dataesr/scientific-paragraphs-categorization huggingface dataset:
4 Past, Present, Future: Much More Than an Archive Software Heritage is now an infrastructure that grows day by day: the bulk of the archive's content is the result of automatic harvesting, but pearls are beginning to be introduced through the patient work of recovering significant historical software, following the SWHAP acquisition process that has been developed in collaboration with the University of Pisa and UNESCO. Exhaustiveness is still far from being achieved, but the archive already contains the largest corpus of source code available on the planet, with more than 180 million archived origins, for over 12 billion unique source files, each equipped with an intrinsic identifier based on cryptographic hashes [2,3] (Fig. 1 ). This unique infrastructure has a multiple mission: of course, it is about preserving for future generations the source codes of the past that made the history of Computer Science and the Information Society, but also, and above all, we are trying to build a very large telescope that will allow us to explore the present evolution of the software development galaxy, in order to better understand it, and to improve it, in order to build a better technological future.
A Peek Under the Hood In Fig. 2  one can find a high level overview of the architecture of the Software Heritage crawler, and of the key data structure that is used to store all the source code with its development history. Software development and distribution platforms abound, and there is no single standard protocol to interact with them, so Software Heritage is building, with the help of a growing community, a large set of connectors, called listers, to identify the available software projects. There is no single standard for keeping track of versions of software either, nor for distributing packaged software, so we are also building a large set of converters, called loaders, that are able to encode all these different formats in a single, universal data structure, that is based on the Merkle tree construction  [4].
This uniform data structure contains today over 30 billion nodes and 350 billion edges, one of the largest public graphs available. In this giant Merkle graph, every software artefact is identified by a cryptographic intrinsic persistent identifier, called SWHID, that can be used to check integrity of the artifact without relying on any central registry. A key side effect is the fact that duplicate artifacts can be immediately spotted (they have the same identifier!), and the Software Heritage archive only keeps one copy of each of them, while keeping track of all their occurrences.
Beyond Preservation, A Strategic Issue The Software Heritage archive is already the most important source code collection in the world, but there is still a long way to go: a wide range of players, from cultural heritage to industry, from research to public administration, must be brought together around it. To do this, we are counting on a growing network of ambassadors (see https://softwareheritage. org/ambassadors/). It is clear that software has become an essential component of all human activity, and therefore unrestricted access to publicly available software source codes is becoming a digital sovereignty issue for all nations. The unique infrastructure that Software Heritage is building, and its universal approach, is an essential element to meet this challenge of digital sovereignty while preserving the common good dimension of the archive.
Once the food sources were set in each hole, we placed a slime mould (10mm ⌀) in the centre of the arena 2 mm away from each food source. We replicated the experiment 50 times for each food quality. For each replicate, we measured the time taken by the slime mould to reach either one of the three food sources. To assess the difference in the latency to reach the food as a function of the food quality, we used a linear mixed model (function "lmer", package "lme4") in R (RStudio Version 1. 2. 1335). The models were fitted by specifying the fixed effects (explanatory variables) and the concentration in yolk (continuous predictor). The sclerotia identity was also added to the model as a random factor. We transformed the dependent variable using the "bestNormalize" function (package "bestNormalize").
Optimal policies Tajima et al. set the general problem of a decision-maker that must integrate evidence simultaneously on the value of different competing options, then reach a decision where the reward for that decision is the true value of the option chosen, discounted by the time taken to reach that decision  [4]. Optimal policy computations were performed in Matlab (Matlab 2020b), and were adapted from the dynamic program of  [4]. Optimal policies were computed for Bayes Risk and geometric discounting, for linear utility (r ≔ x), and for non-linear utility functions having the form r ≔ m 2 1 þ e À sx À 1 � � ; ð1Þ where m and s are shape parameters for the logistic function determining the interval of utilities and steepness of the slope respectively, and x is the raw input value. We systematically varied the m and s parameters to test magnitude-sensitivity under Bayes Risk optimisation and geometric discounting, under a range of utility function shapes ranging from almost linear, to almost stepwise.
