dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and sample We used a unique dataset created by combining data on individual backers, equity crowdfunding campaigns, entrepreneurs, and logo design characteristics. Our sample consisted of 10,611 actual investments made by 5,427 backers across 62 crowdfunding campaigns during the period April 2015 -January 2016. These 62 campaigns were from the same equity crowdfunding platform 2  and represented a subset of the sample of 174 campaigns used in Study 1 (data for the other 112 campaigns was not made available to us by the other platform). Our detailed individual-backer-level panel dataset allowed us to observe the investment frequency and campaign choices of each of the 5,427 backers in our sample. For each of the 62 campaigns in our dataset, supplementary data on entrepreneurs was manually collected from the LinkedIn profiles of entrepreneurs and data about logo design characteristics was obtained from the survey reported earlier (see Study 1).
Model specification Our dataset allowed us to observe multiple investments by an individual backer across multiple campaigns and over time. Hence, investment behavior could have been correlated across the investments made by the same backer, violating the assumption of independence across investments. Further, the campaigns spanned across multiple industries, creating a problem of nested data. We addressed these issues by explicitly modeling the multilevel structure of our data via a mixed model with individual-specific random effects and industry dummies. 3  The use of mixed effects allowed us to account for unobserved individual-level heterogeneity using individual-specific random intercepts. We further included time-invariant variables to capture observed heterogeneity in backers and use industry fixed effects.
e. ,. 08 × GBP 1,416; see Tables  2  and 4 ). This effect is comparable in magnitude to the effect of high validity cues such as venture team size and number of previous pledges (a one unit increase in team size resulted in a 10% increase amount invested; a one unit increase in the number of previous pledges resulted in a 7% decrease in amount invested; see Tables  2  and 4 ). Effect size. Since our dataset had multiple variables that were scaled differently, we followed  Allison et al. (2015)  and computed the standardized coefficients to examine the effect size of Logo complexity. These coefficients are reported in Table  4  -Model 4. The standardized estimate for Logo complexity (   =. 04; z(10,562) = 2. 22; p =. 03) was significant at the 5% level. Moreover, we found that the effect of Logo complexity was more than twice as strong as that of -Model 4 show that the effect size of Logo complexity was stronger than the effect size of eight of the campaign-related, venture-related, backer-related, and entrepreneur-related controls included in our model.
However, the data used in this study is not without limitations. For instance, our data emanates from a single platform and pertains only to campaigns that have successfully reached their funding goals, thus limiting our ability to infer whether logo complexity can play a role in driving the success or failure of crowdfunding campaigns. Moreover, while the data for logo design characteristics was collected at the aggregate logo level (from an independent set of respondents), the investment decisions were observed at the backer level. Our multi-level modeling approach attempted to mitigate this problem. However, the effect sizes might have been more substantial if all data had been collected for the same cohort of investors. To address this concern, we conducted a controlled experimental study (Study 3), where logo evaluations and investment decisions were made by the same backers.
7 We obtain EW, ERC and GMV data from August 1970 onward because the GSCI data began in January 1970; we need 150 returns from that point forward to compute the variance-covariance matrix. We end our study in August 2017. 8 The GSCI and BCOM indices are not identical. The GSCI and BCOM comprise 24 and 22 individual commodities, respectively; 19 of these commodities are common to both indices. We do not see any reason to favour one particular universe of the two commodity indices. Therefore, we consider the union of the two commodity universes, and our commodity universe comprises 27 individual commodities at the end of our study period. lower volatilities and lower correlations with equity and bonds. Our results are not affected by the various kinds of robustness checks: analyses with the periods covering the full range of the GSCI and the BCOM, since January 1970 and 1991, respectively; and sub-sample analysis breaking our studying period down into financial expansionary and recessionary cycles  (Bessler and Wolff, 2015).
