dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodology for identifying NPEs registered as UK dormant companies Most of the literature to date has focused its attention on NPE activity by examining the patenting and litigation activity of large, well-known patent aggregators in the US; however, here, we focus on small NPEs operating in Europe. To do so, we opt not to rely on external lists of NPEs provided by specialized IP companies, but rather we propose a new methodology for identifying independent NPEs that are registered as dormant companies. To identify dormant companies, our primary source of information is Orbis Intellectual Property Database (Orbis IP), 5 which identifies 1 Note, the US Federal Trade Commission (2016) uses the term "PAE" as opposed to "NPE". 2 Fed. 3 The Xilinx v. Invention Investment Fund I LP case reported by Feldman (2014,   p.
292) is illustrative of this problem. In response to the litigation case brought by Invention Investment Fund, Xilinx filed a declaratory judgement action challenging some of the patents asserted against them. However, some of the parties that Xilinx named were discharged by the judge, for the simple reason that the owners of the patents were not those identified by Xilinx. 4 http://patentblog. kluweriplaw. com/2013/06/10/u-s-white-house-annou nces-initiatives-against-patent-trolls/?print=print  5  The Orbis IP database (compiled by Bureau van Dijk Electronic Publishing, BvD) is a commercial dataset that provides economic and administrative data for more than 360 million companies and information on approximately 115 million patents worldwide, including publication information, ownership, industry, history of transfer, and opposition.
companies with "dormant company" status and links global patent data to companies. Orbis IP defines a dormant company as one that is "still registered, but has no significant activity (and no significant accounting transactions during the accounting period)". According to Orbis IP, there were about two million dormant companies active in Europe in 2019, concentrated primarily in the United Kingdom (679,583), Spain (583,098), Romania (129,390) and the Czech Republic (123,399). Legislation regulating dormant companies differs from country to country, which might in part account for the differences in the numbers of such companies. In France, for instance, companies that do not report any activity for two years can be dissolved, 6 while in the UK companies can remain dormant for any length of time.
Patent data and main variables Our sample of 224 NPEs registered as dormant companies in the UK hold a total of 14,158 patent documents 14  (including granted and nongranted applications), of which only 3,348 (24%) are filed at the UK Patent Office, highlighting the international dimension of the phenomenon. The rest of the patent applications are filed primarily at the EPO (9%), USPTO (8%), Chinese Patent Office (7%) and Australian Patent Office (7%) (Fig. A1  in Appendix). In the analysis reported here, we collected patent data from the three patent offices with most applications (i. e. , the UK Patent Office, EPO, and USPTO) in order to determine the characteristics of the patents held by the NPEs identified in the analysis. The data come from the OECD Patent Quality Indicators Database (January 2020)  15  for patents filed at the USPTO and EPO, and from PATSTAT (October 2019) and the UK Patent Office (2020) for patents filed at the UK IPO.
The reason is that our sample Fischer and  Henkel (2012) focus on large and known NPEs and patent aggregators, while we do focus on small and less known NPEs -that, on average, assert patents as their primary business models (Chien, 2012; FTC,  2016). However, given that, on average, transferred patents are of higher Filing years: 1990-2015. Sources: OECD Patent Quality Indicators Database, January 2020 (Squicciarini et al. , 2013). For reasons of space, we omit the estimated coefficients for the year and technology dummies.
Margins tables are available upon request by the authors. quality and broader scope than their non-transferred counterparts (Serrano, 2010), we suspect that our estimated coefficients can be biased, as NPE patents are generally subject to more trading than the average patent. To control for this possibility, we used the USPTO Patent Assignment Database (PAD, 2017 Version) -which tracks patent ownership changes for all patents filed at the USPTOto detect and exclude from the analysis any non-dormant company patents that had not been transferred. 22, 23 Table  A1  in the Appendix confirms the previous results: a broader scope increases the relative probability of a patent being held by the NPEs registered as dormant companies in the UK, as opposed to be other types of assignees, while patent quality plays no role.
