dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical response. The clinical response evaluation in the overall population was performed in 119 patients. These data were not available for 7 patients. Clinical control of symptom rates was 95% (95% CI: 89-98%) after a median time of 4. 4 months (1. 1-21. 3 months). We noted complete clinical remission symptoms in 37% of patients and improvement in symptoms in 14%. The symptomatology remained stable in 44% of cases. A worsening of the neurological symptoms was observed in 5% of the cases.
Dose protocols. Several hypofractionated schedules of dose and fraction were used according to the practices of the radiotherapy departments. The median dose was 25 Gy (range 12-40 Gy) with median dose per fraction of 5 Gy (range 4-8 Gy). A total dose was prescribed to the 80% isodose line, which covers 95% of PTV. The dose constraints for the organs at risk (OARs) are listed in Table  3. Treatment was delivered every other day. Follow-up evaluation. After treatment, the patient follow-up visits, including physical examination and cranial MRI, were held every three months for the first year, every six months for the next year, and annually thereafter. Endpoints and statistical analysis. Statistical analyses were performed with Stata 13. 1 (StataCorp, 2013; Stata Statistical Software, release 13; StataCorp LP, College Station, TX).
These acoustic waves are then monitored in real time by detecting the green light from a continuous-wave laser (wavelength = 532 nm) diffracted on the surface relief associated with the acoustic disturbances. The detection of elastic waves is made using a heterodyne scheme and fast detection electronics. Controlling the angle between the two incident pump beams via adjusting of the focal path allows to tune the wavelength Λ of acoustic wave. The Fourier analysis of experimental data at fixed value of Λ shows distinct frequencies, each corresponding to distinct mode of the structure, as depicted in Figure  4a. Varing Λ and recording the frequencies resonance peaks for each value, allows therefore to map the dispersion diagrams with the step size in wave vector determined by the pattern of diffractive optical element and the magnifying power of optical system.
Therefore, a maximum of 352 compound/concentration pairs can be evaluated with two 384-well cell plates. The following protocol considers such an experiment performed with 352 test compounds, two cell plates, and 12 million cells supplied as three frozen aliquots; it can be easily scaled-up if additional data points are needed. 1. Day 0 of the experiment NOTE: For planning purposes, the test compound application can be performed on any day between days 22-28 of the experiment. 1. Turn on the water bath at 37 °C and warm 40 mL of plating medium (as supplied by the manufacturer). NOTE: Use the cells with their supplier-provided matching culture and maintenance media. 2. Thaw the hiPSC-CM by placing the cryotubes containing frozen cells in the water bath for 2 min. 3. Transfer the 1 mL cell suspensions carefully to a 50 mL conical tube.
Data Analysis NOTE: The data analysis can be performed any time after the measurement. 1. Export the raw data for each recording period from the plate reader software as ASCII text files. 2. Import the raw data into the data analysis software. 3. For each well and all time points, extract the primary beating frequency. NOTE: With the data analysis software listed in the Table of Materials, beating frequencies can be calculated with a custom analysis routine using the LombPeriodogram function, based on the Lomb-Scargle method of least-squares spectral analysis 7. The periodogram should be calculated for a range of frequencies between 0. 01 and 5 Hz. The primary frequency can be extracted from the periodogram using the PeakAutoFind routines. This analysis method provides more accurate measurements of beating frequencies than the method supplied as an option with the plate reader software.
Aims. -To explore comparative glycaemic control and hypoglycaemia incidence with insulin degludec 100 U/mL (IDeg) or insulin glargine 300 U/mL (Gla-300) versus glargine 100 U/mL (Gla-100) in trial-level meta-analyses of phase 3a clinical trials including people with type-2 diabetes. Methods. -Meta-analyses of HbA 1c , fasting plasma glucose (FPG), average 24 h self-measured plasma glucose (SMPG), pre-breakfast SMPG and hypoglycaemia incidence and rate, using data from the BEGIN (IDeg) and EDITION (Gla-300) insulin development programmes, were performed. Results. -In BEGIN, despite greater FPG reduction with IDeg than Gla-100, HbA 1c reduction was greater with Gla-100 (mean difference [95% CI] in HbA 1c change: 0. 09 [0. 01-0. 18] %) whereas in EDITION, there was no difference in FPG and HbA 1c reduction between Gla-300 and Gla-100.
