dataesr/scientific-paragraphs-categorization huggingface dataset:
Their respective importance in turn determines whether or not such a change stimulates the public policy. We provide some conditions under which it is possible to conclude that inequality impairs the environmental policy. But in general the outcome remains unclear. Second, we conduct a full-fledged empirical investigation of the link between the income distribution and environmental policy. The main dependent variable corresponds to the public expenditure in environmental protection. Our data consist of a sample covering thirty-one European countries over the period 1996-2019. We analyze the impact of the Gini index on public environmental expenditure by means of a fixed-effect model with robust standard errors, accounting for the potential non-monotonicity. Results show the existence of of an inverted J-shape relationship between inequality and environmental spending.
Introduction Linking healthcare data sets can create valuable resources for research, particularly when investigating rare exposures or outcomes. However, across Europe, the permissions processes required to access data can be complex. This paper documents the processes required by the EUROlinkCAT study investigators to research the health and survival of children with congenital anomalies in Europe. Methods Eighteen congenital anomaly registries in 14 countries provided information on all the permissions required to perform surveillance of congenital anomalies and to link their data on live births with available vital statistics and healthcare databases for research. Small number restrictions imposed by data providers were also documented.
METHODS Each congenital anomaly registry was required to submit to the EUROlinkCAT Coordinator (St George's, University of London), the permissions for the registry to fulfil its surveillance remit and to send data to EUROCAT (the European network of population-based congenital anomaly registries). An umbrella protocol was then supplied to each registry, providing sufficient information for them to apply to the relevant authorities to enable them to link their data on liveborn children with congenital anomalies to national and regional electronic databases containing information on the survival and morbidity of these children. The registries were responsible for obtaining the necessary permissions, and once obtained these were submitted to the EUROlinkCAT Coordinator. This article summarises all the permissions obtained.
As the permissions needed for surveillance differed from those needed for the subsequent research, details regarding the two sets of permissions are reported separately. The English data on congenital anomalies included in the EUROlinkCAT project were provided by five regional congenital anomaly registries. However, in April 2015, the regional registries in England were all combined into the National Congenital Anomaly and Rare Disease Registration Service (NCARDRS). As the regional registries no longer exist, the permissions that would be required to obtain Open access data from NCARDRS are reported here instead. This is why information is available for 18 registries in total, rather than the original 22. Information from 18 registries in 14 countries are presented, relating to the timeframe of the EUROlinkCAT project from 2017 to 2022-it is therefore possible that some permission requirements have since changed.
Permissions required for data linkage and analysis for research purposes Of the six registries who only linked their data to mortality data, five required specific ethics permission to do so, and three of these also required other additional permissions. Of the 12 registries who linked their data to electronic healthcare databases, eight required specific ethics permission to do so and four of these required other additional permissions. The four registries who did not require specific ethics permission to link their data to electronic healthcare databases, did however require other additional permissions, such as demonstrating to the data providers that the data would be stored safely, that the project was of sufficient scientific merit and providing other assurances arising from the European Union's General Data Protection Regulation (GDPR) 2016/679 requiring that the data be used in a transparent manner.
Once the data had been linked to other databases, the authorities providing these data often imposed strict restrictions on the publication of results when small numbers of children were involved. This was to ensure that children with rare congenital anomalies and their families could not be identified. Some authorities did allow aggregate results from small numbers of individuals to be distributed among named researchers, with the requirement that these results could only be published when combined with data from other registries. Permitting the internal distribution among specified researchers was extremely helpful, as it enabled meta-analyses over several European countries to be performed for groups of rare congenital anomalies and combined results to be published. Authorities suppressing the provision of small numbers of cases for analysis (eg, through rounding counts to the nearest 5) often resulted in a registry's data being excluded from the meta-analyses due to Open access the rounding having a significant effect on the overall estimates.
