dataesr/scientific-paragraphs-categorization huggingface dataset:
They were grouped into six batches on the basis of the timing of their acquisition. The full-text PDFs for each batch were placed in the team's Google Drive folder, and the MS Excel list of titles and abstracts in each batch was further machine-processed to assist in identifying the perishable and durable crop studies to help divide the articles between the screening members of the team. After reading each assigned article, the screening team members recorded their decisions in their MS Excel sheet. For the excluded studies, the reason for exclusion was recorded. This information was later entered into Covidence to produce the summary data on inclusion rates and exclusion reasons (Extended Data Fig. 1 ). If there was uncertainty regarding the inclusion of a study, it was checked and discussed with at least one other member of the team.
Fleiss' kappa score was used to measure the level of agreement between screeners and gave a score of 0. 659 for the three main screeners, who screened 83% of the 1,788 available full-text articles, indicating that the level of agreement was substantial (0. 61-0. 80)  74. The coding framework was developed and trialled by the researchers using four of the benchmark studies, followed by discussions and amendments before finalizing and registering the protocol on the Open Science Framework at https:// osf. io/6zc92/ (Supplementary Table  2 ). An interactive SQL database and web app were built on the basis of the coding framework for entry of the relevant data extracted from each of the studies. This database is available for policymakers to explore the results. Due to uncertainty regarding how many of the 12,786 records from the initial search would be included, a two-part full-text coding approach was initially planned.
However, due to the time it took to obtain PDF copies of the 1,887 full texts and the short time frame available for the evidence synthesis, it was not feasible to wait for Part I screening of all the included full-text articles to be done to randomly select a sample of the included studies from across the different postharvest stages, crops and geographies. The Part II data extraction would have been conducted from this sample. After the full texts of batches 1 and 2 were screened, the inclusion rate was around 20%, so the team decided to do the Part I and II data extraction on all the included full-text studies after the screening of each batch. The relevant data were extracted into the database using drop-down menus based on the coding framework categories. The database structure was finalized in October 2019.
In hindsight, the database should have included more options for the quality loss data, as more than one set of measurements was often available (for example, percentage damage, percentage decay, percentage sprouting, percentage germination and aflatoxin content). Each included study was coded by one reviewer for Part I and II data extraction, and any uncertainties were discussed. Three of the researchers extracted the data for 88% of the studies. For the 40 studies where data were extracted by other team members, one of the three main data-extracting researchers then went through them in the database to check and standardize data capture across the 334 included studies. In addition to its bibliographic information, the researchers extracted data for each article using a two-part coding framework (Supplementary Table  2 ).
Part I data comprised the following: geographic locations (country, region and village), focal crops, crop form (fresh, dried, shelled or on the cob), focal postharvest activities (harvesting, handling, field drying, transport to homestead, curing, cooling, further drying, threshing/shelling, milling, packing, storing dry, storing fresh, transport to market and wholesale market), targeted postharvest actors (small-scale producers/point of production; packers and processors; service providers of harvesting, drying, milling, storage and transport; and traders, middlemen or collectors), type of study (field or on-farm trial, on-station trial or survey), study method (quantitative, qualitative, survey or mixed), study design (comparison with traditional practices, other types of intervention, non-adopters or pre-and post-adoption) and funding source.
Tier 4 was included for reference but not used in the data synthesis. In Part II, the following were captured: the PHL measurements of quantity or quality; facilitators and barriers for adoption; study design, duration and scale; intervention cost; and any assessment of any social, economic or environmental outcomes associated with the interventions. Thanks to programming expertise within the team, machine-scraped sections of the PDF files were placed in special fields in the database to support faster data extraction. However, the research team found that for accuracy and comprehension, validation still required reading of the paper and manual extraction of the required content. Moreover, the challenge in this body of literature has been that authors often use terms interchangeably, making codifying context difficult.
In addition, given that this exercise in assessment is still new, building the knowledge base to train algorithms to function more accurately is a process that takes years. Given the complex and nuanced understanding required, we do not yet have machines that can sufficiently and accurately identify or automatically extract the relevant information from complicated postharvest research studies. But if leveraged correctly, this dataset can be used to build a more effective algorithm. Data synthesis. The captured data were downloaded into MS Excel and synthesized using pivot tables. The meta-analysis was conducted at both the study level and the intervention level. A few studies covered multiple crops, multiple countries or multiple postharvest activity stages. Each study reported on at least 2 and as many as 24 interventions.
The wide range in treatment temperatures used in the studies (from <5 °C to >38 °C) made standardization by storage time for perishable crops inappropriate, even for ambient conditions. Multiple comparisons of the mean quantity and quality loss data for storage interventions of the durable crops were conducted. The Least Significant Difference test function in the R agricolae package  75, 76  was applied to the output of a one-way analysis of variance using a Holm-corrected least significant difference method to generate groups of means that do not differ significantly at P < 0. 05. There were insufficient data on perishable crop interventions for further analysis. The searchable SQL database was created to facilitate interactive data visualization, given the numerous dimensions of the challenge and the scope of the interventions.