Thus, trial-level meta-analyses can, despite some inherent limitations, enable a better understanding of results across the multiple individual trials for which data are already available, facilitating the interpretation of clinical importance. Although there are a number of published meta-analyses comparing IDeg with Gla-100, unlike the current trial-level meta-analysis, curiously these previous analyses did not include HbA 1c  [16, 20] , fasting selfmeasured plasma glucose (SMPG) or hypoglycaemia incidence comparisons  [16, 20, 21]. The aim of the trial-level meta-analyses presented here was to examine a range of glycaemic outcomes including HbA 1c , average 24 h and pre-breakfast SMPG, fasting plasma glucose (FPG) as well as incidences and rates of different categories of hypoglycaemia, with IDeg or Gla-300 versus Gla-100 in two trial-level metaanalyses of people with T2DM from the phase 3a BEGIN and EDITION insulin development programmes.
Eligibility criteria, study selection and information sources Only data from phase 3a trials, with HbA 1c reduction as a primary endpoint, were included, focusing analyses on the BEGIN and EDITION programmes. For both the BEGIN and EDITION programmes, trials were selected if they included people with T2DM who received the study insulins on a once-daily regimen and compared IDeg 100 U/mL (BEGIN) or Gla-300 (EDITION) with Gla-100. In the BEGIN Flex trial  [10] , participants in the ''OD Flex'' arm received IDeg at intervals of 8-40 h; therefore, these data were not included. The SWITCH 2 study  [22]  was not included in the IDeg metaanalysis as the study design of SWITCH 2 included crossover periods with the potential for carry-over effects. Also, this type of study design makes it difficult to pool data from the individual crossover periods if the baseline values are different, as they were for HbA 1c and FPG in SWITCH 2  [23].
Outcomes data extracted The following outcome data were extracted from the BEGIN and EDITION trial sources: HbA 1c (primary endpoint in all trials), FPG, average 24 h SMPG based on 9-point (BEGIN) or 8-point (EDITION) SMPG profiles, pre-breakfast SMPG; percentage of participants with ! 1 confirmed (BEGIN, < 3. 1 mmmol/L [< 56 mg/dL]; EDITION, < 3. 0 mmol/L [< 54 mg/dL]) or severe hypoglycaemic event, or documented symptomatic ( 3. 9 mmol/L [ 70 mg/dL]) hypoglycaemic event, during the night (BEGIN, 00:01-05:59 h; EDITION, 00:00-05:59 h) and at any time of day (24 h) or with ! 1 severe hypoglycaemic event at any time of day; annualized rates of hypoglycaemia (events per participantyear). In the EDITION trials, confirmed or severe hypoglycaemia was primarily reported with a glycaemic threshold of 3.
BEGIN once Asia  [11]  EDITION 1  [12]  EDITION 2  [13]  EDITION 3  [14]  EDITION JP 2  [15]  NCT number From the BEGIN Flex trial only the ''OD'' arm was included in this trial-level meta-analysis; participants in the ''OD Flex'' arm received IDeg at intervals of 8-40 h  [10] ; therefore, these data were not included. BMI: body mass index; Gla: insulin glargine; IDeg: insulin degludec; MET: metformin; N/A: not applicable; OAD: oral anti-hyperglycaemic drug; OD: once daily; SMPG: self-measured plasma glucose.
There was no direct head-to-head comparison of IDeg versus Gla-300. Additionally, although the trial-level methodology allowed the analyses to be performed without access to patient-level data, the summary nature of such an analytical technique may be a limitation as the source data are restricted by previous analyses. There were also several differences between the trial designs of EDITION and BEGIN that may have affected the analysis. For example, the study populations of three of the five BEGIN trials included insulin-naı ¨ve individuals compared with only one of the four EDITION trials. All the main EDITION trials were 26 weeks in length, whereas two of the five BEGIN trials were 52 weeks. There was a notable difference in the fasting SMPG targets in EDITION (4. 4-5. 6 mmol/L [80-100 mg/dL]) compared with BEGIN (generally 3.