J. A. R. M. was funded by the  European Research Council (ERC)  under the European Unions  Horizon 2020 research and innovation programme  (grant agreement number  647704 ), making a falsifiable prediction that is confirmed in two, highly divergent, species, one with a brain and one without. https://erc. europa. eu/funding/. A. D. acknowledges support from L' Agence Nationale de la Recherche , (grant number  ANR-17-CE02-0019-01-SMARTCELL ), https://anr. fr/. A. R. acknowledges support from the  Belgian F. R. S. -FNRS , of which he is a Chargede Recherches (https://www. frs-fnrs. be). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In pursuing this objective, we test a research model based on the Protection Motivation Theory (PMT), which was developed by  Rogers (1975)  and  Maddux and Rogers (1983). PMT has been widely applied to studies on information security behavior (see  Sommestad, Karlzén, & Hallberg, 2015; Mou, Cohen, & Kim, 2017). We adopted a variance-based structural equation modeling approach, namely Partial Least Squares (PLS) modeling, using the software SmartPLS 3. 2. 7  (Ringle, Wende, & Becker, 2015)  to test the conceptual model and the related hypotheses. The rest of the paper is organized as follows. In Section 2, we present the theoretical background and hypotheses. Section 3 describes the methodology and data collection approach. Section 4 presents the results, followed by the theoretical and managerial implications in Section 5.
Data collection Data were collected via an online survey administered in Spain in October 2019, with smartphone or tablet users targeted as respondents. More specifically, a market research company was charged to recruit the sample -inviting potential respondents via email, which were randomly distributed to members of the panel that met the requirements to participate in the study and a link to the survey. According to Statista (2020), the most widely used applications among the Spanish population are Whatsapp, Facebook and Instagram (see Fig. 2 ). Moreover, Statista's (2021) forecast displays the development of the number of smartphone users in Spain up to 2024. Between 2018 and 2024 the number of users is predicted to grow by nearly three million reaching 40. 33 million users. The share of smartphone users will reach 72.
96 percent in 2021 according to Statista estimates. The data covered includes individuals of any age who own at least one smartphone and use one or more of their devices once a month or more often. The operating system market in Spain is dominated by Android with an 87. 1 percent share of the market in December 2017. The only other significant system with more than a fraction of a percent is Apple's iOS. A majority of smartphone users in Spain use their devices at least once a week to search online, check emails, and visit social media sites. The indications provided by the most recent The App Date report (Statista, 2014), which analyzed the distribution of smartphone users in Spain in terms of age range, were applied to guarantee an acceptable level of representativeness of the sample. Thus, the market research company collected the data according to the following distribution in terms of percentage for each age range: 15% for the 18-24, 39% for the 25-34, 26% for the 35-44, 12% for the 45-55 years and 8% for the over 56-year-old users.
Measures Measurement scales used to assess the variables included in this study were drawn from the extant literature and some were slightly adapted to the context of privacy protection. The Appendix shows the whole set of items included in the questionnaire that was administered to collect the dataset. More specifically, the items were adapted from the scale of perceived severity proposed by  Woon et al. (2005) ,  Crossler (2010) , and  Mohamed and Ahmad (2012). Likewise, the concept of perceived vulnerability was measured by means of a scale adapted from  Mohamed and Ahmad (2012) ,  Dinev and Hart (2004), and Woon, Tai and Low (2005). To assess self-efficacy, a scale based on  Woon et al. (2005)  was applied, while the measurement scale for perceived response-efficacy was drawn and adapted from  Mohamed and Ahmad (2012) ,  Lee et al.
