dataesr/scientific-paragraphs-categorization huggingface dataset:
Understanding regional intangibles Although there is no consensus on the definition of intangible capital, it is generally viewed as a set of nonmaterial factors that contribute to the production of goods and services and are expected to enhance subsequent performance and generate future economic benefits to the entities that control their deployment  (Eustace, 2000). Intangibles encompass a broad range of factors and activities, and common examples include intellectual property (e. g. , patents, trademarks, copyrights), computer software and databases, brand recognition and other non-material assets. In knowledge-based economy, firms with limited tangible assets have an opportunity to benefit from investing in valuable intangible assets  (Velibeyoglu & Yigitcanlar, 2010). Reviewing intangible capital research over the past 20 years,  Ståhle et al.
Data To analyse intangible assets in the regional context, a combined dataset has been built from multiple data sources at the European regional level. In the context of Europe, intangible assets are viewed as key factors in growth policies and are promoted for achieving regional competitiveness and welfare (Peiró-Palomino, 2016). To classify regions, a common classification of territorial units for statistics (NUTS -Nomenclature des Unités Territoriales Statistiques) provided by Eurostat is followed, which divides the economic territory of the EU into hierarchical levels. 2 The NUTS classification is a hierarchical system, which distinguishes between three levels: NUTS 1 captures major socio-economic regions, NUTS 2 captures basic regions for the application of regional policies, and NUTS 3 captures small regions for specific diagnoses.
This study utilises NUTS2 level of classification as it allows to make finer comparisons as well as has data available in most statistical databases. The latest NUTS classification includes 28 European countries that are divided into 281 NUTS2 regions (excluding Extra-Regio). The NUTS classification is defined only for the Member States of the EU. This study also added two European Free Trade Association (EFTA) countries, not included in the classification, but which, in agreement with Eurostat, have a defined coding of statistical regions (Norway and Switzerland, 7 NUTS2 regions each). The total number of NUTS2 regions is equal to 295. The data on assets (tangible and intangible) and other regional characteristics have been drawn from Eurostat Regional Database, 3 European Quality of Government Index (EQGI), 4 European Social Survey (ESS) 5 and Regional Innovation Scoreboard (RIS).
Dependent and control variables Regional performance was assessed using regional gross domestic product (GDP) per capita and used as a dependent variable. This measure is closely related to regional welfare (Peiró-Palomino, 2016). The measure is obtained from Eurostat regional database. The study uses control variables of labour and capital investment. The former is measured by labour force expressed by economic activity rates of population aged between 15 and 64 year old. The latter is measured by gross fixed capital formation that covers, in particular, machinery and equipment, vehicles, dwellings and other buildings. Natural logarithm of the measure was taken. Both indicators are retrieved from Eurostat regional database.
If no regional and no higher level data were available for the current and previous year, missing data were not imputed. As five countries in our sample -Cyprus, Estonia, Latvia, Luxembourg, and Malta -consist of only one NUTS2 and one NUTS1 regions (excluding Extra-Regio), the country's values of the variables are unitised. For data imputation using the previous year and, therefore, if it was based on earlier NUTS classifications, the NUTS codes were recoded to the latest classification to preserve consistency. This concerns several regions of such countries as Finland, France, Hungary, Ireland, Lithuania, Poland, Slovenia and UK. In ESS dataset, the variables were measured at the individual level, and, therefore, the data were aggregated to the regional level using specific post-stratification weights to reduce the sampling error and potential non-response bias, according to the recommendations in ESS methodology report.
7  For the calculation of composite indicators (forms of capital) and regional intangible capital index, the data have been normalised. To follow more closely a normal distribution, individual indicators have been transformed using a square root transformation if the degree of skewness of the raw data exceeded 1. Following this transformation, data were normalised using the min-max procedure so that the normalised scores ranged from 0 to 1. The composite indicators are calculated as the unweighted average of the normalised scores of the indicators. The regional intangible capital (RIC) index is calculated as an average of six forms of capital that describe different aspects of intangibles at the regional level.
Spatial analysis of direct and complementary effects of intangibles To test regional economic implications of intangible assets, a set of regression analyses was performed. In the model, we estimated the relationship between six different regional intangibles and regional performance, following The IC-dVAL model (Figure  1 ). Direct and complementary effects of intangibles were analysed. Complementary effects were tested using the moderated regression analysis. In particular, human capital and renewal capital were included in the model as independent variables, institutional capital, social capital, ICT infrastructure capital and entrepreneurship capital, which can be considered as the context in which economic actors operate, were used as moderator variables, and regional performance in terms of GDP per capita was used as a dependent variable, transformed by taking the natural logarithm to decrease the variability of data and make data conform more closely to the normal distribution.