Despite these limitations, these trial-level meta-analyses in T2DM may support informed clinical evaluations. Head-to-head trials of IDeg versus Gla-300 are warranted to allow direct comparisons between these insulins. A phase 4 trial of Gla-300 versus IDeg (NCT02738151) has recently been completed and the results of this trial are awaited with interest  [26]. In summary, both improved longer-acting basal insulin formulations IDeg and Gla-300 show more similarities than differences. However, when comparing IDeg versus Gla-100 in the BEGIN trials the hypoglycaemia benefit of IDeg was only seen for nocturnal events, not those at any time (24 h), and this was achieved in the context of slightly but significantly less improvement in HbA 1c. For Gla-300 versus Gla-100 in the EDITION trials there was comparable HbA 1c improvement alongside a hypoglycaemia benefit favouring Gla-300 both at night and at any time (24 h).
This uncertainty is about 7% for p miss T = 150 GeV and decreases to 0. 7% for p miss T > 300 GeV. Several data control samples are employed to validate the analysis techniques and to estimate systematic uncertainties in the background estimates. The control sample for the principal background from tt events requires exactly one electron or one muon, while the Z → νν background is studied with a control sample requiring two leptons consistent with a Z → + - decay. These data samples were obtained with triggers that require at least one electron or muon with p T greater than 27 or 24 GeV, respectively. Signal events have four b jets from the decay of two Higgs bosons and no isolated leptons, with any additional hadronic activity coming from initial-or final-state radiation. Thus, we select events with four or five jets, no leptons or isolated tracks, N b,T ≥ 2, p miss T > 150 GeV, high ∆φ, ∆m < 40 GeV, and ∆R max < 2.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other Council and  Horizon 2020  Grant, contract No. 675440  ( European Union ); the  Leventis Foundation ; the A. P. Sloan Foundation; the  Alexander von Humboldt Foundation ; the  Belgian Federal Science Policy Office ; the  Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; the  Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium) ; the  Ministry of Education, Youth and Sports (MEYS)  of the Czech Republic; the  Council of Science and Industrial Research, India ; the  HOMING PLUS program  of the  Foundation for Polish Science , cofinanced from  European Union, Regional Development Fund , the  Mobility Plus program of the Ministry of Science and Higher Education , the  National Science Center (Poland) , contracts Harmonia  2014/14/M/ST2/00428 ,  Opus 2014/13/B/ST2/02543 ,  2014/15/B/ST2/03998 , and  2015/19/B/ST2/02861 , Sonata-bis  2012/07/E/ST2/01406 ; the  National Priorities Research Program  by  Qatar National Research Fund ; the  Programa Clarín-COFUND del Principado de Asturias ; the Thalis and Aristeia programs cofinanced by  EU-ESF  and the  Greek NSRF ; the  Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University  and the  Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; the  Welch Foundation , contract  C-1845 ; and the  Weston Havens Foundation (USA).
The present work focuses on the International Neuroimaging Data-sharing Initiative (INDI)  12  , as it uniquely embodies both of these models of sharing. The bibliometric measures we have employed could be easily applied to other initiatives in future work. Another distinctive aspect of INDI is its reliance on the formation of grassroots consortia as a primary vehicle for achieving its goal of aggregating and sharing previously collected data. Self-initiated and organized by scientists in the community, these consortia aggregate and share independently collected data from sites around the world. Examples of INDI-based consortia include the 1000 Functional Connectomes Project (FCP; n = 1414, released in December 2009)  13  , the ADHD-200 (n = 874, released in March 2010)  14  , the Autism Brain Imaging Data Exchange (ABIDE; n = 1112, released in August 2012)  15  , and the Consortium for Reliability and Reproducibility (CoRR; n = 1629, released in June 2014)  16.
In the present work, we use the grassroots consortium component of the INDI model to examine the relative benefits of open data sharing versus "pay to play" models, in which only those who give data can benefit from sharing. To examine the benefits of data resources explicitly generated for the purposes of sharing, we use INDI's Nathan Kline Institute-Rockland Sample (NKI-RS) initiatives, a combination of large-scale cross-sectional and longitudinal multimodal imaging samples of brain development, maturation, and aging (ages 6. 0-85. 0)  17, 18 initial release in October 2010; quarterly releases ongoing, current n = 1000+). INDI efforts have been lauded by funding agencies, journal editors, and members of the imaging community. However, such subjective recognition does not quantify research impact.
