dataesr/scientific-paragraphs-categorization huggingface dataset:
A Bayesian framework To perform the optimisations, we used ORCHIDAS, the ORCHIDEE data assimilation system. ORCHIDAS is a variational DA system in which all observations within the assimilation time window are included in the optimisation. It uses a Bayesian statistical formalism  Tarantola (2005)  where errors associated with the parameters, the observations, and the model outputs are https://doi. org/10. 5194/egusphere-2022-745 Preprint. Discussion started: 22 August 2022 c Author(s) 2022. CC BY 4. 0 License. assumed to follow Gaussian distributions. The optimal parameter set corresponds to the minimum of a cost function, J(x): J(x) = 1 2 (y -M (x)) T R -1 (y -M (x)) + (x -x b ) T B -1 (x -x b ) (4) where J(x) measures the mismatch between (i) the observations y and the corresponding model outputs M (x) (where M is the model operator), and (ii) the a priori (x b ) and optimised parameters (x).
Defining edges To identify the edges of the GrIS, we exploited the fact that the edges are steeper than the middle of the ice sheet. To calculate the slope of a given pixel, we used the NOAA (National Oceanic and Atmospheric Administration) National Geophysical Data Center (NGDC) -ETOPO2 product  (NOAA, 2006) , which is based on a 2 arc-minute global relief model of Earth's surface and integrates land topography and ocean bathymetry. This product is already integrated into the ORCHIDEE, where it is used to determine the fraction of runoff that pools in flat areas  (Ducharne, 2016; d'Orgeval et al. , 2008). In a default ORCHIDEE simulation, when the slope is greater than 0. 5%, all precipitation over that pixel will run off immediately -it is too steep for precipitation to infiltrate the soil.
Furthermore, with this method, it is not possible to distinguish nonlinearity from interactions. It is also very dependent on the range of variations assigned to the parameters. Nevertheless, the Morris approach can still help give a broad overview of the most influential parameters and the model outputs they impact. Therefore, in addition to considering further structural changes, it will be necessary to further optimise the model against a range of datasets. With the ever-growing quantity of satellite datasets available, there are many different avenues we could consider. For example, we could use data from the GRACE (Gravity Recovery and Climate Experiment) satellite mission to constrain SMB  (Sasgen et al. , 2020). To constrain ice velocity, we could use products based on Sentinel-1 retrievals  (Mouginot et al.
Code availability. The ORCHIDEE vAR6 model code and documentation are publicly available via the ORCHIDEE wiki page (http: //forge. ipsl. jussieu. fr/orchidee/browser/) under the CeCILL license (http://www. cecill. info/index. en. html, CeCILL, 2020). The associated ORCHIEE documentation can be found at https://forge. ipsl. jussieu. fr/orchidee/wiki/Documentation. The ORCHIDEE model code is written in Fortran90 and is maintained and developed under an SVN version control system at the Institute Pierre Simon Laplace (IPSL) in France. The ORCHIDAS data assimilation scheme (in Python) is available through a dedicated web site (https://orchidas. lsce. ipsl. fr).
Like many web platforms, Facebook is under pressure to regulate misinformation. According to the company, users that repeatedly share misinformation ('repeat offenders') will have their distribution reduced, but little is known about the implementation or the impacts of this measure. The first contribution of this paper is to offer a methodology to investigate the implementation and consequences of this measure, which relies on an analysis combining factchecking and engagement metrics data. Using a Science Feedback and a Social Science One (Condor) datasets, we identified a set of public accounts (groups and pages) that have shared misinformation repeatedly during the 2019-2020 period. We find that the engagement per post decreased significantly for Facebook pages after they shared two or more 'false news'.