Concluding remarks The purpose of the study was to increase our understanding of intangibles and their economic implications, focusing on the regional level. Regional context is one of the closest environments in which organisations and other economic actors operate. This study adopted an integrative approach towards examination of regional intangibles and covered multiple types of intangibles including human capital, renewal capital, institutional capital, social capital, ICT infrastructure capital and entrepreneurship capital. The study also adopted a holistic geographical perspective by analysing regions across Europe. Methodologically, a regional database on NUTS 2 level was created composed of multiple data sources such as Eurostat Regional Database, European Quality of Government Index -EQGI, European Social Survey -ESS, and Regional Innovation Scoreboard -RIS.
It is comprehensive in covering most intangibles and simultaneously accounts for economic, social and institutional facets of intangibles and their relationships. Important and original results are found when considering complementarities. For example, institutional and entrepreneurship capitals are found to positively moderate the relationship with both human and renewal capital, while ICT infrastructure capital -the relation with renewal capital. Also, whereas institutional and entrepreneurship capitals did not show direct positive effects on regional performance, their moderating effects are positive and significant. This gives insights on the comparative and complementary effects of different types of intangibles as determinants of regional level of economic performance in Europe. Fourth, from the methodological standpoint, the study provides a unique and comprehensive dataset of intangible assets of European regions and proposes an integrative measure of regional intangible capital composed of six forms of capital, which to the best of our knowledge have not been studied simultaneously before.
Furthermore, the dataset was created with the most recent data available and the measures go beyond investments data and include inputs (e. g. , R&D expenditures), process (e. g. , institutions), and outputs (e. g. , GDP per capita) data, as well as are readily available from reliable statistical sources providing data at the regional level. The results have policy implications and stress the importance of policy actions aimed at accelerating the accumulation of intangible assets in regions to boost local and national competitiveness and welfare. As for the policy implications, the study brings to the fore the importance for policymakers of considering intangibles' contribution to economic growth holistically as well as by developing a reasoning in terms of constitutive pillars: Human capital, Social capital, Entrepreneurship capital among others.
Background: Since pediatric cancer drug development is a global enterprise, we sought to provide an overview of the landscape of intercontinental clinical trials in pediatric oncology opened over the last decade. Methods: ClinicalTrials. gov was systematically searched to identify all clinical therapeutic trials which opened between 2010 and 2020 and recruited pediatric patients (<18 years) with cancer. Results: Over the last 10 years, 295 (8. 7%) of 3383 therapeutic pediatric cancer trials were international and 182 (5. 4%) were intercontinental. Most intercontinental trials were phase-1 or 2, with 25% late-phase, 65% were sponsored by industry, and North America was involved in 92%. Industry-sponsored proportionally more phase-1 trials than academia (41% vs. 25%); conversely, academia sponsored more phase-2 and late-phase trials (39% and 31% vs.
| Trial selection The ClinicalTrials. gov database  13  was searched to identify all clinical trials (early and late phase, including post-marketing authorization studies) which opened between 2010 and 2020 and recruited pediatric patients (<18 years) with cancer. The ClinicalTrials. gov database was selected as it is the largest publicly available trial database and includes the majority of registered trials. The search was performed in October 2020 with the term "Cancer" (all fields) and filtered by "Child (birth-17)" (Age Group) AND "Interventional (Clinical Trial)" (Study Type) AND "from 01/01/2010 to 01/01/2020" (Start Date). All studies focusing on cancer and meeting the search criteria were reviewed. Only international trials (open at sites in ≥2 countries) were included. The exclusion criteria were applied hierarchically: (1) Not international (i.
K E Y W O R D S adolescent cancer, childhood cancer, clinical research, clinical trials, drug development, international collaboration, rare diseases Supportive care and diagnostic procedure trials that were specifically cancer-directed were included, as they constitute an essential part of cancer care. A review of ClinicalTrials. gov was performed by one investigator (T. d. R. ) and eligibility of all identified studies was assessed by at least two investigators (T. d. R. /G. R. ). Data extraction was performed by one investigator (T. d. R. ) with quality review by three investigators (G. R. /A. P. /G. V. ). In case of discrepancy, the decision was taken by consensus of the investigators. Further data cleaning/validation was carried out by T. d. R.
| Definition of trial variables The sponsor was considered academia for trials sponsored by universities, hospitals and/or academic research institutions/groups, or industry for trials sponsored by pharmaceutical companies. The sponsor classification by clinicaltrials. gov was followed. COG/National Cancer Institute (NCI) trials were grouped together as a subcategory of academic trials. Location was categorized as North America, South America, Europe, Asia, Oceania, Africa, or intercontinental (for those with two or more participating continents). Intercontinental trials were subcategorized according to the involved continents (e. g. , North America-Europe, Europe-Asia-Oceania, etc. ). Trials were considered phase 1 (including phase 1/2), phase 2, late phase (phase 2/3 or phase 3). Primary outcome measures were considered as efficacy (when measuring response rate, incidence of adverse events (AEs) for support interventions, technique effectiveness, etc.
| Statistical analysis The median and interquartile range (IQR) were used to describe quantitative data. Percentages were used to describe qualitative data. Percentages may not always total 100% due to rounding error. The χ 2 or the Fisher exact test were used for statistical comparisons when appropriate. Multiple linear regression models were used to analyze evolution in the number of trials over time (one for each analysis). The R software v. 3. 4. 0 was used to perform data processing and data analysis, and to plot the results. The maps were created using the free online software mapchart. 14 he study methodology complies with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and guidelines whenever applicable to the registry meta-research context.