One might expect that also the employees' careers to suffer as a result. However, we argue that the firm's employees benefit from such collaborations. They leverage these collaborations to build social capitalhelping their mobility and careers. We find empirical support for our theory using a large sample dataset of video game companies. Our study points to an important yet hitherto neglected agency conflict: employees seek interfirm collaborations that benefit them personally but hurt their firm. Managerial Summary: We show that some alliances can be detrimental to a firm's performance yet can benefit its employees. Specifically, we find that collaborating with the same partner as a competing firm hurts firm performance but can be leveraged by employees to advance their careers-by using the opportunity to connect with competing firms and find better job opportunities.
| Data sources and sample We build a comprehensive dataset that entails detailed information on video games, the employees involved in their development, the developers they work for, and the publishers. MobyGames, our primary data source, 2 allowed us to track employees' entire careers in both public and private firms around the globe. Information entry follows strict coding standards and instructions specified on the MobyGames website. All entries are peer-reviewed before they 2 One of the world's largest video game documentation projects, it contains comprehensive information on games past and present from public and private firms from all around the world. The data include, among other things, a game's release date, the employees and firms involved in making it, whether it draws on licensed or franchised material, and reviews from print and online game magazines.
A key reason for the quality of the data is that those involved in game development have an incentive to document their participation in detail. Employees point to MobyGames to validate their track records when interacting with industry stakeholders. are published. For these reasons, research has widely used MobyGames (e. g. ,  Claussen, Grohsjean, Luger, & Probst, 2014; Katila et al. , 2022; Mollick, 2012). We match the data from MobyGames with revenue data collected by NPD Group, 3 that has tracked sales of video games in the United States since 1995, covering all distribution channels. We have monthly sales data from January 1995 through December 2018. NPD data is the primary source for video game sales information in many studies (e. g. ,  Kretschmer & Claussen, 2016; Zhu & Iansiti, 2012).
We complement our dataset by manually gathering information on the ownership of the developing and publishing firms from various websites, including MobyGames, RAWG, GiantBomb, VGChartz, and Wikipedia. We test our baseline hypothesis H1 by examining the effect of peer competition on two levels: the project (game) level and the year level. At the project level, we study the revenuebased performance of 28,560 video games. At the year level, we study the revenue-based performance of 3,420 developers, relying on 20,020 firm-year observations. As some developers are involved in multiple projects in a year, we use the average of the variables across the games with which the developer was involved in the focal year. To test our hypothesis on the effect of peer competition on employees' personal success (H2), we created an unbalanced panel of all employees active in the video games industry.
Though we analyze only 1995-2017, we collect data from the dawn of the industry in the 1970s to allow for accurate historical measures at the beginning of our panel regression (e. g. , experience-related measures). The unit of analysis to test H2 is the employee-year. This dataset has 582,272 employee-year observations from 135,580 employees. As some employees are involved in multiple projects in a year, we build the average of our measures across all games with which the employee was involved in the focal year to test H2.
| H1: Firm performance We measure firm performance at the individual project level and across all projects at the level of the year. At the project level, we operationalize firm performance (project level) as a game's revenues measured in millions of USD. 4 As the distribution of revenues in the video game industry is skewed, we use the natural logarithm of the revenues as a robustness check. At the year level, we operationalize firm performance (year level) as the sum of the revenues of all games a developer released in the focal year. 3 We used a five-step process to merge the MobyGames and NPD data. (a) We identified all versions of a single game listed in NPD and aggregated their revenues. (b) We matched the two data sources-based on the game's name, platform, and release date. (c) We manually reviewed all matching results to ensure accuracy.
(d) To improve the merge, we gave the unmatched observations to three research assistants who independently tried to identify the corresponding titles in NPD and MobyGames. If they did not identify the same pair of names in NPD and MobyGames as belonging to the same game, we tried to match them ourselves. (e) We managed to merge 83% of all the revenues listed in the NPD data to games in MobyGames. 4 To compare games released at different times, we focus on the revenues in the first 12 months after release. This is reasonable as the games in our sample made 83% of the recorded revenues in their first year.