More specifically, twelve are located at The 606 Centre, 5A Cuthbert Street W2 1XT, London and one at Carpenter Court 1, Maple Road SK7 2DH, Stockport. Interestingly, the same Source: Based on data from Orbis IP, OpenCorporates and Companies House. The methodology used to identify the companies is described in Section 3. 1. 22 We relied solely on the USPTO database as its patent transfers are more complete than those of EPO, which only records reassignments during the grant stage (Ciaramella et al. , 2017), and they provide a more complete set of patent controls than UK IPO. 23 To identify patent transfers at the USPTO, we drew on information contained in the 'assignment' data file in the USPTO Patent Assignment Database (2017) (Graham et al. , 2018). The full methodology is described in Sterzi  (2020).
Therefore, neither A. M. C. P. nor L. V. have control over the company. In addition, Article 8 of the incorporation document states that in meetings of the board of directors, the directors may be represented by one or more proxies who do not have to be directors of the company but who must be appointed by public or private document, with or without powers of substitution. 26 Orbis is a financial database, it contains data from financial reports of companies as well as shareholder and management information (directors, ultimate corporate owners, etc. ) (https://www. bvdinfo. com/en-gb). 27 We identified ten companies in which DGDB acts as a director. However, since both Orbis and OpenCorporates only allow searches to be made in fields related to the role of company directorsand not as shareholderswe suspect that the number of companies in the DGDB network is likely to be more than ten.
AVM is the most frequently targeted entity, appearing as a defendant or plaintiffin the case of invalidity actionsin eight litigation suits. 37 Source: Based on data from Darts-ip and Google Patents. 29 It is, of course, possible for multiple companies to have the same address, given that companies selling incorporation services provide a registration address in their packages. The services sold by these companies are tailored towards providing anonymity for individuals that wish to create a company. For this particular purpose, a service provider, such as SFM (https://www. sfm. com/), offers nominee directors and nominee shareholders: "The nominee director service may be used where a client doesn't wish to be personally appointed or has to meet local requirements. The name of the director will appear in the corporate documents, in any business contract and sometimes in the jurisdiction's business registers.
32 One inventor in particular has filed 81 patents. 33 This may be the case, for example, of W. R. , who appears as the director of two UK companies with names that are similar to one of DGDB's shell companies and which specialize in providing legal services to help individuals set up Limited Companies and monetize patent assets. 34 Citations are computed by Orbis IP at July 2021, 12 th. 35 These patents are DE10220060B4, DE20204265U1, DE10148799C2, DE10050370B4, DE19834095B4, DE50210968D1, DE202006018745U1, DE10220061B4. 36 The patent litigation analysis is based on data provided by Darts-ip. 37 In some cases, the names of the defendants and plaintiffs were not reported or only their initials were reported. The decision to litigate patents in Germany follows the general strategy of NPEs of targeting the largest technological market in the EU, as well as the jurisdiction that is predisposed to grant preliminary injunctions even when a patent is later found to be invalid (Cremers et al.
Funding Funding was received for this work. All of the sources of funding for the work described in this publication are acknowledged below: 1 French National Research Agency (ANR, Project NPEIE -https:// npeie. org -ANR-17-CE26-0014-01_ACT). 2 VIA Inno (https://viainno. u-bordeaux. fr/) 3 IP2Innovate (https://ip2innovate. eu/).
Declaration of Competing Interest We wish to draw the attention of the Editor to the following facts, which may be considered as potential conflicts of interest, and to significant financial contributions to this work: The paper is part of a study (Sterzi et al. , 2020) that has been produced with partial funding from French National Research Agency (ANR, Project NPEIE -https://npeie. org -ANR-17-CE26-0014-01_ACT), VIA Inno, the technology intelligence platform of the University of Bordeaux specialised in developing methodologies for innovation landscapes characterization, and IP2Innovate (https://ip2innovate. eu/). The responsibility for the information and views expressed in the paper lies entirely with the authors. The nature of potential conflict of interest is described below: IP2Innovate is a coalition of small and large companies that create innovative products and services in Europe.
