dataesr/scientific-paragraphs-categorization huggingface dataset:
IV. VALIDATION OF RESULTS: CONTROLLED EXPERIMENTS In this section we validate our framework studying its performance on data surrogates, i. e. statistical ensembles of randomized data. In other words, we apply our approach to a set of data that by construction do not contain the temporal correlations we find in real datasets. This step is crucial to prove that our observations capture genuine features of real collective events. In the following, for simplicity, we considered the 15M protests datasets.
A. Statistical randomized surrogates of original data In order to validate our results, we need to make sure that our analysis and conclusions are mere artifacts which would arise in any case. To provide a reasonable baseline, we need to build randomized counterparts of the data and then analyze it just as we did for the actual case. In this line, we consider two methods to obtain data random surrogates: amplitude adjusted Fourier transform surrogates and constrained randomization surrogates, with an extensive use of the TISEAN software  [19]. We present the results for the randomization of the Spanish data, with qualitatively similar insights for the other datasets.
Amplitude Adjusted Fourier Transform (AAFT) surrogates A first, robust step to provide a suitable null model is to generate randomized datasets which ensure that certain features of the original data will be preserved. In particular, we generate AAFT surrogates as proposed in  [21] , who established an algorithm to provide surrogate datasets containing random numbers with a given sample power spectrum and a given distribution of values. Under these constraints, we obtained 50 randomized versions of the 15M data, which were then analyzed in the same way as the original data (see main text and previous sections). The averaged results from such analysis are offered in Figure ??. Clearly, the original patterns are completely blurred and just a single characteristic time scale can be observed. Furthermore, our approach do not capture any change in the the characteristic time scale as correlations and driving between different units have been artificially eliminated in the data.
Compton and T-C Lu at HRL Laboratories LLC for assistance with the Brazilian dataset and useful discussions; we also thank A. Lima for The dynamic of information-driven coordination phenomena: a transfer entropy analysis List of keywords used to find tweets related to the 'Outono Brasileiro' rates (up to May 15, day 20 in the Figures) plus an "explosive" phase beyond that date, in which the phenomenon reached general public and was widely covered by traditional mass media, see Figure 1 in the Main Text. Scraps on Twitter servers yielded 581, 749 messages.
Acknowledgments AA acknowledges the support of the  European Union   MULTIPLEX 317532 , the  Spanish Ministry of Science and Innovation   FIS2012-38266-C02-01 , and partial financial support from the  ICREA Academia  and the  James S. McDonnell Foundation. YM acknowledges support from  MINECO  through Grant  FIS2011-25167 ;  Comunidad de Aragn (Spain)  through a grant to the group FENOL, and by the  EC FET-Proactive Project MULTIPLEX  (grant  317532 ). For the analysis of data outside of the  United States of America AV  and NP acknowledge the  Intelligence Ad-.
Objective: Advanced therapy medicinal products (ATMPs) constitute a class of innovative products that encompasses gene therapy, somatic cell therapy, and tissue-engineered products (TEP). There is an increased investment of commercial and non-commercial sponsors in this field and a growing number of ATMPs randomized clinical trials (RCT) and patients enrolled in such trials. RCT generate data to prove the efficacy of a new therapy, but the discontinuation of RCTs wastes scarce resources. Our objective is to identify the number and characteristics of discontinued ATMPs trials in order to evaluate the rate of discontinuation. Methods: We searched for ATMPs trials conducted between 1999 to June 2015 using three databases, which are Clinicaltrials. gov, the International Clinical Trials Registry Platform (ICTRP), and the EU Drug Regulating Authorities Clinical Trials (EudraCT).
Materials and methods Data were collected by two independent researchers who retrieved withdrawn, terminated, or prematurely ended ATMPs clinical trials, referred to as 'discontinued trials' in this article, conducted during the time period from 1999 to June 2015 and ongoing and completed trials conducted from 1999 to June 2013 with a recent last update date (last update date after May 2014). Three clinical trials databases were searched: Clinicaltrials. gov, the International Clinical Trials Registry Platform (ICTRP) of the World Health Organization (WHO), and the European Clinical Trials Database (EudraCT). The same combinations of keywords was used for the three databases searches. (For more details on materials and methods, see the relevant article (3)). Data extraction forms were designed using Microsoft Excel 2010 to extract the registration number, date of registration, title, status, phase, study design, target enrolment number, sponsor, disease, and last update date.
