dataesr/scientific-paragraphs-categorization huggingface dataset:
Substantial improvements in performance on the target datasets were observed despite only providing few tens of unlabeled micrographs. Even modality transfers from SEM to LOM could be facilitated successfully with such data. The UDA frameworks' insensitivity with respect to different phase fractions in source and target domains yields hope to enable generalization across different alloys and heat treatments. Considering the increasing image acquisition rates and automation, the discrepancy between labeled and unlabeled data quantities is very likely to dramatically increase in the future, raising interest for unsupervised-learning methods (e. g. UDA). Indeed, the availability and expense of annotation processes in material science are impediments, that UDA can evidently help to overcome. This applies especially when source domain data is substantially cheaper to annotate, shows relevant features for labeling precisely, and when domain gaps to bridge are narrow enough (comparable features between the domains).
Lastly, efforts have been made over the past years to infuse image classifiers with semantics and structured knowledge  41, 42  , to alleviate the restrictions with respect to data efficiency and generalizability across datasets. These models utilize knowledge modeling in the form of ontologies and derived knowledge graphs as well as graph neural networks to introduce reasoning capability to models. As materials science currently undergoes the digital transformation in numerous large scale projects  [43] [44] [45]  , resulting knowledge graphs will not only increase the findability of potential source training data but can, along with the techniques presented here, potentially shape the future of generalizable learning to account for materials diversity.
3. 5 keV X-ray line An unidentified X-ray emission line near 3. 5 keV has been detected by a number of groups using XMM-Newton, Chandra and Suzaku datasets  [560] [561] [562] [563] [564] [565] [566] [567]. The most popular explanation so far is in terms of a decaying keV-scale sterile neutrino dark matter  [568, 569]. The dark matter interpretation was ruled out with blank-sky observations  [570] , but these results have been disputed in Refs. [571, 572]. To add to this confusion, there exist various other astrophysical as well as non-astrophysical explanations for the line, and in particular, there are many atomic lines in this energy range, which could mimic the signal  [573]. Hopefully, the situation will be clarified with more precise data from future X-ray missions, such as Athena  [574].
The mixing angle θ is constrained to be very small due to various experimental datasets including the Higgs boson strength data  [711]. The new scalar boson h s originating from a hidden sector can decay back into SM particles via the mixing angle, which is suppressed by sin 2 θ. For example, the partial width into a pair of SM fermions is given by (h s → f f ) = N f sin 2 θ m 2 m h s 8π φ 2 1 - 4m 2 f m 2 h s 3/2 , ( 48 ) where N f is the colour of the fermion. The total decay width of h s can be obtained by summing over all kinematically allowed fermion pairs. For small enough θ and light h s the lifetime of h s can travel a macroscopic distance before decay, which may complicate its detection. To detect a new physical scalar through mixing with the Higgs boson at the LHC is more complicated than one would think.
All major software used in scientific computation is made available under a public licence (most commonly the GNU Public licence, but occasionally Creative Commons or similar licences may be used. ) Zenodo  [751]  is another sister repository, also maintained by CERN with the aim to provide citable DOIs to preserve research software and data products and in this sense is complementary to HEPData. The idea that data from publicly funded experiments might still remain behind a curtain is therefore highly unpalatable to most high-energy physicists. However, the question of what to release as data and how to ensure integrity in its future use remain questions that are under active and vigorous discussion.
Open data in particle physics As a first step towards open data, CERN launched the Open Data portal in November 2014  [752]. At the moment, this portal contains rich content involving collision and simulated datasets for research, derived datasets for education, configuration files and documentation, virtual machines and container images and software tools and analysis examples. As of March 2021, this data contains over 7600 bibliographic records and over 900k files or with 2. 4 petabytes of data. The availability of Open Data has also enabled novel theoretical research. There are currently over thirty papers citing the open data framework and multiple new studies in the realm of new searches, QCD jet studies, and Machine Learning  [753] [754] [755]  have been performed. At least one such work has then prompted further work by the publishing experiment  [756] , completing the experiment to theory and back to experiment cycle and providing evidence to the claim that Open Data would result in genuine scientific advance.
The FAIR principles This independent effort is the articulation of principles needed to publish and maintain the quality of data released. This is summarised by the FAIR  [757]  guiding principles for scientific data management, which emphasises that all data published should be Findable, Accessible, Interoperable, and Reusable. The CERN Open Data effort aligns with the FAIR principle by ensuring: • Findability: Assigning a "record ID" and optionally a DOI to every data product. Rich context description and associated documentation is provided. All histograms and numerical data are required to be machine readable. A search interface is provided for searching and identifying correct datasets. and reference proton-proton datasets (214 TB). We will discuss the CMS Open data format in detail in the coming sections, however, we outline general principles here.