Acknowledgements This systematic analysis of the evidence-base on PHL reduction interventions was conducted without funding. The research team contributed their time to work together virtually on this study outside their main work roles and responsibilities. The  Ceres2030  project (funded through the  Bill and Melinda Gates Foundation ) identified the need for the synthesis of the evidence on PHL reduction interventions, and we thank  J. Porciello  of  Cornell University  for supporting the research team with advice. The  Ceres2030  project covered travel and accommodation costs for six of the team members to join an inception meeting in June 2019. We thank the PHL reduction community of researchers and the stakeholders they have worked with for their study of different interventions, which provided the content for this analysis.
These precedents suggest that changing user demographics and preferences, and competition from other social networks such as Snapchat or a new platform (discussed further below) could be key drivers of Facebook's decline. However, given Facebook's pre-eminence as the world's largest social networking platform, the ethical, legal and social repercussions of its closure would have far graver consequences than these precedents. Rather, the demise of a global online communication platform such as Facebook could have catastrophic social and economic consequences for innumerable communities that rely on the platform on a daily basis  (Kovach, 2018) , as well as the users whose personal data Facebook collects and stores. Despite the high stakes involved in Facebook's demise, there is little research or public discourse addressing the legal and ethical consequences of such a scenario.
In particular, to the extent that most sectors in today's economy are already, or will soon become, data-driven and data-rich, these considerations, many of which relate to the handling of Facebook's user data, are ultimately relevant to the failure or closure of any company handling large volumes of personal data. Likewise, as human interaction becomes increasingly mediated by social networks and Big Tech platforms, the legal and ethical considerations that we address are also relevant to the potential demise of other social networks, such as Google or Twitter. However, focusing on the demise of Facebook -one of the most data rich, social networks in today's economy -offers a fertile case study for the analysis of these critical legal and ethical questions.
In particular, tighter data privacy regulation in various jurisdictions (notably, the EU General Data Protection Regulation [GDPR]4 and the California Consumer Privacy Act [CCPA])5 could severely inhibit the company's ability to collect and analyse user data. This in turn could significantly reduce the value of the Facebook platform to advertisers, who are drawn to its granular, data-driven insights about user behaviour and thus higher ad-to-sales conversion rates through targeted advertising. In turn, this would undermine Facebook's existing business model, whereby advertising generates over 98. 5% of Facebook's revenue  (Facebook, 2018) , the vast majority of which on its main platform. More boldly, regulators in several countries are attempting to break up the company on antitrust grounds  (Facebook, 2020, p.
7 As we have seen in the context of both Facebook as well as precedents such as Friendster, due to reverse network effects, any such exodus of users and/or advertisers can occur suddenly and escalate rapidly  (Garcia et al. , 2013; Seki and Nakamura, 2016; Cannarella and Spechler, 2014). Collectively, these socio-technical and regulatory developments may force Facebook to shift its strategic priorities away from being a public networking platform (and monetising user data through advertising on the platform), to a company focused on private, ephemeral messaging, monetised through commerce and payment transactions. Indeed, recent statements from Facebook point in this direction: I believe the future of communication will increasingly shift to private, encrypted services where people can be confident what they say to each other stays secure and their messages and content won't stick around forever.
83) , without which we risk falling into a 'digital dark age'  (Kuny, 1998; Smit et al. , 2011)  -a state of ignorance of our digital past. The concentration of digital cultural heritage in a single (privately controlled and corporate) platform is in and of itself problematic, especially in view of the risk of Facebook monopolising private and collective history  (Öhman and Watson, 2019). These socio-political concerns are magnified in the context of the platform's demise. For such a scenario poses a threat not only to the control or appraisal of digital cultural heritage, but also to its very existence -by decompartmentalising the archive, thus destroying its global significance, and/or by destroying it entirely due to lack of commercial or other interest in preserving it. These risks are most acute in an insolvency scenario, where, as discussed above, the data are more likely to be deleted or sold to third parties, including by being split up among a number of different data controllers.
Although such an outcome may be viewed as a positive development in terms of decentralising Facebook's power  (Öhman and Watson, 2019) , it also risks dividing and therefore diluting the global heritage and cosmopolitan value held within the platform. Worse still would be a scenario in which cosmopolitan value is destroyed due to a lack of, or divergent, commercial interests in purchasing Facebook's data archives, or indeed the inability to put a price on these data due to the absence of agreed upon accounting rules over a company's (big) data assets (Lyford-Smith, 2017). The recent auction of Cambridge Analytica's assets in administration, where the highest bid received for the company's business and intellectual property rights (assumed to include the personal data of Facebook users) was a mere £1, is a sobering illustration of these challenges.