The construction of the ERC, EW and GMV indices Let us denote by Ω nt the time-t variance-covariance matrix of commodity futures returns, where n stands for the number of commodities in the portfolio. We consider the individual commodities present in the GSCI and BCOM and build our indices backward by including these futures contracts when they are available in the futures market. The number of individual commodity futures contracts, n, ranges from 10 at the beginning of our study in August 1970 to 27 in August 2017. 9 The prices of the futures contracts were downloaded from the Bloomberg database. We estimate the variance-covariance matrix using historical moments with 150 past daily commodity futures returns. 10 et us denote by π (GM V ) nt and π (ERC) nt the time-t n-dimensional portfolios of the GMV and ERC indices invested in the commodity futures markets.
The empirical comparison of first generation commodity indices This section compares all the first generation commodity indices: the BCOM, ERC, EW, GMV, GSCI, GSLE, GSEW, GSRW and GSLE. The period starts in March 1995 (the backdating of the GSRW) and includes 270 monthly returns until August 2017. We start with some summary statistics in Table  1. Table  1  demonstrates that the indices computed after the risks of the commodity futures contracts, namely, the GMV, ERC, EW, GSEW and GSRW, have higher Sharpe ratios than do the indices based on commodity world production, the BCOM, GSCI and even the GSLE. Table  1  shows that the Sharpe ratios of the commodity indices are lower than the Sharpe ratio of the S&P 500. This result is unusual in the literature, as shown by  Bhardwaj et al. (2015) , and is related to our short data set.
Robustness checks In this section, we extend our study to cover a longer backdated data set of the GSCI and GSLE. The five indices available for this period are then the ERC, EW, GMV, GSCI and GSLE. The study period starts in August 1970, with a dataset of 565 monthly returns. The results provided by this robustness check are in line with those of section 3, with the following minor differences. The Sharpe ratios of the ERC, EW and GMV are of the same order of magnitude as the S&P 500  (Bhardwaj et al. , 2015) , whereas those of the GSCI and GSLE are approximately half those of the Sharpe ratios. The positive statistical differences in the Sharpe ratios between the indices based on financial diversification and those based on the production of commodities are greatly improved. The correlations of the commodity indices with the S&P 500 are lower than are the correlations in the short period.
In this case, what emerges is not larger but "smaller than the parts", a kind of "intersection of individuals" by standardization of one of their characteristics  [7, 8]. This viewpoint is supported by the analysis of digital databases  [7]. Yet, the type of information one gets from database analysis cannot be easily rephrased in the language of statistical physics, which mostly deals with the analysis of collective phenomena through simple models of interacting agents. It is thus a priori hard to reconcile the physicist's viewpoint according to which "the whole is more than the sum of its parts", and the sociologist's viewpoint supporting the idea that the "the whole is less than its parts" because humans need to simplify their intrinsic complexity to form a coherent whole in certain circumstances.
), we get the expected result. Acknowlegement : The authors wishes to thank Valentine Genon-Catalot and Fabienne Comte for helful discussions. This work was supported in part by the Labex CEMPI (ANR-11-LABX-0007-01)A Technical toolsBurkholder Davis Gundy inequality. Let us consider the filtration F t = σ(η, (W s ) 0≤s≤t , (L s ) 0≤s≤t ).
Paper selection and data extraction Our objective was to identify high quality articles addressing behavioral interventions to promote and support long-term medication adherence among adult patients. To identify relevant articles, we used a three-phase review process. First, we identified recently published Cochrane Database reviews. Second, we assessed the studies referenced in the included reviews for their risk of bias. Third, we extracted data from the included studies regarding study characteristics (Table  2 ) and reported implementation research components (Table  3 ). We describe each phase in detail below. Phase 1. We searched MEDLINE via PubMed for Cochrane Database systematic reviews that were published from January 1, 2014 through November 9, 2017, addressing medication adherence (Table  1 ).