CERN open data portal The CERN Open Data Portal 10  is the gateway to accessing the CMS open data. For CMS open data users, it provides access to the data themselves, the software needed access these data, and a number of examples and mini-tutorials on how to use these tools both for analysis and education. It also has an incredibly detailed amount of information about the provenance of these datasets, both collision data and Monte Carlo. In a sense, the "documentation" provided is complete in the same way a dictionary is complete. But while a dictionary will help you define any word you run across, it is perhaps not the best resource if you want to compose a sonnet in the style of Shakespeare. For a more concrete example, the CMS Open Data Guide will show a user how to access the data and explore the different types of "physics objects", but it does not give much explanation on these objects or why there might be multiple definitions of a "Muon".
As described in Sect. 4. 4, in a 2017 paper from Tripathee et al. [761] , the non-CMS-member authors used CMS open data to explore jet substructure. That paper contains an appendix, "Advice to the community", in which the authors detailed their experience with the open data, both good and bad, and provided advice for improving access for other users. While the authors were able to produce new scientific measurements with these data, it was not without challenges. The success of their group motivated the CMS open data team to provide a better experience for open data users by significantly improving the documentation. It should be pointed out that this did not mean getting rid of the information on the Portal. That information is complete and necessary for users who want the details of what triggers were used for collision datasets or the details of generator parameters for the Monte Carlo.
2. HEPData also provides an interactive interface to explore and download publication-level data behind plots and tables. So far HEPData hosted only results in the form of plots, however now it has started to also host likelihood information, which is a very welcome step towards creating a central place for repository of a publication level data. Along with HEPData, it also offers a bridge to GitHub and hence preserves older software releases. Zenodo is widely used by the machine learning community among other users at the LHC. Finally, some amount of internal or "restricted" analysis knowledge still needs to be preserved. This is also done in accordance of the FAIR principles, on this portal  [760]. It should be remembered that FAIR principles do not incorporate open access.
CMS data releases The CMS data releases take place regularly, after an embargo period of six years following data taking. The releases include 50% of the collision data and the corresponding simulated datasets. The collision data release is completed within 10 years. However, the amount of open data will be limited to 20% of data with the similar centre-of-mass energy and collision type while such data are still planned to be taken. CMS releases a full reprocessing of data at Level 3 (reconstructed data good for physics analysis) from each datataking period and the data are released in the same format and with the same data quality requirements from which analyses of the CMS collaboration start. For the Run 1 data (data taking 2010 -2012) the format is the Analysis Object Data (AOD) format, based on the ROOT framework  [766]  and processed through the CMS software CMSSW [767].
Final corrections and fine-tuning to the objects are often applied in the analysis phase. This happens because corrections and algorithms are developed at the same time as the first analysis of the data, and do not necessarily make their way to the final reprocessed data. Some of these corrections are available from the condition database, and some others as separate "recipes" to be applied to the objects. Another challenge are the triggers and procedures related to data taking. A single dataset consists of events passing one or more of hundreds of different trigger paths. In some cases triggers are prescaled so that only a predefined fraction of events passing the trigger selection is recorded, to enable collecting data for processes that occur often and otherwise would fill the entire data-taking bandwidth.
One event may also end up in two different datasets, and this eventual overlap needs to be accounted for in the analysis. All this needs to be handled in the data analysis to properly scale the number of selected events with the cross-sections being measured. The use of CMS data also requires some knowledge of the CMS experiment software CMSSW, built on top of the HEPspecific ROOT data structure. The software is openly available, and provided as software containers to open data users. The efforts for facilitating its use are discussed in Sect. 4. 3. 3. Part of these difficulties will be overcome when the slimmer miniAOD and nanoAOD formats will be made available. These formats are both based on the ROOT data structure, but the nanoAOD format does not require using CMSSW software, and will therefore be of particular interest to CMS open data users.
Examples and documentation At the time of publication of this document, users of the CMS Open Data have had three options for learning about how to access and analyse these data: the information provided on the CERN Open Data Portal, CMS Open Data Guide, and the (to-date) two workshops run by the CMS open data team. The Open Data Portal is the original source of documentation, but suffers from a lack of any sort of "roadmap" for new users, cf. Sect. 4. 2. 4. The CMS Open Data Guide is very much a workin-progress at the time of this document (Summer 2021). The workshops have been very successful and have provided person-power and feedback to improve the documentation, in addition to being a pinned source of information. CMS Open Data Guide: Within CMS, one of the most widely used onboarding tools is the CMS Offline Workbook, 11 a series of wiki pages that walk users through all the software requirements of becoming a CMS member.