23 However, our concerns are not limited to an insolvency scenario. In the more plausible scenario of Facebook closing the shutters on one of its products, such as the main platform website and app, the archive assembled by the product would no longer be accessible as such to either the public or future generations, even though the data and insights would likely continue to exist and be utilised within the Facebook Inc. group of companies (inter alia, to provide insights on users of other products such as Instagram and Messenger).
STRENGTHEN THE LEGAL MECHANISMS FOR USERS TO CONTROL THEIR OWN DATA IN CASES OF PLATFORM INSOLVENCY OR CLOSURE. Existing data protection laws are insufficient to protect Facebook users from the ethical harms that could arise from the handling of their data in the event of the platform's closure. As we have highlighted, the nature of 'Big Data' is such that even if users object to the deletion or sale of their data, and request their return, Facebook would be unable as a practical matter to fully satisfy that request. As a result, users face ethical harm where their data is used against their will, in ways that could undermine their privacy, dignity and self-identity. This calls for new data protection mechanisms that give Facebook users better control over their data. Potential solutions include creating new regulatory obligations for data controllers to segregate user data, in particular as between different Facebook subsidiaries (e.
g. , the main platform and Instagram), where data are currently commingled. 26 This would allow users to more effectively retrieve their data were Facebook to shut down and could offer a more effective way of protecting the interests of ad hoc 'algorithmic' groups  (Mittelstadt, 2017). However, to the extent that segregating data in this way undermines the economies of scale that facilitate Big Data analysis, it could have the unintended effect of reducing the benefits that users gain from the Facebook platform, inter alia through personalised recommendations. Additionally, or alternatively, further consideration should be given to the concept of 'data trusts', as a bottom-up form of data governance and control by users  (Delacroix & Lawrence, 2019). Under a data trust structure, Facebook would act as a trustee for user data, holding them on trust for the user(s) -as the settlor(s) and beneficiary(ies) of the trust -and managing and sharing the data in accordance with their instructions.
STRENGTHEN LEGAL PROTECTION FOR THE DATA AND PRIVACY OF DECEASED USERS. Although the interests of non-users as a group need to be given serious consideration, we highlight the privacy of deceased users as an area in particular need of protection. We recommend that more countries follow the lead of Denmark in implementing legislation that, at least to some degree, protects the profiles of deceased users from being arbitrarily sold, mined and disseminated in the case of Facebook's closure. 28 Such legislation could follow several different models. Perhaps the most intuitive option is to simply enshrine the privacy rights of deceased users in data protection law, such as (in the EU) the GDPR. This can either be designed as a personal (but time-limited) right (as in Denmark), or a right bestowed upon next of kin (as in Spain and Italy).
As such, developing more robust legal protection for the data privacy rights of the deceased is important for mitigating the ethical harms due to corporate demise, broadly defined. However, for obvious reasons, deceased data subjects have little political influence, and are thus unlikely to become a top priority to policy makers. Moreover, any legislative measures to protect their privacy are likely to be adopted at national or regional levels first, although the problem inevitably remains global in nature. A satisfactory legislative response may therefore take significant time and political effort to develop. Facebook should therefore be encouraged to specify how they intend to handle deceased users' data upon closure in their terms of service, and in particular commit not to sell those data to a third party where this would not be in the best interests of said users.
CREATE STRONGER INCENTIVES FOR FACEBOOK TO SHARE INSIGHTS AND PRESERVE HISTORICALLY SIGNIFICANT DATA FOR FUTURE GENERATIONS. Future generations cannot directly safeguard their interests and thus it is incumbent on us to do so. Given the societal, historical and cultural interest in preserving, or at least averting the complete destruction of Facebook's cultural heritage, stronger incentives need to be created for Facebook to take responsibility and begin acknowledging the global historical value of its data archives. A promising strategy would be to protect A digital version of this label may similarly entail acknowledgement by data controllers of, and a pledge to preserve, the cosmopolitan value of their data archive, while allowing them to continue using the archive. However, in contrast to physical sites and material artefacts, which fall under the control of sovereign states, the most significant digital artefacts in today's world are under the control of Big Tech companies, like Facebook.
As such, there is reason to consider a new international agreement between corporate entities, in which they pledge to protect and conserve the global cultural heritage on their platforms. 30 However, bestowing the label of global digital heritage does not resolve the question of access to this heritage. Unlike Twitter, which in 2010 attempted to donate its entire archive to the Library of Congress,31 Facebook's archive arguably contains more sensitive, personal information about its users. Moreover, these data offer the company more of a competitive advantage compared to Twitter (the latter's user accounts are public, in contrast to Facebook, where many of the profiles are visible only to friends of the user). These considerations could reduce Facebook's readiness to grant public access to its archives.