Percentage of patents by technology field. The category "Other fields" includes Consumer goods, Civil Engineering, and Furniture, Games. Source: OECD Patent Quality Indicators Database (January 2020) for EPO and USPTO patents and UK IPO and PATSTAT (October 2019) for UK patents. Year of filings: 1990-2015 for EPO and USPTO patents; 2000-2015 for UK-IPO patents.
Patent Quality and Scope: Descriptive evidence. Patent quality is proxied by the number of forward citations in a five-year period following the filing date and takes into account patent equivalents. Patent scope is proxied by the number of distinct four-digit International Patent Classification (IPC) classes assigned. Summary statistics are based on observations from regression #1, Table3for EPO; regression #1, Table4for USPTO; and regression #1, Table5for UK patents. Sources: OECD Patent Quality Indicators Database, January 2020.
7 The countries in this study include Australia, Canada, England, France, Germany, the Netherlands, New Zealand, Spain, Sweden, Switzerland, and the United States (US). Using the NAM high-need, high-cost framework, we identified a comparable persona, defined as an older person hospitalized with a heart failure exacerbation with a co-occurrence of diabetes. We then evaluated patterns of resource use across countries for 365 days following a heart failure exacerbation. Specifically, in this study, we asked the following key questions. (1) How does health care spending vary across different components of the care pathway-including primary care, outpatient specialty care, hospital care, drugs, and post-acute rehabilitative care-among 11 high-income countries (where data are available)? (2) To what extent do these variations in health care spending persist after taking into account the relative country-specific utilization across care settings? Finally, (3) among a more complex subset of patients hospitalized with heart failure with a comorbidity of diabetes, how does an additional comorbidity of COPD influence health care spending across countries relative to those without diabetes or COPD?.
| Country datasets The ICCONIC collaborative uses patient-level data from 11 countries: Australia, Canada, England, France, Germany, the Netherlands, New Zealand, Spain, Sweden, Switzerland, and the United States. Specific details of each dataset used can be found in Appendix Table  A1. The representativeness of each dataset is found in Appendix Table  A2. The years used in this dataset were between 2015 and 2017 across most countries, except in England (2014-2016)   and Australia (2012-2016), both of which used more years of data to obtain a larger sample size. Data in three countries-New Zealand, Sweden, and Switzerland-covered their entire population. Data in three other countries were regional samples-Australia (New South Wales), a full region in Canada (Ontario province), and Spain (Aragon region).
Data in the remaining five countries were large, diverse national samples, including in England, France, Germany, the Netherlands, and the United States. The proportion of patients covered in each dataset varied across countries, from 3% in Spain (Aragon) to 100% in New Zealand, Sweden, and Switzerland. Datasets included patient-level linked data across different domains of care, including primary care, outpatient specialty care, acute hospital care, post-acute rehabilitative care, and outpatient pharmaceuticals. Countries' ability to collect comprehensive data across each domain for health care utilization and spending categories varied (see Appendix Tables A3 and A4 for details).
We then built a construct that reflected these requirements, which were identified as an older person, aged 65-90 years old, who was hospitalized with a heart failure exacerbation and who had a comorbidity of diabetes at the time of hospitalization. As a sub-sample, we also identified patients who had an additional comorbidity of COPD in a subset of countries that were able to do so. We required at least 2 years of data. The first identification step was to identify all patients in year 1 who were hospitalized with a primary diagnosis of congestive heart failure with International Classification Code-10 of I50. x, as defined by the World Health Organization. Patients were included in the sample only once from their first recorded hospitalization in year 1. Given the lack of comprehensive longitudinal data across most countries, we were unable to know if the hospitalization was the first hospitalization related to heart failure or not.