Duplications were removed, as well as therapies not classified as ATMPs. We classified the remaining trials by ATMPs type, based on the definition of ATMP provided by the European regulation EC N8 1394/2007  (2). The data were sorted out by:. Type of ATMPs: sCTMPs, GTMPs, TEPs, and combined ATMPs. Sponsor status: commercial, non-commercial. For non-commercial sponsors, the corresponding clinical trials were classified into five settings: hospital, university, institute, medical center, and government. Development phase: phase 1, 1/2, 2, 2/3, and 3. Pathology: Cancer, cardiovascular, musculoskeletal, immune system/inflammation, neurology, gastrointestinal/diabetes, ophthalmology, pulmonology, dermatology (wounds, ulcers), and others. We calculated the rate of discontinuation of ATMPs clinical trials using: Probability of discontinuation of ATMP trial ¼ Number of discontinued trials ðNumber of discontinued trials þ Number of.
The discontinuation rates for commercial and noncommercial sponsors are the same, suggesting that the reason of discontinuation may not be financially driven. One may have anticipated that commercial organizations are more cautious and attract fewer risk takers, thus resulting in a lower risk of discontinuation rates compared to the rates of non-commercial organizations. This possibility is not supported by our findings. This study carries some limitations. First, the reason for discontinuation is not available in the searched databases, and therefore some discontinuations may not be related to scientific/technical considerations. Second, another way to do this analysis would have been to identify all studies registered at a given time interval and follow them up to discontinuation or completion.
Methodology A preliminary comprehensive search was conducted to assess how the concept of drug repurposing was described in the literature. The search was conducted in English language, limited to publications released between 2000 and December 2015 (cut-off date of the search) in the following databases: Medline; Embase; Cochrane; Generics and Biosimilars initiative (GaBi) website. The search strategy included preliminary free search terms ('marginal innovation', 'incremental innovation', 'adaptive innovation', 're-innovation', 'hybrid products', 'drug repurposing', 'drug reformulation', 'drug repositioning'), and was complemented with additional free search terms found in the literature. Following completion of the literature review, a primary research related to drug repurposing was conducted across EU MS to define from key stakeholders' perspectives (patients, healthcare professionals, regulatory authorities, and Health Technology Assessment (HTA) bodies/payers): (1) how drug repurposing was currently perceived; (2) how drug repurposing might respond to their needs; and (3) which actions might be undertaken to enhance value recognition and access of drug repurposing across Europe.
As the difference in the yearly-average (1) Data on R&D expenditure is not available prior to 1996. Also for Brazil data is not available between 1996 and 2000 both years inclusive. expenditure is very sharp for India and rest of the three countries, we try to change the scale of analysis from R&D expenditure per capita to R&D expenditure per student enrolled in tertiary education. 2 Considering the gross enrolment ratio in tertiary education (IS-CED 5 and 6 3 ) for the year 2011, the population between age group 15 to 64 years and R&D investment in 2011 4 for India, we estimated the number of people potentially enrolled in the tertiary education. We find that India has spent approximately US$ 81, which is still way less than one-fourth of the R&D investment per capita by Singapore. We admit that the estimate of the R&D investment per unit student in tertiary education may be less than actual figures because the calculation relies on the percentage of population between 15 to 64 years age group and not on the percentage of the total population of the five-year age group following on from secondary school leaving as mentioned in World Bank data source; however the main point of this estimate is primarily to provide an interpretation that even if the total R&D investment per capita is adjusted for the actual number of people with potential to enter into research studies 5 , India might be able to barely match the actual investment made by Singapore per unit of its total population.