Three reviewers (LLZ, MD, SDG) independently assessed the titles and abstracts of identified Cochrane Database reviews for relevance to: 1) medication adherence; 2) long-term or chronic medication use; and 3) adult populations. The reviewers then met and discussed differences in their categorization. Phase 2. Two reviewers (LLZ, MD) independently assessed all individual studies published after 2005, with selection bias evaluated as 'low risk of bias' using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions  (62). We included papers that had: 1) no criteria evaluated as a high risk of bias; and 2) a maximum of five criteria evaluated as an unclear risk of bias, or one criterion evaluated as high risk of bias in combination with up to four criteria evaluated as an unclear risk of bias.
Studied populations included patients with hypercholesterolemia (n=4), HIV (n=4), cardiac problems (n=3), schizophrenia (n=2), osteoporosis (n=2), diabetes (n=2), epilepsy (n=1), glaucoma (n=1), asthma (n=1), and hypertension (n=1). With regard to the medication adherence phase, two studies were intervening on medication initiation, two on persistence, and four on implementation, while in thirteen studies no differentiation was or could be made between the implementation and persistence phase. Medication adherence was measured using prescription refill data (n=7), self-reporting (n=5), electronic monitoring (n=3) or a combination of two of these measures (n=6). Sixteen studies (76%) reported statistically significant differences on at least one of the reported medication adherence outcomes.
Context: the environment or setting in which patients receive healthcare • Dissemination: the process of spreading knowledge, information, or interventions to other settings • Implementation science: scientific methods and principles that inform and influence the integration of evidence-based ideas and interventions into clinical practice • Medication adherence: the process by which patients take their medication as prescribed consisting of three interrelated phases TABLES Table 1. Search Strategy ((("2014/01/01"[Date -Publication] : "3000"[Date -Publication])) AND Cochrane Database Syst Rev. ) AND (((((((("Medication Adherence"[Mesh]) OR ("Patient Compliance"[Mesh:noexp]) OR (adherence[tiab] OR adherent[tiab]) OR (compliance[tiab] OR compliant[tiab]) OR (nonadherence[tiab] OR nonadherent[tiab]) OR (non adherence[tiab] OR non adherent[tiab]) OR (noncompliance[tiab] OR noncompliant[tiab]) OR (non compliance[tiab] OR non compliant[tiab]) OR ("Patient Dropouts"[Mesh]) OR ("Treatment Refusal"[Mesh]) OR (treatment refusal[tiab]))) OR (treatment refusals[tiab])) OR (treatment refusing[tiab])) OR (missed dose*[tiab])))).
• Context: the environment or setting in which patients receive healthcare • Dissemination: the process of spreading knowledge, information, or interventions to other settings • Implementation science: scientific methods and principles that inform and influence the integration of evidence-based ideas and interventions into clinical practice • Medication adherence: the process by which patients take their medication as prescribed consisting of three interrelated phases TABLES Table 1. Search Strategy ((("2014/01/01"[Date -Publication] : "3000"[Date -Publication])) AND Cochrane Database Syst Rev. ) AND (((((((("Medication Adherence"[Mesh]) OR ("Patient Compliance"[Mesh:noexp]) OR (adherence[tiab] OR adherent[tiab]) OR (compliance[tiab] OR compliant[tiab]) OR (nonadherence[tiab] OR nonadherent[tiab]) OR (non adherence[tiab] OR non adherent[tiab]) OR (noncompliance[tiab] OR noncompliant[tiab]) OR (non compliance[tiab] OR non compliant[tiab]) OR ("Patient Dropouts"[Mesh]) OR ("Treatment Refusal"[Mesh]) OR (treatment refusal[tiab]))) OR (treatment refusals[tiab])) OR (treatment refusing[tiab])) OR (missed dose*[tiab])))).