DISCUSSION To the authors' knowledge, this is the first example in the literature of a multicentre, multinational health data linkage project documenting the broad range of permissions required across Europe to access and link data on children with congenital anomalies. The increased awareness that this article provides of the high variability in data providers' requirements and the timescales that the permissions-related processes operate within, will be of great assistance to funding bodies and both current and future studies in this and related fields. For multicentre studies, the widely variable waiting times make it very challenging to determine a common timeframe for the overall study to work to. Indeed, the challenges faced by EUROlinkCAT researchers are not unique, with researchers across the world facing similar issues (UK, 1 Australia 2 and USA 3 ).
, 2022). Here, we conducted a systematic review of studies on delay discounting in neurodegenerative diseases following the guidelines of preferred reporting items for systematic review and meta-analysis (PRISMA). We provided the context and objectives of the review, we described sources, eligibility criteria, and selection process of studies included in the review and we detailed our main variables of interest (see "Different outputs of the task" in Box 1). We used a list of key words related to delay discounting and neurodegeneration (delay discounting, temporal discounting, intertemporal choice, Alzheimer's Disease, mild cognitive impairment, frontotemporal dementia, Parkinson's disease, dementia with Lewy bodies, Huntington's disease, progressive supranuclear palsy, amyotrophic lateral sclerosis) in several databases, and we selected 37 studies based on pre-defined inclusion and exclusion criteria (see all methodological details in Fig.
ATP, adenosine 5′-triphosphate; JAK, Janus kinase; TYK2, tyrosine kinase 2. • Two global phase 3 trials, POETYK PSO-1 (NCT03624127) and POETYK PSO-2 (NCT03611751), demonstrated that in patients with moderate to severe plaque psoriasis, deucravacitinib 6 mg once daily (QD) was significantly more efficacious than placebo and apremilast based on the coprimary endpoints of ≥75% reduction from baseline in the Psoriasis Area and Severity Index (PASI 75) and static Physician Global Assessment score of 0 (clear) or 1 (almost clear) with a ≥2-point improvement from baseline (sPGA 0/1) at Week 16 and was well tolerated  8, 9  -Clinical responses were maintained through 52 weeks in patients who received continuous deucravacitinib treatment from Day 1  10  • Patients who completed the POETYK PSO-1 and PSO-2 parent trials could enroll in the ongoing POETYK long-term extension (LTE) (NCT04036435) trial and receive open-label deucravacitinib • Clinical efficacy was shown to be maintained well through 2 years with no new safety signals compared with Year 1 in deucravacitinib-treated patients who entered the POETYK LTE trial  11, 12  Objective • To report the safety and efficacy of deucravacitinib for up to 3 years (Week 148) through the cutoff date (June 15, 2022) in patients with moderate to severe plaque psoriasis who participated in the POETYK PSO-1 and PSO-2 trials.
61. 4% (57. 0%-65. 6%) 72. 4% (68. 3%-76. 2%) 76. 2% (71. 4%-80. 6%) 61. 4% (57. 0%-65. 6%) 72. 4% (68. 3%-76. 2%) 79. 4% (74. 6%-83. 5%) 61. 1% (56. 6%-65. 6%) 72. 6% (68. 2%-76. 6%) 73. 2% (68. 7%-77. 8%) TFR analysis captures discontinuations coded as "lack of efficacy. " b For mNRI analyses, if there are no missing data (ie, no imputed values in the dataset), 95% CI was obtained using the Clopper-Pearson method based on the observed data. c Data callouts represent the response rate (95% CI). CI, confidence interval; LTE, long-term extension; mNRI, modified nonresponder imputation; PASI 75, ≥75% reduction from baseline in Psoriasis Area and Severity Index; TFR, treatment failure rules. TFR analysis captures discontinuations coded as "lack of efficacy. " b For mNRI analyses, if there are no missing data (ie, no imputed values in the dataset), 95% CI was obtained using the Clopper-Pearson method based on the observed data.
b TFR analysis captures discontinuations coded as "lack of efficacy. " c For mNRI analyses, if there are no missing data (ie, no imputed values in the dataset), 95% CI was obtained using the Clopper-Pearson method based on the observed data. d Data callouts represent the response rate (95% CI). CI, confidence interval; LTE, long-term extension; mNRI, modified nonresponder imputation; PASI 90, ≥90% reduction from baseline in Psoriasis Area and Severity Index; TFR, treatment failure rules. a.