Women Scientists Programs Women between age group of 30-50 years are provided three types of scholarship -for research in basic/ applied science, research in S&T and self-employment. This scheme gives preference to women with a break in career en Scientists Program are as follows. o For research in basic/applied science -An allowance of about US$ 12000 is provided annually per person, which includes fellowship of the applicant and cost of small equipment, contingencies, travel, consumables etc. o Research in S&T -this provides support to women in two modes -internship and project mode. A special one-year internship program has been launched during 12 th year plan. The internship aims at developing a proposal for financial support, which is provided under project mode. About US$ 4500 per year per person is provided under internship mode and about US$ 5500 per year per person is provided under project mode.
Entrepreneurial capabilities of India vis. à vis. China, and Singapore 7 To study the entrepreneurial capabilities of India we analyse the data on total early-stage entrepreneurial activity (TEA) by stages of economic development (percent of population aged 18-64) in 2013 and 2014 (Table 3). These stages involve nascent entrepreneurship rate, new business ownership rate, early-stage entrepreneurial activity, established business ownership rate and discontinuation of businesses rate and are provided by the Global Entrepreneurship Monitor (GEM). We find that India's stand is the lowest among all four countries with only a 15. 82 percent of population aged 18-64 in TEA whereas Singapore has 22. 63 percent, China has 41. 3 and leading in the group is Brazil with a total score of 48. 1. Stages wise as well India has the minimum percent in all the stages except for nascent entrepreneurship rate where it has second lowest percentage of adult population.
comparison to Singapore. At the same time, it is observed that the entrepreneurial activities in India are on the decline, a situation that is likely to slow down India's catch up process. It indicates a requirement of government intervention to boost entrepreneurial activities. We bring forth to the attention of the policy makers key tools for implementation of already existing STIP, based on the experiences from across the world. These tools are: • Making the government as a venture capitalist to support entrepreneurship, • Making a robust database for all and major aspects of science and innovations to facilitate policy designs and • Incorporating entrepreneurial as a part of the science curriculum at a faster pace to integrate science and innovation with profitability. These three factors together constitute the "E for entrepreneurship" and are likely to prove to be an accelerator to the science, technological and innovation led growth and development.
contains 681 independent in vivo studies on 634 individual chemicals representing a wide range of chemical classes. A description of all the ocular effects observed in vivo, i. e. degree of severity and persistence of corneal opacity (CO), iritis, and/or conjunctiva effects, was added for each individual study in the database, and the studies were categorised according to their UN GHS/EU CLP classification and the main effect driving the classification. An evaluation of the various in vivo drivers of classification compiled in the database was performed to establish which of these are most important from a regulatory point of view. These analyses established that the most important drivers for Cat 1 Classification are (1) CO mean ≥ 3 (days 1-3) (severity) and (2) CO persistence on day 21 in the absence of severity, and those for Cat 2 classification are (3) CO mean ≥ 1 and (4) conjunctival redness mean ≥ 2.
Moreover, it is shown that all classifiable effects (including Abstract A thorough understanding of which of the effects assessed in the in vivo Draize eye test are responsible for driving UN GHS/EU CLP classification is critical for an adequate selection of chemicals to be used in the development and/or evaluation of alternative methods/ strategies and for properly assessing their predictive capacity and limitations. For this reason, Cosmetics Europe has compiled a database of Draize data (Draize eye test Reference Database, DRD) from external lists that were created to support past validation activities. This database.
In contrast, more recent retrospective validation activities  (ICCVAM 2007 (ICCVAM , 2010;; EURL ECVAM 2008a, b)  have already used the UN GHS/EU CLP classification for serious eye damage/eye irritation  (UN 2013; EC 2008). In addition, the Eye Irritation Reference Chemicals data bank published by the European Centre for Toxicology and Ecotoxicology of Chemicals  (Bagley et al. 1992; ECETOC 1992 ) was many times the main, if not the only, database used for selecting test chemicals for validation studies. At that time, this database contained only 55 chemicals (72 in vivo studies in total), and it was therefore not a comprehensive representation of chemical classes. Since this version of the ECETOC database was a fundamental source of in vivo data used in validation studies conducted in the 1990s, e.
