptomic, genetic, breeding and ancillary data for specific species, families or clades. Due to the complexity and increasing quantities of these data, construction of online resources is increasingly difficult especially with limited funding and access to technical expertise. Furthermore, online repositories are expected to promote FAIR data principles (findable, accessible, interoperable and reusable) that presents additional challenges. The open-source Tripal database toolkit seeks to mitigate these challenges by creating both the software and an interactive community of developers for construction of online community databases.
Additionally, through coordinated, distributed codevelopment, Tripal sites encourage community-wide sustainability. Here, we report the release of Tripal version 3 that improves data accessibility and data sharing through systematic use of controlled vocabularies (CVs). Tripal uses the community-developed Chado database as a default data store, but now provides tools to support other data stores, while ensuring that CVs remain the central organizational structure for the data. A new site developer can use Tripal to develop a basic site with little to no programming, with the ability to integrate other data types using extension modules and the Tripal application programming interface. A thorough online User's Guide and Developer's Handbook are available at http://tripal. info, providing download, installation and stepby-step setup instructions.
Introduction Online repositories for biological data serve as a valuable resource for researchers as they provide a home for public data and offer services such as tools for data analysis, web services, or content for scientific community engagement. In 1988, the National Center for Biotechnology Information (NCBI) was created with the goal to 'design, develop, implement and manage automated systems for the collection, storage, retrieval, analysis and dissemination of knowledge concerning human molecular biology, biochemistry and genetics'  (1). NCBI has served as a global repository for genomic sequence data, gene transcriptome data, genetic variance data and more for both human and non-human species for three decades. Online community databases play a similar role but unlike large repositories such as NCBI, they serve a specific group of researchers focused on one species or a group of related species.
In addition to housing data, these sites provide analyses, tools and outreach specific to their respective communities. In 1992, the FlyBase project developed one of the first community databases for genomic and genetic resources for Drosophila melanogaster  (2, 3). Dendrome (now TreeGenes) initially begun in 1993 to house forest tree genetic maps and markers (4); the Rat Genome Database (RGD)  (5, 6)  in 1999 for rat research; The Arabidopsis Information Portal (TAIR), also in 1999, for genomic and genetic data for Arabidopsis thaliana, a model plant species  (7). Later examples include the Saccharomyces Genome Database (SGD) in 2002 for the model species Saccharomyces cerevisiae  (8) ; the Genome Database for Rosaceae (GDR) in 2003 for Rosaceae species including agriculturally important tree fruit and berry species  (9) ; Knowpulse began in 2010 for pulse crop breeding  (10) ; and many others.