Second, our results suggest that intermediation is a determinant of export participation. The literature (at least since  Melitz, 2003)  has largely viewed the decision to export as driven by the profitability of firms' own products in foreign markets. Our results, however, show that (pure) trade intermediation is an important activity of manufacturers in export markets. Developing an export network thus allows firms to reap gains beyond the possibility of exporting their own products. Third, our results show that trade intermediation has so far been underestimated. To get a sense of the relative magnitude of intermediated trade in Turkey's total exports, we add to our dataset exports done by professional intermediaries. We find that the presence of PI -thus focusing on a strict definition of trade intermediation -increases the amount of aggregate trade that is intermediated by around 70%.
Data We use three main databases from Turkey in this paper: the first is the Industry Production Statistics database that is available for 2005-2014 and provides the volume and value of production and sales of each product that is produced by a firm. 3 Products are classified according to the 10-digit PRODTR classification of which the first 8-digits correspond to the EU's official production classification PROD-COM (production communautaire). 4 All PRODTR are concorded overtime by the Turkish Statistical Institute (T Üİ K) to the 2010 classification for consistency. The second dataset is the Industry and Services Statistics database that includes annual statistics such as total sales, number of employees, wages, expenses, and investment for all firms that have at least 20 employees for 2003-2014.
5 This dataset reports the firm's self-proclaimed industry based on the 4-digit NACE Revision 2 classification. In our final sample of manufacturing exporters, we find that around 6% were self-proclaimed wholesalers or retailers. 6 Excluding these manufacturing wholesalers/retailers does not change our results as shown in subsection 3. 2. The third dataset we use is the Foreign Trade Statistics database which reports exports and imports of each firm annually for 2002-2014. Exports and imports are classified by firm-country-GTIP (Gümrük Tarife İstatistik Pozisyonu), where GTIP is a 12-digit product code whose first 8-digits correspond to the EU's Combined Nomenclature (CN) and the first 6-digits correspond to the internationally standardized Harmonized Schedule (HS). Since the PRODTR-GTIP concordance table provided by T Üİ K is for 2010, the descriptive cross-sectional analysis in this paper uses 2010 as the benchmark year.
We merge the three databases and firms that report production only. Then, we follow  Erbahar's (2020)  data cleaning procedures and restrict the sample to firms with at least 20 employees, and drop observations where exports are larger than total sales, or where production sales are larger than total sales. Finally, we keep firms that have produced and exported at least one manufacturing good in 2005-2014. This results in a sample of 22,255 firms, covering around half of manufacturing exports and two-thirds of manufacturing production. In the last part of our paper, we add exports done by professional intermediaries which make up about 11%-18% of manufacturing exports.
Potential misreporting and misclassification by firms Since the Industry Production Statistics database is based on survey data, one might be concerned that firms do not report all of their production activities. If this is the case, we would be incorrectly labeling some produced products as sourced, since trade data is coming from the customs, which records all transactions. However, as illustrated in Figure  A. 1, the instructions page (Descriptions section) of the official Annual Industrial Production Statistics Questionnaire Form requires that all production activities done by the firm, including products the firm produces itself to sell to consumers, products that the firm produces as an intermediate product for its own production, and products that the firm produces as a subcontractor for other firms (including intermediate and final goods), among others, should be listed and detailed.