The median decrease for pages identified with the Science Feedback dataset is -43%, while this value reaches -62% for pages identified using the Condor dataset. In a different approach, we identified a set of pages claiming to be under 'reduced distribution' for repeatedly sharing misinformation and having received a notification from Facebook. With this set of pages, we observed a median decrease of -25% in engagement per post averaged over 30 days after receiving the notification minus 30 days before. We show that this 'repeat offenders' penalty did not apply to Facebook groups. Instead, we discover that groups have been affected in a different way with a sudden drop in their average engagement per post that occurred around June 9, 2020. While this drop has cut the groups' engagement per post in about half, this decrease was compensated by the fact that these accounts have doubled their number of posts between early 2019 and summer 2020.
CrowdTangle exclusively tracks public content, and provides access to engagement metrics (such as the number of likes, shares and comments), but not to the reach (number of views) of content  (Shiffman, 2021). If Facebook decreases the visibility of posts from accounts sharing misinformation, we expect that their reach decreases. As less users see these posts, the engagement per post should also decrease. To investigate the effect of the reduce policy, we used the engagement per post as a proxy for the visibility of the 'repeat offender' accounts content. We first combined data from one of Facebook's fact-checking partners (Science Feedback) identifying URLs sharing misinformation and from CrowdTangle tracking engagement metrics of the Facebook accounts that repeatedly shared such misinformation.
Investigating the reduce policy on accounts repeatedly sharing misinformation (Science Feedback data) To investigate a possible impact of Facebook's policy against accounts that repeatedly share misinformation, we first identified such accounts using data from Science Feedback, one of Facebook's third-party fact-checking partners  (Vincent, 2019). Science Feedback is a fact-checking organization dedicated to verifying the credibility of science-related claims and articles. The organization tracks the most viral press articles or social media posts, invites scientists with domain expertise to evaluate their credibility and publishes explanatory articles for a general audience. It contributes to maintaining a database of URLs where the claims checked have been published or repeated that is available online at open.
Due to the API limitations (https://github. com/CrowdTangle/ API/wiki/Links), if a URL was shared in more than 1000 posts, we collected only the 1000 posts that received the highest number of interactions; this means that we miss some of the accounts that generate the least interactions and our results focus on the most prolific accounts. We focused on the accounts that spread misinformation the most often, choosing a threshold of 24 different false news links shared over the two years period. The corresponding 307 Facebook accounts (289 Facebook groups and 18 Facebook pages) are referred to as 'repeat offenders accounts'. The list of accounts is available on the paper's GitHub repository: https://github. com/medialab/webclim_ipm/blob/ master/data/section_1_sf/list_accounts_sf. csv. The repository also contains the code used to collect and analyze the data, and to plot the figures.
Investigating the reduce policy on accounts repeatedly sharing misinformation (Condor data) One limitation of the results described above is that we obtained the links labelled as 'False' from only one fact-checking organization (Science Feedback), while Facebook partners with over 80 fact-checking organizations  (Rosen, 2020). The accounts might have received strikes from other fact-checkers apart from Science Feedback, which would create longer or additional 'repeat offender' periods. The true 'repeat offender' periods could thus be different than the ones inferred, potentially changing the magnitude of the 'reduce' effect. In this section, we clear up this potential issue by replicating the above analysis using a dataset containing flags from other fact-checking organizations.
Methods We use data from the Social Science One organization  (King & Persily, 2020) , a consortium of research centers that builds partnerships between academia and private companies such as Facebook to share data and expertise. In June 2021, Social Science One released a new version of the Condor dataset  (Messing et al. , 2021) , which contains all the URLs shared on the platform by at least 100 Facebook users between January 1, 2017 and February 28, 2021, as well as their fact-checking metadata. From this list, we extracted the 6,811 URLs that were shared in 2019 or 2020, that were flagged as 'False', and whose country in which it was shared the most was either the USA, Canada, Great Britain or Australia. We then replicated the data processing described in the previous section. Using CrowdTangle, we collected all the posts that shared one of the false links and identified 706 Facebook accounts (671 Facebook groups and 35 Facebook pages) that shared at least 24 false links between January 1, 2019 and December 31, 2020.