20 The industry-driven, phase 1/2 SCOUT trial, which involved the same four continents, investigated the use of larotrectinib in tumors with NTRK-fusion in children, and contributed to its histology-agnostic approval. 21, 22 Finally, the academic, randomized, phase 3 EURAMOS-1 trial involving Europe, North America, and Oceania investigated intensified postoperative chemotherapy in newly diagnosed osteosarcoma, in patients with a poor response to preoperative chemotherapy, and stablished standard of care for this population. 11 e acknowledge the limitations of this study. Clinicaltrials. gov was the only source used for the search, a registry that is being increasingly used by investigators to assess research practices. Conducting valid analyses requires an understanding of both the capabilities and limitations of the database.
23 A major issue to be considered is that requirements for reporting trials have changed over time. However, the search for this study was limited to trials starting from 2010, notably after systematic registration of clinical trials was promoted in 2005. 24 Nonetheless, the changing nature of clinicaltrials. gov needs to be considered when interpreting the results of the study, especially regarding the analysis of evolution over time, as well as our dependence on some of the categorizations given by the platform that might be misleading or incomplete (e. g. , age, primary completion date). Another limiting aspect is the possible underrepresentation of trials conducted in low-and middle-income countries, which may be listed in other registries,  23, 25  or of small European trials only registered in EudraCT.
Despite these shortfalls, clinicaltrials. gov remains the largest publicly available trial database and included the majority of registered trials. Using MedLine, Embase or other literature databases would have limited the search to only trials with published results (58 of 295, 20%). Finally, we acknowledge that analyzing data from pediatric and AYA trials jointly may limit the conclusions. Nonetheless, since we are aiming to identify barriers and at a later stage propose solutions to increase intercontinental collaboration, we believe that a high-level approach including pediatric and AYA issues is appropriate given the global nature of our work. A more detailed AYA-specific analysis is currently under preparation, in collaboration with the ACCELERATE FAIR (Fostering Age Inclusive Research) working group.
ACKNOWLEDGMENTS The authors thank  Andrea Demadonna  and  Michael Vranken  for their dedication and support to the activities of the  ACCELERATE International Collaboration Working Group , and the  Andrew McDonough B+ Foundation  for financial support of  ACCELERATE. The funding source had no role in study design, collection, analysis, or interpretation of data, or decision to submit results. Members of the  ACCELERATE International Collaboration Working Group: Academia: Todd Alonzo ,  University of Southern California, USA; Andrea Biondi, Università degli Studi di Milano-Bicocca, Italy ;  Kathy Brodeur-Robb ,  C17 Council, Canada; Teresa de Rojas, ACCELERATE, Belgium ;  Maryam Fouladi ,  Nationwide Children's Hospital, Columbus, USA ; Thomas Gross,  NIH  ( National Institute of Health ),  USA ;  Stephen Hunger ,  Children's Hospital of Philadelphia, USA ;  Pamela Kearns ,  National Institute for Health Research (NIHR) Birmingham, UK ;  Geoff McCowage ,  Australasian Children's Cancer Trials, Australia ;  Alberto Pappo, St.
The complementarity of imported capital goods and skills due to skill-biased technical change embodied in those capital goods then boosts firms' production skill intensity  (Burstein et al. , 2013; Koren and Csillag, 2011; Parro, 2013). This explanation differs from the input-quality explanation that hypothesizes that all intermediate inputs of high quality and not only capital goods affect the demand for skills in production. To provide just one example from our database, an Ecuadorian firm improved the quality of the jerseys, pullovers and related articles (HS-6 code 611090) it produced, importing as part of the quality upgrading process higher quality fibres (HS-6 code 550700). In conclusion, these models predict that input-trade liberalization creates incentives for firms to access high-quality foreign inputs and to upgrade output quality.