ACKNOWLEDGMENTS We especially acknowledge the editorial team for their encouragement, feedback, and guidance throughout the revision process. We further appreciate the helpful comments and feedback from Seminar participants at  Harvard Business School  and  University of Mannheim  as well as from  Vikas Aggarwal ,  Sarath Balachandran ,  Matthew Bothner ,  Gianluca Carnabuci ,  Seth Carnahan ,  Carmelo Cennamo ,  Aaron Chatterji ,  Raj Choudhury ,  Russ Coff ,  Jason Davis ,  Linus Dahlander ,  Gina Dokko ,  Kathleen Eisenhardt ,  Alfonso Gambardella ,  Martin Ganco ,  Martin Gargiulo ,  Henrich Greve ,  Karin Hoisl ,  Alessandro Iorio ,  Riitta Katila ,  Ronnie Lee ,  Yonghoon Lee ,  John Mawdsley ,  Denisa Mindruta ,  Hakan Özalp ,  Emily Pahnke ,  Stevo Pavicevic ,  Sandeep Pillai ,  Hart Posen ,  Woody Powell ,  Joost Rietveld ,  Vera Rocha ,  Michelle Rogan ,  Oliver Schilke ,  Eunkwang Seo ,  Victoria Sevcenko ,  Deepak Somaya ,  Evan Starr ,  Marco Tortoriello ,  Filippo Carlo Wezel ,  Sandy Yu ,  Aks Zaheer , and  Feng Zhu.
After properly training the neural network, it can be utilised to assess the vehicle trajectories. However, the issues here are the assessment tends to suffer from a high computational burden for real-time applications when considering complex scenarios and the neural network needs to be re-trained when adding some 'assumed normal/abnormal' behaviours to the neural network for some specific scenarios. For example, the 'deceleration + stop/slowly moving' pattern is a normal behaviour when we consider public traffic monitoring but is becomes abnormal when a vehicle is loitering a high-value asset such as a military base. In order to accommodate these issues, a RD dictionary algorithm is created by summarising the observed manoeuvre patterns provided by the neural network. This can be carried out by providing a new dataset, called RD-training set, as an input dataset to the neural network and follows the same procedure used for the NN-training set.
Numerical Simulation of Target Monitoring Algorithm This section validates the proposed approach through numerical simulations. In order to train the neural network, the dataset with positive labels are manually created by the means Markov Chain. By means of the Markov Chain, plausible patterns are generated as the positive data to train the neural network. As for the negative dataset, the pattern is generated as a random sequence of driving modes where a sub-sequence is added that represent a known abnormal behaviour. The following datasets have been produced when applying the proposed algorithm: (i) NN-training set: 20,000 patterns, including 75% positive and 25% negative data, used to train the neural network; (ii) RD-training set: 20,000 patterns, including 75% positive and 25% negative data, used to query the trained network and create the RD dictionary.
Results and practical implications The mean individual responses for moral judgment by treatment are reported in Tables 2 (ethical actions) and 3 (unethical actions). Moreover, we present in Table  4  the results of a multiple hypotheses testing using the Stata MHTEXP module  (List et al. , 2019). Although the considered scenarios cover three distinct dimensions of (un)ethical practices and can be treated independently, we checked their reliability by computing the Cronbach's alpha. The coefficients (0. 7601 and 0. 8346 for ethical and unethical versions, respectively) can be considered as good. -  ------------------------------------------------------ Please, insert Tables 2, 3 and 4 around here  ------------------------------------------------------- Our findings indicate that NGO origin matters when onlookers judge the ethical and unethical actions of a given NGO.