Then we used CrowdTangle to collect all the posts published by those accounts in 2019 and 2020. The Condor dataset contains the date of the first fact-check article used to flag a URL. We could thus infer the 'repeat offender' periods for each account and conduct the same analysis as in the previous section. 2 A decrease in engagement on June 9, 2020 can be seen for the 'Australian Climate Sceptics Group' in Fig. 1  (the percentage change was -60% for this example).
H. Théro and E. M. Vincent Science Feedback being a third-party fact-checker working with Facebook, some of their URLs are also contained in the Condor dataset (see Supplementary Figure  S5 ). Thus a significant part of the 'repeat offender' groups and pages obtained from the Condor URLs are the same as the ones analyzed previously. As the point of this section's analysis is to test the replicability of the previous results, we chose to exclude the accounts that were analyzed in the previous section. This section hence presents the engagement metrics only for the 503 'novel' accounts, which represent 476 groups and 27 pages (see Supplementary Figure  S6 ). The list of accounts is available here: https://github. com/medialab/webclim_ipm/blob/master/data/section_2_condor/list_accounts_condor.
The values are still significantly different from zero (W = 42651, p-value = 3. 8 × 10 -5 ). When we consider groups and pages separately, the percentage changes for the 442 groups are significantly different from zero (W = 37889, p-value = 3. 8 × 10 -5 ) and the median is -27%, whereas the values for the 23 pages are not different from zero (W = 133, p-value = 0. 89), with a median of -2% (Fig. 7 ). When the engagement per post is plotted separately for groups and pages, we again observe a drop in engagement for groups only (see Supplementary Figure  S2 ). This analysis using a dataset of 'False' URLs flagged by several fact-checking organizations confirms the main findings of the previous section. Pages undergo a period of decrease in engagement following the publication of two false links, while groups have been affected by a sudden decrease in engagement for all their posts in June 2020.
To verify whether Facebook applied any restriction to these pages, we collected all the posts that these 94 pages have published between January 1, 2019 and December 31, 2020 from the CrowdTangle API using the '/posts' endpoint. The collection was run on January 11, 2021. We were only able to collect data from 83 of these pages, as 11 were deleted from the CrowdTangle database since our search in November 2020. This highlights an important issue when studying misinformation trends on Facebook: some data disappears as accounts are deleted or changed to 'private'. Among the 83 Facebook pages collected, two were already among the 18 pages included in the first analysis, and one was already present in the set of 35 pages included in the second analysis (see Supplementary Figure  S6 ). We excluded these pages to present only the 80 'novel' pages in this section.
The list of accounts is available here: https://github. com/medialab/webclim_ipm/blob/ master/data/section_3_self_declared/list_accounts_reduce. csv. The date of the last fact-check notification was used as the inferred start date of reduced distribution, when it appeared in the screenshot. When it was not visible, we used the date of the post as the inferred start date of reduced distribution. The inferred 'reduced distribution' dates range from April 1rst to November 23, 2020. We are aware that the inferred date may not correspond to the real date at which the restrictions has begun to be enforced. For example, a page may have received a 'reduced distribution' notification from Facebook in early 2020, while sharing a screenshot of this notification only a few days or weeks later. Because the 'reduced distribution' notification is a private message that cannot be accessed unless the page shares it publicly, we had no choice but to rely on this inferred date as a proxy for the start date of the restrictions.
Discussion Facebook, the most widely used social media platform in the world, has announced a series of measures to curb the spread of misinformation, notably by reducing the visibility of content shared by 'repeat offenders', which are accounts that repeatedly share false information. However, the effects of the platforms' diverse policies to tackle misinformation remains understudied  (Pasquetto et al. , 2020). The present research article aims to contribute to filling this knowledge gap by providing a method to verify the application and measure the consequences of Facebook's 'reduce' policy on the targeted accounts' engagement metrics. As a first step, we investigated 307 Facebook accounts (mainly groups) having repeatedly shared misinformation using a factchecker's dataset. Sharing two false links over a three-month period is supposed to be penalized by a reduced visibility of the account's content  (Solon, 2020).