Moreover, the key information for our purposes is whether a firm is producing a given product or not (i. e. the extensive margin) since that determines how we classify a product as produced versus sourced. There can still be misreporting on the intensive margin of production but that would not confound our analysis. Finally, one might be concerned that firms misclassify product codes when filling out the surveys. Recall that the Foreign Trade Statistics database reports codes based on the GTIP classification which covers 15,337 products, whereas the Industry Production Statistics database reports codes based on the PRODTR classification which covers 4,446 products. Our analysis, on the other hand, due to the concordance between the two datasets, aggregates the data to the level of 2,494 HS6+ products.
Alternative definitions One might argue that multinational companies (MNCs) drive sourced exports since they might be exporting goods produced by their subsidiaries that have different tax IDs. We label a firm as an MNC if it has any foreign ownership, and find that there are 1,350 MNCs in Turkey, making up around a third of the manufacturing exports in our sample. 11 In Table  6  columns 1 and 2, we exclude these MNCs from our dataset, and find TIP shares that are similar. We find that PI shares are slightly higher (except for 2014) when we exclude MNCs, indicating that domestic firms engage relatively more in pure intermediation. Another related concern is that firms that engage in sourced exporting might be re-exporters (firms that export imported goods). We follow our approach in subsection 3.
Acknowledgements We thank  Carolina Giorgetti ,  Luca Dal Zilio ,  Giacomo Pozzi ,  Lauro Chiaraluce  and  Marco Maria Scuderi  for insightful discussions. We thank the Editor  Rebecca Bendick  and two anonymous reviewers for their valuable comments. AG and CM were supported by  European Research Council Advance  Grant  835012  (TEC -TONIC ). MC participated in this work as Principal Investigator of the  European Research Council (ERC)  project  FEAR  (grant  856559 ) under the European Community's  Horizon 2020 Framework Programme. GM acknowledges support from  NUS Startup  Grant  22-3565-A0001-1 , and  MOE Tier   2  Grant  22-5191-A0001-0.
Data: Contentious Cases of Patent Litigation The empirical analysis focuses on contentious patent infringement cases in Germany. Cases are defined as contentious if they went through the entire appellate process, including court decisions by each of the respective courts, including RC at the first instance, HRC at the second instance, and FCJ at the third instance. Judgments of these cases were publicly available through databases of the FCJ (Bundesgerichtshof 2020a) and the case law of North Rhine-Westphalia (Ministerium der Justiz des Landes Nordrhein-Westfalen 2020). The FCJ database contains all publicly available decisions back to the year 2000. Since 2005, all decisions include reference to prior instances, including their docket number, which enabled us to retrieve judgments issued by the respective RCs and HRCs.
The lion's share of 116 cases (86%) originated from the three RCs, Duesseldorf, Mannheim, and Munich (Table  2 ), yet complete documentation at the lower instances was only accessible for 37 cases, of which 34 originated from the RC Duesseldorf. The Duesseldorf cases predominate for two reasons. First, Duesseldorf attracts by far the most cases even when compared to the other prominent locations of Mannheim and Munich (Table  1 ). Second, the case law database of the state of North Rhine-Westphalia provides the most comprehensive documentation of cases. Since we do not aim to compare regional court locations but to shed light on the appellate process as a whole, this dominance does not negatively affect our results. Rather, it helps to understand the mechanisms that may be of interest to the future UPC framework.
Methods: Qualitative Content Analysis of Judgments Written judgments are 'standardized artifacts'  (Wolff 2017, p. 503 ) that allow researchers to arrive at interpretations and draw conclusions. Our review of these documents is based on qualitative content analysis techniques  (Mayring 2017). Using iteratively coding, paraphrasing, and reducing the content of more than 100 decisions (3 instances multiplied by 34 cases), we inductively developed comparison criteria and categories that we perceived at all instance levels  (Mayring 2017). For this purpose, we used the MAXQDA software (VERBI Software 2020). Through this iterative process, we developed a comprehensive understanding of the litigation procedure and were able to trace the structure of the available texts  (Srivastava and Hopwood 2009).