We then identified the subset of patients who at the time of admission also had a diagnosis of diabetes, including International Classification of Diseases, Tenth Revision (ICD-10) codes: E11. x, E12. x, E13. x, and E14. x. Of note, type 1 diabetes (E10. x) was excluded from these analyses a priori in order to identify a more uniform population across countries. Finally, we identified subsets of patients that had an additional comorbidity of COPD, including those with ICD-10 codes: J41. x, J42. x, J43. x, and J44. x. We also identified the prevalence of other chronic conditions using Elixhauser comorbidity definitions. 8 All countries used ICD-10 classification codes, except Spain (which used ICD-9 codes) and the Netherlands (which used a set of codes given by the data supplier that matched the ICD-10 diagnostic codes).
| Ethics approval The proportion of women was as low as 36. 5% in Australia to as high as 50. 7% in Germany. Countries varied in the ability to capture secondary diagnoses in the index hospitalization. The United States (6. 3, SD 1. 7) and Germany (6. 1, SD 2. 0) had the highest mean number of comorbidities captured while Canada (3. 5, SD 1. 3) and New Zealand (3. 9, SD 1. 4) captured the lowest number in their datasets. The ability to track diagnosis varied significantly based on the financial incentives to upcode diagnosis and the number of secondary diagnoses that are recorded in each dataset. 7,9.
| Limitations This study has notable limitations. First, there are important differences in country datasets used for this study. For example, some countries utilized administrative claims data from public or private insurers, while other countries used national registry data or large survey data that are linked with claims data. It is possible that the data captured in the regional databases may not be generalizable to other parts of the country. However, the use of a prespecified construct with clinical input from an advisory board was used to limit crossnational differences and potential misclassifications. In the United States, data were limited to the Fee-For-Service Medicare population, and it did not include people enrolled in Medicare Advantage, the private option for beneficiaries.
In addition, all countries had access to hospital data with diagnostic codes that allowed for the reliable identification of people with heart failure. Second, there are important differences in national coding practices between countries and costaccounting, which may influence the results. Where possible, we have documented these differences to identify potential sources of bias. Furthermore, many countries were missing key variables of utilization and spending, for example, those related to the post-acute rehabilitative care setting. Identification of specific procedures and tests in the hospital were also not able to be detected across countries due to data limitations. Long-term data were also not available across the majority of countries, which may be important as well. However, we believe the dataset and comparison as currently collected offers important insights into the care for multimorbid personas across countries.
persona with heart failure and diabetes. Drivers of spending vary across countries, with the United States being the most expensive country due to high prices and higher use of post-acute rehabilitative care. ACKNOWLEDGMENTS This work was partially funded by Commonwealth Fund and the Health Foundation. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would like to acknowledge the ICCONIC Advisory Board members for their contributions to this project. This research representing Spain has been partially financed by the Regional European Fund, ISCIII, REDISSEC, grant number RD16/0001/0007. Canadian data management was supported in part by a grant from the Ontario Ministry of Health to the Health System Performance Network and by a grant to ICES.
Walter P. Wodchis is supported by a Research Chair funded by the Trillium Health Partners Foundation. This work representing England is based in part on data from the Clinical Practice Research Datalink obtained under license from the UK Medicines and Healthcare products Regulatory Agency and in part from Hospital Episode Statistics Copyright © (2020), re-used with the permission of the Health & Social Care Information Centre. All rights reserved. The data are provided by patients and collected by the NHS as part of their care and support. The interpretation and conclusions contained in this study are those of the authors alone. We thank the Data Management Team at the Health Foundation and Dr. Will Parry for their work in preparing the data. We like to thank BARMER for providing access to the researcher data warehouse for the German data of this project, especially Joachim Saam and Martial Mboulla-Nzomo who were incredibly supportive in analyzing and extracting the data.