Underwater images are used to explore and monitor ocean habitats, generating huge datasets with unusual data characteristics that preclude traditional data management strategies. Due to the lack of universally adopted data standards, image data collected from the marine environment are increasing in heterogeneity, preventing objective comparison. the extraction of actionable information thus remains challenging, particularly for researchers not directly involved with the image data collection. Standardized formats and procedures are needed to enable sustainable image analysis and processing tools, as are solutions for image publication in long-term repositories to ascertain reuse of data. the FaIR principles (Findable, accessible, Interoperable, Reusable) provide a framework for such data management goals.
Introduction Marine image datasets are critical for monitoring and managing the vast and remote ocean environments in the face of anthropogenic impacts  [1] [2] [3]. However, a comprehensible image of the global seafloor at a resolution of one millimeter per pixel resolution would require exabytes (millions of terabytes) of data storage  4. Such a single, static seafloor snapshot would ignore temporal aspects and not even touch on the largest habitat on Earth: the open ocean water column  5. Repeated imaging for monitoring and the ongoing technical evolution of cameras will increase dataset sizes by many orders of magnitude. Marine research institutions already acquire image data in the petabyte-range annually  6  , and unquantifiable volumes of industry and government data have to be counted in as well  7.
Such enormous dataset sizes present immense challenges for management. The processes of storage, management, linking, and interpretation is carried out by a growing number of national organizations with different financial backgrounds. Each one struggling to find sustainable software solutions for these issues individually, although a much higher degree of cooperation would be required facing the high volume and diversity of data. Imaging is often conducted to obtain occurrence, abundance, size and taxonomic information on benthic and pelagic fauna  8, 9. Planktonic images can be linked with allometric equations to estimate e. g. respiration rates of zooplankton  10  and to reconstruct vertical community structure in relation to hydrography  11. Additionally, particulate matter flux can be estimated from image data at unprecedented spatial and temporal resolution  12, 13.
A multitude of tools have been developed in this regard, for example to annotate images or detect important segments of video sequences  26, 27. Yet, no community standard exists to structure, share, or reuse such image-derived information. This gap has been repeatedly identified as part of scientific and environmental management initiatives, such as the UN Ocean Decade program Challenger 150 (https://challenger150. world), workshops of the UN International Seabed Authority, the Big Picture group in the UK or the international Marine Imaging Workshop community (https://www. marine-imaging. com). All three challenges -data volume, technical heterogeneity and missing semantic structure -can be mitigated. Data has the potential to be distributed between organizations via a federated infrastructure to distribute the storage burden, as well as to facilitate joint analyses approaches using also future science cloud infrastructure services.
Standards developed would systematically capture the technical heterogeneity and facilitate links between data sets. Software tools and procedures need to embrace those standards to facilitate efficient information extraction from large or multiple marine image data sets. All of these efforts can be addressed by applying the FAIR principles (Findable, Accessible, Interoperable, Reusable) to marine image data  28  , which represents the current main driver in research data management. In brief, the goal is to i) create visibility for all existing and upcoming data by making them Findable via data portals through unique identifiers; ii) enable efficient sharing of and Access to data by providing it through standardized interfaces; iii) facilitate links to connected resources for use with the data by providing Interoperable links usable by software tools; and iv) allow others to easily Reuse data by providing licensing information as well as using standardized data formats  28.
How to achieve FAIRness for different data types or data use communities is an ongoing research topic  20. Organizations such as the European Open Science Cloud (EOSC) or the Research Data Alliance (RDA) publish recommendations on FAIRness  [29] [30] [31]  , some of which are directly applicable to marine image data. These include human-and machine-readable standardized vocabularies or the use of persistent identifiers (PIDs). However, the massive data set sizes in marine imaging in monitoring and exploration create additional requirements for FAIRness such as the detachment of the image data from associated data (called metadata) to maintain efficient handling of data and facilitate accessibility and reuse. Finally, the inherent lack of semantic structure in image data, i. e. , that information in images is encoded in millions of pixels that only together create a picture by means of interpretation by a brain or computer, requires standards for tools that create and use information derived from images such as annotations.