Results Data use. A keyword-based search identified 1541 possible INDIrelated publications as of March 22, 2017 of which 913 were determined to have used data from INDI. Figure  1a  provides a non-cumulative breakdown of the 913 publications by year and initiative, revealing steady yearly increases in shared data use. Author affiliations for the 913 publications using INDI data spanned 50 countries across 6 continents, with peak affiliation densities in the United States (48. 5%), China (10. 7%), Germany (6. 5%), and the United Kingdom (6. 0%) (see http://fcon_1000. projects. nitrc. org/indi/bibliometrics/map/map. html for the world map of author affiliations generated using the Google Maps JavaScript API V3). The overwhelming majority of publications were either peer-reviewed journal articles (n = 739; 81%) or preprints (n = 65; 7%); scholarly theses had a substantial presence as well (n = 58 [33 doctoral; 19 master's; 4 bachelor's; 2 unspecified]), demonstrating the value of shared data for trainees and early career investigators (see Fig.
1b ). As expected given the brain imaging focus of the INDI consortia, the largest proportion (45. 7%) of publications were in journals focused on general neuroscience, neurology, psychiatry, and psychology. However, INDI data sets were also used in other domains (e. g. , mathematics, computer science, physics, and engineering journals accounted for 6. 6% of publications) (see Fig. 1c ). Publication impact. The impact of each of the major INDI efforts (FCP, NKI-RS, ADHD-200, ABIDE, and CoRR) on the scientific literature was quantified using an array of commonly used citation-based indices, including the h-(the number of publications with at least the same number of citations) and i10-(the number of publications with at least 10 citations) indices (see Table  1 ). As of March 22, 2017, the 913 publications that explicitly used INDI data had been cited 20,697 times by publications referenced in Google Scholar, with an average of 4.
Nonetheless, for the purpose of citation analysis, they can be effective tools for summarizing literature-level trends. Several articles that used INDI data have been published in high-impact specialized journals (e. g. , Biological Psychiatry and Neuron) and generalinterest journals (e. g. , Proceedings of the National Academy of Sciences and Nature Communications) (see Fig. 2a  for the 15 highest-impact journals in which publications using INDI data sets have appeared based on CiteScore  20  ). As shown in Fig. 2b , of all publications measured by 2015 CiteScore values, 50% were published in a journal with a CiteScore of 4. 05 or higher, 25% with a CiteScore of 6. 71 or higher, 5% with a score of 8. 84 or higher, and 1% with a score of 12. 02 or higher. Two of the three journals with the highest number of publications were Neuro-Image and Human Brain Mapping, which are among the highestranked field-specific brain imaging journals.
Two questions that may arise regarding our findings concerning the ability to publish in higher-impact journals using openly shared data are: (1) how would our results compare to those obtained using the same analysis applied to closed (not shared) data, and (2) would our results generalize to data-sharing initiatives other than INDI? To provide insights regarding the first question, we used PubMed to identify MRI studies of autism using the following set of keywords: (autism OR autistic OR ASD) AND (rest fMRI OR resting-state fMRI OR rest state fMRI OR Intrinsic Brain Function OR MRI OR sMRI OR functional MRI OR resting-state functional), and then programmatically vetted them-yielding 80 ABIDE and 1834 non-ABIDE studies. Of note, there were 50 ABIDE papers identified by Google Scholar that were not identified by this PubMed search.
In large part, this can be explained by two factors. First, PubMed limits searches to title, abstract, and keywords; Google Scholar searches the entire document; some articles use more obscure terminology or incomplete terminology in their title and abstract. Second, a subset of papers used ABIDE data to study questions outside the autism field, such as typical brain development (using only the neurotypical data sets) or methods development. Post hoc examination found the cumulative density curves for excluded and included papers to be highly similar. Next, we generated a cumulative density plot for studies using ABIDE data and those using closed (not shared) data. The curves we obtained were nearly identical, suggesting that papers using open data and those using closed data fared equally well with respect to the impact of the journals in which they were accepted.