A disadvantage of these systems is that they cannot be used to match electricity generation with consumer demand. 10 By construction, the technology set Γ = {1, 2}. The sample comprises data on M = 3 inputs and N = 1 output for 16 Chilean hydroelectric power plants over the 12 months of the year 1997. There are 5 dam plants and 11 ROR plants in our sample. The data are publicly available on the web site of the Journal of Applied Econometrics. 11 The single output is electricity generated (in gigawatt hours). The three inputs are labor (thousands of workers), capital (real pesos), and water (cubic meters). More details concerning the data can be accessed from  Atkinson and Dorfman (2009)  and  Atkinson and Halabí (2005). Our knowledge of hydroelectric power generation in Chile leads us to believe that it may be possible for the manager of a given (dam or ROR) plant to use a given input vector to produce a given level of output for some of the time, and then use a different input vector to produce a different level of output the rest of the time.
e. , the number of times the relevant measure of performance is estimated to be ation with daily or weekly consumer demand. However, there are no pumped storage plants in our dataset. 1). The next three rows in each block report the geometric averages, 13  standard deviations, and minima of the relevant estimates. Four observations can be made with regard to Table  1. First, by construction, estimates of ITE obtained using the estimators (  14 ) and (15) can be no higher than the estimates obtained using the estimators (10) and (11) (i. e. , estimates of ITE obtained using the C-NC model can be no higher than those obtained using the NC-NC model). Table  1  reveals that, in the case of Chilean hydroelectric power generators, estimates of ITE obtained using (  14 ) and (  15 ) are on average 1 -0.
Conclusions In their seminal paper, O'Donnell, Rao, and Battese (2008) consider a metaset that is defined as the union of two or more underlying group-specific sets. They refer to the boundary of the metaset as a metafrontier, and they refer to the boundaries of the group-specific sets as group frontiers. They suggest estimating the metafrontier under the assumption that the metaset is convex. If this assumption is false, then their "convexifying" estimator is biased. Our key result, Proposition 5. 5, shows that both C and NC group-specific sets can (and generally do) yield NC metasets. This suggests that the convexification strategy should not be maintained a priori. In any case, it should be empirically tested. We used secondary data on Chilean hydroelectric power plants to explore the consequences of making incorrect assumptions about the convexity or nonconvexity of TPPSs and MTPPS.
7 See https://www. eia. gov/totalenergy/data/browser/xls. php?tbl=T02. 01&freq=m for US monthly data broken down by sector, and https://yearbook. enerdata. net/ for privately collected but publicly accessible yearly data on energy production by source and aggregate consumption for over 40 countries. The International Energy Agency IEA provides for-fee national and sectoral data at https://webstore. iea. org/world-energy-balances-2017. The UN has for-fee and free after a time lag data at https://unstats. un. org/unsd/energy/yearbook/default. htm. a number of researchers have calculated the energy to exergy ratios for many industrial processes, from which an aggregate measure of exergy K x E Σ has been calculated (Ayres, Ayres et al. 2003,   Figure 17, p. 243), and from which an aggregate measure of the exergy/energy ratio K x e Σ can been derived (Ayres and Warr 2005, Warr, Ayres et al.
15) 10 8 There are also some online databases; see f(1. 19)or example https://tntcat. iiasa. ac. at/PFUDB/dsd. 9 Here ( ) 0 K E represents the energy input to a representative machine at a specified initial time 0 (which can be any arbitrary time in the past). 10 Here ( ) 0 K X E represents the exergy output of a representative machine at a specified initial time 0 (which can be any arbitrary time in the past). Since we are considering the basic aggregate production function in this paper, we reduce Equation (1. 9) to: ( ) ( ) ( ) K X K E K t E t = ⋅ (1. 16) Our final and perhaps most controversial redefinition is that of Q (GDP). We argue that, with the inputs to the production function being exergy, the output should also be exergy: real GDP should be measured in terms of useful work. In keeping with our practice above, this should have two components: the total energy harnessed to generate GDP ( ).