This research representing Australia was completed using data collected through the 45 and Up Study (www. saxinstitute. org. au). The 45 and Up Study is managed by the Sax Institute in collaboration with major partner Cancer Council NSW; and partners: the National Heart Foundation of Australia (NSW Division); NSW Ministry of Health; NSW Government Family & Community Services-Ageing, Carers and the Disability Council NSW; and the Australian Red Cross Blood Service. We thank the many thousands of people participating in the 45 and Up Study. We also acknowledge Services Australia (formerly the Australian Government Department of Human Services) for the provision of Medicare Benefits Schedule (MBS) and the Pharmaceutical Benefits Schedule (PBS) datasets. The Admitted Patient Data Collection (APDC) and Emergency Department Data Collection (EDDC) data were provided by the NSW Ministry of Health and linked to the 45 and Up Study data by the Centre for Health Record Linkage (CHeReL).
Invasive alien species are responsible for a high economic impact on many sectors worldwide. Nevertheless, there is a scarcity of studies assessing these impacts in Central and South America. Investigating costs of invasions is important to motivate and guide policy responses by increasing stakeholders' awareness and identifying action priorities. Here, we used the InvaCost database to investigate (i) the geographical pattern of biological invasion costs across the region; (ii) the monetary expenditure across taxa and impacted sectors; and (iii) the taxa responsible for more than 50% of the costs (hyper-costly taxa) per impacted sector and type of costs. The total of reliable and observed costs reported for biological invasions in Central and South America was USD 102. 5 billion between 1975 and 2020, but about 90% of the total costs were reported for only three countries (Brazil, Argentina and Colombia).
Data collection We collected cost data for invasive alien species from a publicly available repository that compiles the economic impacts of invasive species worldwide, the InvaCost database (originally 2,419 entries;  Diagne et al. 2020b ). The original dataset was complemented by incorporating data collected from non-English references (5,212 entries;  Angulo et al. 2020)  and by adding supplementary cost data from new references containing cost information (2,374 entries; Ballesteros-Mejia et al. 2020). These data resources were reviewed and merged into a single database, which is the current and most up-to-date version of InvaCost (version 3. 0; accessible at https://doi. org/10. 6084/m9. figshare. 12668570. v3). The data were filtered to contain only the countries of interest (see below).
Cost entries with low reliability or reporting only potential costs (as classified by  Diagne et al. 2020b)  were also removed to allow for a standardised multi-country comparison. In short, low reliability identify grey source documents that used an estimation methodology based on no traceable or relevant references, ambiguous underlying assumptions or irreproducible calculations (see  Diagne et al. 2020b). Next, we used the "expandYearlyCosts" function of the "invacost" R package v. 0. 1-3  (Leroy et al. 2020)  to expand the 442 cost entries to 960 cost entries in total, so that each cost entry corresponds to a single-year cost estimate (see  Leroy et al. 2020  for a detailed explanation). In the InvaCost database, references reporting costs for a multi-year period can be inserted in one row and need to be expanded as previously explained to allow the assessment of the cumulative and mean yearly costs  (Leroy et al.
In South America, there are the Falklands/Malvinas, which are part of the United Kingdom and French Guiana as part of France and, amongst the Caribbean Islands, there are Guadalupe, Martinique and Saint-Martin also as part of France. In the subset used here, there are no data for Guyana in South America and the Bahamas, Barbados, Dominica, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines and Trinidad and Tobago in the Caribbean Region. The dataset used for this study is provided as Suppl. material 1: Table  S2.
Analysis To describe the costs of invasive alien species over the years, we calculated the average annual cost caused by invasive alien species between 1975 and 2020, considering intervals of 5 years, using the "summarizeCosts" function in "invacost" R package  (Leroy et al. 2020). To investigate the geographical pattern of costs amongst the countries, we ran two non-metric multidimensional scaling (NMDS) analyses, using the "metaMDS'' function, from the "vegan" package  (Oksanen et al. 2019). NMDS is an ordination method to represent a distance matrix in a predetermined number of axes  (Borcard et al. 2011). Thus, first, to represent the countries according to the differences in presence and absence of invasive alien species presented in the database, we ran the analysis using a Jaccard distance matrix.