We did observe a significant decrease (median of -43%) in the engagement per posts published by pages under a presumptive repeat offender status. However, we find that this policy is not leading to a significant decrease in engagement for Facebook groups. As a second step, we replicated this methodology using another dataset of URLs shared by Facebook, and identified 503 additional accounts that have shared misinformation repeatedly. We again observed a significant decrease (median of -62%) in engagement per post for 'repeat offender' pages, while the engagement for 'repeat offender' groups was not affected. As a third step, we identified 83 Facebook pages which have shared a Facebook notification, indicating that their account was under reduced distribution. The pages' engagement metrics were significantly lower after the date of the notification (median of -25%), suggesting that the 'reduced distribution' measure was indeed applied to the pages after they received a notification from Facebook.
We noted that no group was found when searching for accounts sharing a reduced distribution notification, which confirms that the 'repeat offender' policy is applied only to Facebook pages, and not to groups. The different methodologies we used to infer the repeat offender periods are subjected to biases. First, to perfectly identify the repeat offender periods for a given account, we should have knowledge of all the False URLs this account has shared. The two URL datasets we relied on have different limitations in this regard: the Condor dataset contains URLs from all the fact-checkers Facebook is working with, but only the URLs shared more than 100 times on its platform, while the Science Feedback dataset contains all URLs they flagged, regardless of sharing numbers, but only for this one fact-checking organization.
Acknowledgments We are very grateful to  Social Science One  and to Facebook for their partnership in making the Condor Facebook URL Shares dataset available to researchers including ourselves. We thank  Shaden Shabayek ,  Manon Berriche  and two anonymous reviewers for their insights on the manuscript. We also thank  Guillaume Plique ,  Benjamin Ooghe-Tabanou  and all the médialab technical team for their help with data collection. This research was supported by the  'Make Our Planet Great Again' French  state aid managed by the  Agence Nationale de la Recherche  under the ' Investissements d'avenir' program  with the reference  ANR-19-MPGA-0005.
Challenges from Practitioners Another perspective used to recover IoT challenges was the practitioners' opinion. From the characterization obtained with the SLR, we had the opportunity to hear people from industry and academia who are interested or already work with IoT. The intent of capturing the information from this source was to increase our observation dataset and triangulate the challenges found in the SLR with the ones reported by practice. With this new vision, we deal with other relevant aspects and put the research closer to the people working in the area. We performed qualitative studies during two scientific events from which all the participants were working on the IoT domain. Therefore, we considered the participants representative, insightful, and experienced in the topic. We organized the discussions at the events inspired by a focus group  (Kontio, Bragge, & Lehtola, 2008 ) process and experiences from previous studies.
Execution We used Scopus 11  as the search engine to index several peer-reviewed databases and balance coverage and relevance  (Motta, de Oliveira, & Travassos, 2019). We incremented the search with snowballing procedures (backward and forward)  (Wohlin, 2014)  as a strategy to increase coverage. The selection process (see Table  9 ) began by removing articles that did not fit the inclusion criteria (reading the title, abstract, and fulltext reading). After that, snowballing was performed. This procedure was defined to eliminate articles that do not explicitly answer the questions. The execution and more detail on this procedure are available  (Motta, Oliveira, & Travassos, 2021). As presented in Table  9 , we started from 9132 papers (sum of the initial set of papers for each Facet), and we concluded with 170 papers for analysis.
Regarding the system features made available, the IoT Oximetry Software System consists of the following main modules: • Oximeter-IoT -IoT device for collecting and sending patient data. • Brokerresponsible for communication between Oximeter and Manager subsystems. This system uses the MQTT communication protocol. • Managerresponsible for managing Oximeter devices, for the association between such devices and patients being monitored in each context (infirmary or home), and for the persistence of data collected in a database. • Dashboardresponsible for displaying data collected by Oximeter devices. For this, the Dashboard obtains the data stored in the database according to related settings (e. g. , update frequency) defined in the Manager subsystem. 25. The allocation was based on the students' personal preferences and balanced by the professor among the teams.