Objectives The goal of this study is to identify, analyse and classify interventions to improve adherence to reporting guidelines in order to obtain a wide picture of how the problem of enhancing the completeness of reporting of biomedical literature has been tackled so far. Design Scoping review. search strategy We searched the MEDLINE, EMBASE and Cochrane Library databases and conducted a grey literature search for (1) studies evaluating interventions to improve adherence to reporting guidelines in health research and (2) other types of references describing interventions that have been performed or suggested but never evaluated. The characteristics and effect of the evaluated interventions were analysed. Moreover, we explored the rationale of the interventions identified and determined the existing gaps in research on the evaluation of interventions to improve adherence to reporting guidelines.
exclusion criteria We have excluded references that include interventions that do not specifically aim to improve the completeness of reporting, even though these interventions may actually influence completeness. For example, we have excluded clinical trial registration even though it may enhance the completeness of reporting, because its main goals are to improve clinical trial transparency while also reducing publication and selective reporting biases. search strategy and study selection On 8 May 2017, we searched PubMed, EMBASE and Cochrane Library databases for articles published between 1 January 1996 and 31 March 2017, in accordance with our scheduled search. 14 The detailed search terms for PubMed can be found in the protocol. The retrieved studies were exported into Mendeley and duplicates were automatically removed using it.
Data extraction A data extraction form was developed to collect the information necessary for data synthesis. Two reviewers (DB, JJK) independently performed a pilot data extraction on a random sample of five articles and subsequently refined the form. Extracted data included: 1. Publication characteristics: title, year of publication, author, author's affiliation country and field of study. 2. Characteristics of the intervention: a. Classification as evaluated or non-evaluated. b. Research stage: education, grant writing, protocol writing, manuscript writing, submission, journal peer review, copyediting and postpublication. c. Rationale of the intervention, which refers to the deduced reasons why the intervention is evaluated or proposed. d. For evaluated interventions: details of the intervention, study design (eg, RCT, before-after, etc), RGs considered and format (checklist, bullet points and/or examples), period of intervention, number of journals and articles involved, effect size of the intervention on adherence to RGs and measure used to assess this effect.
results The database search yielded 1399 citations after deduplication (see figure  1 ). Screening of titles and abstracts resulted in a first classification, after which 435 papers were included for full-text review. We also reviewed the full text of 24 additional references found through forward citation searching. Furthermore, a grey literature search yielded seven additional references. Finally, 109 references were included. Some of these interventions appeared in more than one reference and some of the references contained more than one intervention. Ninety of these references    1  shows all interventions in a tabular format together with their rationale. All interventions reported in this section were found in the literature and do not necessarily correspond to the personal ideas of the scoping review authors.
25 Finally, further databases of examples of good reporting for different RGs that are accessible to authors can be developed, as has been done for CONSORT. 26 couraging adherence Fourteen interventions found were associated with different strategies to facilitate compliance with RGs. 11 12 21 27-115 Six of these were evaluated. 12 27-107 113 unders might require authors to use RGs as a template for grant application proposals. 21 Later on, research ethics boards may require that protocols submitted for ethical approval clearly state which RGs the study will be using based on the study design, and that RG checklists are part of the application for ethics approval. 11 Funders.
Acknowledgements The authors thank the MiRoR Project (http:// miror-ejd. eu/) and  Marie Sklodowska-Curie Actions  for their support. The authors also thank  Matt Elmore  for editorial help. This review is part of a larger project whose next goals are (1) to capture editors' perceptions on the barriers and facilitators of some promising interventions identified in this review, (2) to explore new possible interventions and (3) to evaluate one of these interventions in collaboration with  BMJ Open. Contributors All authors contributed to conceptualising and designing the study. DB, EC and JJK independently performed screening. DB and JJK independently performed data extraction. DB performed initial data synthesis and EC, IB, DM, DA and JJK refined it. DB drafted the manuscript. EC, IB, DM, DA and JJK made major revisions.
Diffusion of low-energy houses is an important part of energy and climate policy in the European Union (EU) and in individual EU countries. Key barriers to the adoption of low-energy houses include additional construction costs and uncertainty sur-rounding actual energy and cost savings. In this paper, we econometrically analyze determinants of low-energy house adoption, including time and risk preferences. We rely on original data from a large survey conducted among households in eight EU countries. To our knowledge, this is the first empirical study of low-energy building adoption to rely on a demographically representative sample. Our set of covariates includes parameters of time and risk preferences that were elicited via state-of-the-art incentivized multiple price list experiments and via self-assessment scales.