Ecuador's accession to the WTO In this section, we describe Ecuador's trade integration process of the 1990s and early 2000s, including the major change that resulted from the country's accession to the WTO. Ecuador's trade policy during the 1970s and 1980s was characterized by trade protection policies focusing on import substitution as was the case in other Latin American and Caribbean countries during this period. Trade was consequently very restricted to shield industries from foreign competition, with high nominal tariffs and import licenses in most sectors. A unilateral trade-reform plan was launched with Ecuador's accession to the World Trade Organization (WTO) in 1996. The main consequence Fig. 1. Changes in input tariffs from 1996 to 2007. Source: Authors' calculation based on input tariff at the product level faced by firms constructed by matching the Ecuador's effectively applied import tariffs with respect to the rest of the world at HS 6-digit product level from WITS (World Bank) with our data by establishing a product correspondence to the 11digit ISIC-Rev.
Data and variables description We use a Census panel dataset that is collected by the Ecuadorian Institute of Statistics (INEC) of formal manufacturing plants (corresponding to ISIC Rev. 3 category D) with 10 or more employees for the period 1997-2007. The manufacturing estimating sample contains 12,343 manufacturing plant-year observations and has information on plants overall sales and value-added, skilled (non-production workers) and unskilled (production workers) employment and wages. The distinctive feature of our data is that it is complemented by two other datasets, which have information on plants' intermediate inputs and on plants' output final good products, respectively. The first dataset gives annual plant-level information on primary materials, auxiliary materials, replacements and accessories, and packing materials used in production.
For each intermediate input, plants provide information on quantities and values separately for national and foreign inputs. The second dataset has information on each plant's final good products on quantities and values sold in the market. Both input and output products are provided by INEC at detailed 11-digit product code level that is built on the ISIC-Rev. 3 industry classification. We implement several data cleaning procedures and check the quality of our dataset following  Bernard et al. (2010) ,  Kugler and Verhoogen (2012)  and  Goldberg et al. (2010). This includes testing for firms with irregular output product drops (i. e. products that disappear from production and then reappear again) and firms with product jumps (i. e. products that are produced only once in the intermediate years of firm presence in the sample).
These tests are satisfactory in that product drops and jumps are relatively infrequent. Moreover, the consistency of importer characteristics to those  Kugler and Verhoogen (2012)  obtain for Colombia gives additional confidence in the quality of the new data (see  Bas and Paunov, 2019 , for these statistics). The Annex describes the dataset and cleaning procedures in more detail (see also  Paunov, 2011). The firm level skill composition is measured by skill intensity, the share of the non-production workforce relative to the total workforce, and the skill premium, the ratio of the wage of non-production workers relative to the total firm' wages. Our firm-final good output (input) product level datasets allow us to compute measures of input and output prices (unit values) and quality at the firm-product level.
We compute complexity at the firm level as the weighted average of the product complexity of all imported (domestic) intermediate inputs used by a firm in a year relying on constant initial weights. For our analysis, we obtain the relative imported input complexity as the ratio of the imported input complexity over the domestic one at the firm level. We also make use of our firm-input data to obtain a measure of imported input varieties. We compute this measure as the share of imported input varieties relative to the total imported and domestic input varieties of a firm in a year. Finally, we use Ecuador's effectively applied import tariffs at HS 6digit product level as provided by the WITS database of the World Bank. The effectively applied tariffs correspond to the most-favourednation (MFN) tariff or the tariff applied by the country as decided under a preferential trade agreement, if applicable.
In addition to those tariff data from the WITS dataset (World Bank), we include an interaction term between exporter status and export tariffs. Results of Table  A3  in the Annex show our findings are robust to the inclusion of export tariffs. The effect of the export tariff reductions on quality upgrading is positive but not significant across all specifications we test. The effect of export status and the interaction term are in general not significant. 6. 5. Ecuador's financial crisis and other reforms of the 1990s and 2000s Then, we test whether our results are affected by Ecuador's financial crisis and RER variations. To do so, we first interact our tariff measures with an indicator variable for the crisis years 1999-2000. The evidence presented in Table  A4  in the Annex confirms results are not driven by Ecuador's economic crisis.
Acknowledgements We would like to thank the  Ecuadorian Statistical Office  for their support. The authors would like to thank  Daniel Yi Xu  (the editor) and two anonymous referees for their comments. The comments from  João Amador ,  Ana M. Fernandes ,  Lionel Fontagne ,  Dominique Guellec ,  Beata Javorcik ,  Volodymyr Lugovskyy ,  Gianluca Orefice ,  Ariell Reshef ,  Vanessa Strauss-Kahn ,  Eric Verhoogen ,  Christian Volpe Martincus  and participants at the 2012 Empirical Investigations in Trade and Investment (EITI) conference in Tokyo, the 2017 Meeting of the  Royal Economic Society in Bristol , the  Novafrica Workshop on Globalization and Development of June 2017 , the DEGIT conference 2017 (PSE), the " International Trade and Labor Markets" seminar of University of Paris 1 , the 2019 CAED at the  University of Michigan ,  Ann Arbor , the 2019 LETC in Slovenia and the 2020 Empirical Trade Online Seminar are gratefully acknowledged.