Nevertheless, safeguarding the existence of Facebook's records and its historical significance remains an important first step in making it accessible to future generations. It goes without saying that the interests of future generations will at times conflict with the interests of the other three ethical stakeholders we have identified. As  Mazzone (2012 Mazzone ( , p. 1660) )  points out, 'the societal interest in preserving postings to social networking sites for future historical study can be in tension with the privacy interests of individual users. ' Indeed, Facebook's data are proprietary, and any interventions must respect its rights in the data as well as the privacy rights of users. Yet, the mere fact that there are conflicts of interests and complexities does not mean that the interests of future generations ought to be neglected altogether.
CONCLUSION For the foreseeable future, Facebook's demise remains a high risk, low probability event. However, mapping out the legal and ethical landscape for such an eventuality, as we have done in this article, allows society to better manage the fallout should this scenario materialise. Moreover, our analysis helps to shed light on lower risk but higher probability scenarios. Companies regularly fail and disappear -increasingly taking with them troves of customer-user data that receive only limited protection and attention under existing law. The legal and ethical harms that we have identified in this article, many of which flow from the use of data following Facebook's closure, are thus equally relevant to the closure of other companies, albeit on a smaller scale. Regardless of which data-rich company is the next to go, we must make sure that an adequate governance framework is in place to minimise the systemic and individual damage.
Our hope is that this article will help kickstart a debate and further research on these important issues. What   (Furman, 2019; CMA, 2020). The measures proposed under this regime -such as facilitating interoperability between social media platforms-would also help to mitigate the potential harms to Facebook's ethical stakeholders due to its closure. L 194, 19. 7. 2016. 26. Facebook has stated that financial data collected by Calibra/Novi, the digital wallet for Libra cryptocurrency, will not be shared with Facebook or third parties without user consent  (Facebook 2019b). The segregation of user data is the subject of a ruling by the German Competition Authority, however this was overturned on appeal by Facebook (and is now being appealed by the competition authority -the original decision is here: https://www.
ACKNOWLEDGEMENTS We are deeply grateful to  Luciano Floridi ,  David Watson ,  Josh Cowls ,  Robert Gorwa ,  Tim R Samples , and  Horst Eidenmüller  for valuable feedback and input. We would also like to add a special thanks to reviewers  James Meese  and  Steph Hill , and editors  Frédéric Dubois  and  Kris Erickson  for encouraging us to further improve this manuscript.
5a-c ), indicating that non-shivering thermogenesis, Fig. 1  BMAT is transcriptionally distinct from white, brown and beige adipose tissues. a-d Transcriptional profiling of gonadal WAT, inguinal WAT and whole BMAT isolated from the proximal tibia (pBMAT), distal tibia (dBMAT) or radius and ulna (ruBMAT) of two cohorts of rabbits. a Principal component analysis of both cohorts. b-d Volcano plots (b), GSEA (c) and heatmaps (d) of transcripts differentially expressed between BMAT (dBMAT + ruBMAT) and WAT (iWAT + gWAT) in rabbit cohort 1. In b-e, red text indicates differentially expressed transcripts (b) or transcripts/pathways relating to glucose metabolism and/or insulin responsiveness (c-e); ns not significant. e Transcriptional profiling of adipocytes isolated from femoral BM or subcutaneous WAT of humans.
Single-strand cDNA was then purified and fragmented with a mixture of uracil DNA glycosylase and apurinic/apirimidinic endonuclease 1 (Affymetrix) in correspondence with incorporated dUTP. DNA fragments were terminally labelled with biotin by terminal deoxynucleotidyl transferase (Affymetrix). The biotinylated DNA was then hybridised to Human GeneChip® HTA 2. 0 Arrays (Affymetrix), containing more than 285,000 full-length transcripts covering 44,700 coding genes and 22,800 non-coding genes selected from the Homo sapiens genome databases RefSeq, ENSEMBL, and GenBank. Chips were washed and scanned on the Affymetrix Complete GeneChip® Instrument System, generating digitised image data (DAT) files. The DAT files were analysed with the Affymetrix Expression Console software. The full dataset was normalised using the Robust Multi-alignment Algorithm (RMA).
As a QC measure, the distribution of probe intensities and the 5′ to 3′ degradation profiles were checked to be consistent across samples. The core's statistician used RMA, from the package of Bioconductor (v2. 11), to fit log 2 expression values to the data  55. Weighted, paired, linear models were then fit and contrast computed using the limma package  56. Weighting was done using a gene-by-gene algorithm designed to down-weight chips that were deemed less reproducible  57. Probe-sets with a variance over all samples less than 0. 05 were filtered out. Of the remainder, probe-sets with a log 2 -fold change of 2 or greater and an adjusted P value of 0. 05 or less were retained. P values were adjusted for multiple comparisons using the Benjamini and Hochberg false discovery rate approach  54.