Data must be available for access throughout the working hours of the facility. The data must be stored in a MySQL database hosted on a server made available to the project. " "A requirement that has not yet been listed is the need to have more than one device per room, as it can have more than one input/output. In addition, the security parameters of each room must be configurable, and the devices properly identified by their mac number and the room where it is located. " "The system must maintain its performance at least 80% of its total processing capacity if there is a high demand of users accessing the system at the same time. It has not been defined how this performance will be obtained. " "It has not been defined how to perform hardware maintenance, which parts to remove in which order.
Finally, according to pre-defined settings, a local microprocessor controls the plant wall system's watering, lighting, and ventilation. Other services are available in a web-based user interface to monitor an indoor climate in real-time, check historical data from a database, and update the schedules and settings. v. 61, p. 120-127, 2015. Using Geographic Information Systems (GIS) and sensors, the proposal measures the waste volume in trashcans or containers, capable of transmitting information to the Internet via a wireless link. It aims to optimize the management and strategies of waste collection logistics. The system is simulated in a realistic scenario in Copenhagen and provides a dynamic OnDemand collection based on waste level status. DE PAOLA, Alessandra et al. Smart buildings: an AmI system for energy efficiency.
This strategy enabled us to build the rst TF binding motifs database that incorporates contextspeci c position weight matrices coupled to epigenetic information and context-speci c TFs' function. Using this repository, we were able to further demonstrate that gene ontology (GO) enriched terms associated with transcription factor target genes may differ notably based on association with alternative cofactors and DNA methylation status. Conclusions: Overall, this update turns MethMotif into an integrative TF motif database with diverse regulatory element analysis tools accessible to a broad audience. Ultimately, this repository and its companion tools greatly facilitate the analysis of the consequences of epigenetic aberrations seen in diseases such as cancers.
, 2022; Lemma et al. , 2022). It is now established that DNA methylation is an epigenetic modi cation crucial for the recruitment and/or selection of transcription factors (TF) within the chromatin, ultimately resulting to profound alteration of transcriptomic pro les. To further decipher the relationships between DNA methylation and transcription regulation by TFs, we developed MethMotif (Xuan Lin et al. , 2019), a two-dimensional TF motif database that records transcription factor binding site (TFBS) and position weight matrices (PWM) completed with CpG methylation information, in a cell type-speci c manner. The integration of TFBS motifs with TFBS DNA methylation allows us to better describe the DNA loci features recognized by TFs. Of note, we have found that DNA methylation patterns within TFBS can be cell type-speci c, while, for a given TF, DNA methylation pro les can be binding motif speci c.
Since the publication of the rst version of MethMotif, we took advantage of user feedback to improve its functionalities, add novel features, and increase our dataset. More particularly we have: extended the number of PWM from 509 to over 700; included datasets for two additional species, Mouse (24) and Arabidopsis thaliana (23); developed additional online tools for batch querying and visualizing data. MethMotif 2023 has an expanded and unique feature set, simplifying the understanding of the contextspeci c role of TFs. Previously  (Lin et al. , 2020) , we showed that cofactors can modulate the target DNA binding sequence motif, target sequence methylation pro le and TF function. Hence, in addition to its main binding motif, the new release of MethMotif provides for each human TF: the TFBS motifs and DNA methylation pro les in the context of each TF's co-factor partners; the genomic locations of the TF of interest in the context of each TF partner; the gene ontology (GO) analyses of TF targets in the context of each TF partner.
Overall growth of database MethMotif 2023 currently integrate a large number of ChIP-seq and WGBS datasets from various sources, including ENCODE  (Davis et al. , 2018) , GEO  (Clough and Barrett, 2016) , and GTRD  (Yevshin et al. , 2019) , which further completed with in-house experiments, reaching a total of 702 methyl-PWMs for three taxonomic groups. Regarding Homo Sapiens, sixteen cell lines are covered, including ve new ones (H9, HEK293T, HUES64, LNCaP, SNU398), which enable us to better characterize cell-type speci c TFs' function (Table  1 ).