This allows us to assess which courts are particularly important for case law and whether regional courts refer to decisions of other regional courts. The case citations in the documents are indicated differently depending on the place and instance, as they are sometimes published in case law reports or collections of court decisions. The online portal dejure. org comprises an extensive law and decision database with around two million court decisions in Germany (dejure. org Rechtsinformationssysteme GmbH 2020). Using this platform, we could assign case numbers to all citations in the judgments under study. As mentioned earlier, the FCJ assigns a designation to the document that relates to its subject matter. For ease of identification, we recorded this designation in addition to the case number.
Research Strategy The systematic literature review covered 5,000 publications, unfolding nearly seven million citations. We also employed the software Publish or Perish  (Harzing, 2007)  to investigate the research coverage from 1900 to date. The search parameters included only words in the English Language. Then, the academic dataset selected was the Google Scholar and Scopus databases. After the first round with Publish or Perish  (Harzing, 2007) , a text network analysis was performed to identify the most relevant emerging themes; they were used as keyword entries in a new iterative round. Then, the data were content, citation, and text network analyzed. The emerging themes were also analyzed geographically, through Google My Maps® (see Figure  4 ).
These queries allowed to obtain all documents matching with the search criteria (often, several documents for 1 patient and 1 stay). Then, for each week, we kept the oldest document for 1 patient and 1 hospital stay, and we calculated the number of hospital stays with at least one document mentioning the keyword contained in the query. As we used 19 keywords, we obtained 19 variables from CDW eHOP. Then, we built a database containing the time series constructed from the structured data (total n=1,335,347 time series). Regrading Google Correlate, we calculated the Pearson correlation between both national and regional incidence rates and the time series from the database. We retrieved the 100 most correlated signals. As our prediction period is from May 2014 to February 2018, we calculated the correlation between January 2008 and April 2014.
Linear Approach To minimize the negative effects of using a large number of input variables, potentially including redundant information, we used Elastic Net, a regularized multivariate regression methodology that can identify parsimonious models  [30]. Elastic Net combines the power of Lasso and Ridge regressions, allowing to perform a variable selection on variables that are highly correlated  [31, 32]. We performed the Elastic Net regression analysis using the caret package in R (R Foundation for Statistical Computing) and the associated function fit with the glmnet method  [33, 34]. We fixed a coefficient λ=0. 5 to give the same importance to Ridge and Lasso methods. The formulation of our model is the following: Here, yT denotes AG incidence rate at time T=t, t+1, t+2, t+3 (for the different levels of prediction), denotes historical variables, denotes Google data, denotes EHR data, and denotes residuals.
Regional Analysis For real-time estimates, the error values range from 76. 9 to 38. 4 and the correlation values range from 0. 54 to 0. 76, with the lowest error and the highest correlation values obtained with AR(52) model and the models combining historical data and external data sources. For 1-week, 2-week, and 3-week estimates, the error values range from 69. 2 to 41. 1, from 60. 5 to 43. 8, and from 63. 3 to 44. 1, respectively. The lowest errors values for long-term forecasts are all obtained with the model using historical and EHR data. In terms of 1-week, 2-week, and 3-week correlation, the values range from 0. 53 to 0. 72, from 0. 56 to 0. 70, and from 0. 53 to 0. 70, respectively. The highest correlations for long-term forecasts are all obtained with the model using historical data and both data sources, Google and EHR.
Linear Approach Figure  8  and Table  S1  in Multimedia Appendix 1 show, for the linear approach, errors and correlation for AG at the national and regional levels, for forecasts up to 10 weeks. At the national level, the lowest error for real-time estimates is obtained with the linear approach using only historical data-AR(52). For long-term forecasts, from up to 1 week to up to 10 weeks, the lowest errors are obtained by using historical data and both data sources, Google and EHR. In terms of correlation, in all cases, the highest values are obtained by using only historical data. At the regional level, in terms of errors, both data sources, Google and EHR, allow to improve accuracy for forecasts from up to 4 weeks to up to 10 weeks. In terms of correlation, results are similar to those at the national level, with high values obtained by using only historical data.