g. EC/HO  (Balls et al. 1995)  and COLIPA, now called Cosmetics Europe  (Brantom et al. 1997) , the scope of chemicals selection was, to a certain extent, limited. Subsequently, a second more comprehensive version of the ECETOC data bank extended to 132 chemicals (149 in vivo studies in total) was published in 1998  (Bagley et al. 1999; ECETOC 1998). Furthermore, variability of the responses observed between rabbits from historical data normally used as the in vivo reference in validation studies  (Scott et al. 2010 ) was identified as potentially influencing the outcome of validation activities and acceptance of in vitro test methods. The impact of the uncertainty of in vivo reference data on the evaluation/validation of alternative methods was already illustrated by the resampling analysis presented by  Adriaens et al.
These data were sourced from several external databases that had been compiled and were used to support past validation activities for in vitro test methods. The comprehensive database developed by Cosmetics Europe systematically covers all drivers of classification based on the observed tissue effects, relevant chemical classes and physical states. This means that it can be used to appropriately select chemicals for the development and evaluation of in vitro test methods. This approach will facilitate an early and thorough assessment of the performance of a new in vitro test method and will help better identify its limitations and applicability within testing strategies such as those suggested by  Scott et al. (2010). Another consequence of an appropriate selection of chemicals is that future validation studies/activities may be performed on a smaller and more focused dataset of chemicals covering all important drivers of classification.
Data sources The Draize eye test Reference Database (DRD) provided in Supplementary Material 1 was compiled using different sources of historical in vivo Draize eye test data which were created to support past validation activities. These data were produced according to OECD TG 405 (OECD 2012a) using proprietary and commercially available chemicals. The data sources used were (1) the Eye Irritation Reference Chemicals Data Bank developed by ECETOC  (Bagley et al. 1992 (Bagley et al. , 1999;; ECETOC 1998)  ; (2) a database developed by ZEBET  (Spielmann et al. 1996) ; (3) the database from Laboratoire National de la Santé (LNS)  (Gautheron et al. 1994 ); (4) the database developed by the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) to support the retrospective evaluations of the Bovine Corneal Opacity and Permeability (BCOP) test method, the Isolated Chicken Eye (ICE) test method, the Isolated Rabbit eye (IRE) test method, and the Hen's Egg Test-Chorioallantoic Membrane (HET-CAM) that were performed by the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM)  (ICCVAM 2007 (ICCVAM , 2010)) ; (5) the database developed by EURL ECVAM to support the prospective validation study of RhCE-based test methods performed by  EURL ECVAM and Cosmetics Europe; and (6)  five studies that were not included in the other databases but that were used in the Cosmetics Europe study on the use of HPLC/UPLC spectrophotometry in Reconstructed human Tissue (RhT)-based test methods  (Alépée et al.
Draize eye test Reference Database (DRD) The DRD (Supplementary Material 1) contains 681 independent Draize eye test studies, which are identified in the first three columns by a "Study Number" (ranging from 1 to 681), "Test Chemical Name" and the Chemical Abstracts Service Registry Number ("CAS RN") of the test chemical. Chemical classes were assigned to most of the chemicals listed in the DRD according to OECD QSAR Toolbox analysis (version 3. 2; http://www. oecd. org/chemicalsafety/ risk-assessment/theoecdqsartoolbox. htm). A profiler comprising around 430 nested categories was implemented which assigns effective organic class according to full functional group, without subdivision of component fragments. For example, RCOOH would be classed as carboxylic acid only, without allocation of ketone and alcohol classes, respectively, to subsidiary CO and OH constituents.
Finally, within each category of "physical form as tested" the studies were sorted in alphabetic order of the test chemical name. The column "Data Source" refers to the source of the data (i. e. ECETOC, ZEBET, LNS, NICEATM, EURL ECVAM and Cosmetics Europe). The commercial source (availability today; provided as an example) and the available purity are also provided. The CAS RN, name and physical form of the raw chemical were verified one by one by three independent investigators using Material Safety Data Sheets (MSDS), where available, or any other reliable source of information. This resulted in the identification of some transposition errors in the original in vivo data source that were corrected in the current database (e. g. study No. 532 was reported with CAS RN 118-82-3 in ECETOC, while the correct CAS RN for this chemical, as mentioned in the DRD, is 118-82-1).