This result extended to the ADHD literature when the same analyses were repeated with corresponding search terms (i. e. , "ADHD," "hyperactivity," "inattention" in place of "autism" and "autistic"), which revealed 38 ADHD-200 and 1986 non-ADHD-200 studies. The results for the ABIDE and ADHD-200 data sets are depicted in Fig. 2c, d,  respectively. To answer the question of whether our findings would generalize to other data-sharing initiatives, we searched for publications using HCP data. Given that this is a supplementary analysis, we used an automated search strategy that limited our search to publications in PubMed that included "Human Connectome Project" as a searchable term; as such, this analysis was meant to provide a sampling of the papers, but not to be exhaustive. Manual vetting for these results (n = 232) yielded 175 publications using HCP data, from which we then generated a cumulative density plot, which we found to be very similar to that obtained for INDI, suggesting that our findings generalize to other efforts.
Beneficiaries of sharing. A common alternative to open data sharing is the "pay to play" model, where one must contribute data in order to gain access to data shared by others; from a consortium perspective, this means that data access is limited to members only. While such models can incentivize data sharing, they miss out on valuable analyses that researchers lacking data to contribute would perform if given the opportunity. INDI's consortium model provides a unique opportunity to compare use of shared data by contributing and non-contributing researchers. Specifically, for each initiative (FCP, ADHD-200, ABIDE, CoRR, and NKI-RS), "contributing authors" were defined as any coauthor of the announcement publication for the respective initiative. Using this definition, 90. 3% of INDI-based publications were authored by research teams that did not include any data contributors.
As shown in Fig. 3 , the number of publications by noncontributors is rapidly increasing year to year. This differential between publications authored by contributors versus noncontributors reflects the potential missed opportunity associated with the "pay to play" model of data sharing. The publication patterns of INDI consortia members can also be used to glean insights into the benefits of contributing data beyond inclusion as a coauthor on data announcement or descriptor papers. To accomplish this, we focused on the ADHD-200 and ABIDE consortia, as they consist of data from clinical populations, which are among the most costly to generate. For each ADHD-200 or ABIDE paper coauthored by a data contributor, we calculated the difference between the amount of data used in the publication (i.
e. , sample size) and the total contribution to the consortium (from coauthors on the manuscript). The median difference between publication sample size and data contribution by coauthors was 286 for ADHD-200 and 142. 5 for ABIDE. Obtaining a similar increase in sample size by acquiring data from these clinical populations in a single lab would have been expensive and time consuming. Interestingly, we found that 20% of INDI-based publications from ABIDE data contributors used fewer samples than they contributed-largely reflecting more restrictive inclusion criteria related to age, sex, diagnostic phenotype, and/or image quality. Application of such criteria was made possible by the availability of shared data, and presumably enhanced the validity and reproducibility of the findings. Another means by which shared data sets are becoming increasingly available is the resource generation model, in which data are specifically collected for the purpose of sharing (e.
g. ). This model is advantageous in that the explicit open access intent allows researchers and funding agencies to justify investing in the creation of data resources that are notably larger in scale and broader in scope than what would typically be acquired by a single team. In INDI, the NKI-RS is an ongoing coordinated effort of three principal investigators and four NIH-funded projects dedicated to generating an open lifespan data resource for the scientific community. To date, 189 articles have been published based on NKI-RS data, 167 of which did not include the NKI-RS PIs and 76 of which were written by individuals completely outside of their publication sphere (i. e. , there is no detectable relationship between the PIs and the authors of these papers based on coauthorship histories in the literature).
Impact beyond. It is important to note that the impact of INDI goes beyond what can be captured by the present analyses. Our searches revealed 639 publications that mentioned INDI in their text but did not use INDI data, suggesting that INDI and resultant research has impacted the thinking of authors in ways other than simply providing data. Additionally, 71 publications employed either the scripts used for the analysis of data in the initial FCP release manuscript  13  or their derivative platforms  21, 22  -again highlighting utility beyond the data alone. INDI has also given rise to a set of projects sharing preprocessed data including the Preprocessed Connectomes Project (http://preprocessedconnectomes-project. org), the R-fMRI Maps Projects (http:// mrirc. psych. ac. cn/RfMRIMaps), and the C3-Brain Project (http:// mrirc.