Distribution of studies according to UN GHS classification The DRD contains data on 681 Draize eye test studies representing 634 unique chemicals and chemical solutions/suspensions in a solvent. For 94 of these 681 studies (13. 8 %) UN GHS study criteria allowing an unambiguous classification were not met. These are identified as SCNM in the DRD. Among the 587 studies which met the UN GHS criteria for classification (representing 547 unique chemicals and chemical solutions/suspensions in a solvent), 41. 6 % were classified, with 13. 5 % being Cat 2 and 28. 1 % being Cat 1 (Table  3 ). This distribution is very similar to that reported by  Adriaens et al. (2014)  for three reference databases that are included in the DRD (ECE-TOC + ZEBET + LNS: 17. 2 % Cat 2 and 22. 6 % Cat 1), but differs substantially from the prevalence of Cat 2 (10.
4 %) and Cat 1 (6. 9 %) chemicals in the European New Chemicals Database (NCD) of the ex-European Chemicals Bureau containing data on "New Chemicals" notified under Directive 67/548/EEC and introduced to the EU industrial market after September 1981  (Adriaens et al. 2014). Although the DRD contains several studies on NCD chemicals for which full raw data are available, most of the studies included in the DRD have been collected over the years specifically to support validation activities, where the goal was probably to increase the proportion of Cat 2 and Cat 1 chemicals rather than be reflective of what is observed in reality. The distribution of UN GHS categories observed in the NCD is therefore expected to represent more closely the true prevalence of Cat 2 and Cat 1 chemicals.
However, based on the mean scores of days 1-3, these Cat 1 studies cannot be distinguished from Cat 2 studies classified based on CO mean ≥ 1 and/or Conj mean ≥ 2. It is likely that methods that predict immediate severe effects but do not necessarily provide direct information on the persistence or reversibility of effects [for example BCOP and ICE with their current protocols (OECD 2013a, b, respectively)], will not be able to discriminate between these Cat 1 and Cat 2 chemicals. OECD TGs 437 and 438 (OECD 2013a, b) state that both BCOP and ICE generate a high number of false negatives for solids when used to identify chemicals inducing serious eye damage (UN GHS Cat 1). Looking at the validation databases of these two methods  (ICCVAM 2007 (ICCVAM , 2010)) , it can indeed be confirmed that 25 % (4/16) and 55 % (6/11) of the Cat 1 solids that were tested with BCOP and ICE, respectively, were underpredicted.
Variability between repeat Draize eye studies Since several chemicals in the DRD were tested more than once in independent studies performed by different laboratories, the reproducibility of the Draize eye test could be assessed for these chemicals. The reproducibility of the repeat studies was evaluated in terms of agreement of classifications and/or classification drivers and is summarised in Table  4  for chemicals classified as Cat 1 in at least one study and for chemicals for which the maximum classification obtained in the repeat studies was Cat 2. Table  5  summarises the data for chemicals not requiring classification in all repeat studies. Of note, studies that resulted in a Cat 1 classification based on severity in the first three observation days or CO = 4 were sometimes terminated before day 21.
1 % (9/23) of the chemicals. The statistical resampling analysis performed by  Adriaens et al. (2014) , demonstrated an overall probability of at least 11 % that chemicals classified as Cat 1 by the Draize eye test could be equally identified as Cat 2 and of about 12 % for Cat 2 chemicals to be equally identified as No Cat. These proportions of misclassifications reflect the within-test variability of the Draize eye test only. Although there are only a limited number of repeat studies available in the DRD, there is evidence that the reproducibility of the Draize eye test reduces substantially when its between-laboratory reproducibility is taken 1 3 into account. Thus, in the current data set, 37. 5 % (6/16) of the chemicals with at least one Cat 1 study could be equally identified as Cat 2 and 28.