From getting your computing accounts to how to start data analysis to generating your own Monte Carlo data. Much of it is public, but it also links to internal pages that require CMS membership to access. It also tends to be focused on the latest data releases, and not older datasets like are released on the open data portal, though the documentation is still there. The Workbook is laid out like a roadmap, walking the user from the first steps of finding and accessing data to more complicated procedures like jet energy corrections. It is a great resource for CMS users and the CMS open data team is currently working on an analogous site for the open data, to be referred to as the CMS Open Data Guide. It is very much a work-in-progress but the overall structure is there, and it mirrors the Workbook in that it tries to provide a starting point for a brand new user, eager to work with the open data.
CMS Open Data Workshops: The CMS open data group has conducted two workshops in 2020 and 2021: "CMS Open Data Workshop for Theorists at the LPC" 12 and "CMS Open Data Workshop", 13 which ran for 3 and 4 days, respectively. There were about 25 engaged participants in the first and about 50 in the second. Because of the pandemic, both were 11 https://twiki. cern. ch/twiki/bin/view/CMSPublic/WorkBook. 12 https://indico. cern. ch/event/882586/. 13 https://indico. cern. ch/event/1031398/. held virtually and attracted scientists from all over the world. These workshops built on the model of a hands-on workshop where participants were required to actually do the exercises and run the code themselves. The group opted to build the workshop lessons on a framework developed by the Software Carpentry 14  organisation, a framework also in use by other CERN workshops.
e. CMSSW in the case of CMS, and for the Run 1 data the AOD format. For a few events, this can be done on a local computer, but the data set required for an analysis including simulation samples typically consist of millions to billions of events. In order to be able to process these, one needs to have access to high-throughput computing resources. In addition, these need to provide the possibility to execute CMSSW. The use of software containers make this significantly easier as long as the underlying processor architecture is the same. Thus, CMS open data releases are accompanied by software container images that contain the respective CMSSW release needed for processing the data. Besides using these software images for Monte Carlo event generation, simulation and reconstruction, they can be used to extract desired event information and convert it into CMSSW-independent data formats as discussed in Sect.
4. 4. Furthermore, by extending the existing software, new algorithms can be developed and integrated. The updated software can be added to the container image and shared via container registries such as Docker Hub  [769]. There are currently no computing resources provided by the CMS Collaboration for the use of CMS open data by non-CMS members. These therefore need to be provided by the open data users themselves, e. g. at their institution or by using commercial public cloud computing services. To help open data users make use of public cloud compute resources, tutorials for "CMS open data in the cloud" have been conducted at the CMS open data workshops, cf. Sect. 4. 3. 3. Since individual data and simulation events are independent of each other, processing of the data sets can be parallelised.
A typical analysis workflow therefore splits or scatters the files belonging to a data set into smaller chunks, which are then merged or gathered after the processing step. The input data can hereby be streamed directly from the open data servers or first copied to local disks. Since public cloud computing services often provide the possibility to quickly scale up computing resources and then scale them down immediately after processing, this scatter-gather step can be sped up significantly. These steps can be implemented as a simple workflow, for example using Argo Workflows  [770] , for which examples are provided at the CMS open data workshops. After this first processing step, the results can usually be analysed further on smaller computing clusters or even local computers. The use of software containers has also been instrumental for the development and validation of CMS open data workflows in general since they provide a possibility to use CMSSW independently of the standard high-energy physics computing environments as well as in continuous integration systems.
Industry standard file format The second recommendation is to release data in an industry standard file format. The current pipeline involves running the CMS software framework on a virtual machine, which can become unwieldy for analyses that require a large number of events or need to run in a cluster/cloud environment. The authors of Ref. [777]  translated a subset of the information from select AOD files into the standard HDF5 format  [784]  and posted them on Zenodo  [785] [786] [787] [788] [789] [790] [791] [792] [793]  along with example code  [794] [795] [796]. This format allows the use of external analysis software, and it provides a benchmark data sample for the jet physics community. This issue may eventually be resolved when CMS releases data in the nanoAOD format.
Two main efforts in this regard are the publication of FAIR principles for scientific data and the CERN Open Data project. All CERN experiments also encourage that published analyses are accompanied by digitised tables and plots uploaded to the HepData repository. The implementation of this last requirement is still patchy, however compliance is improving. The premise and usage of open data is multi-fold, therefore data releases correspond to four different levels ranging from publication-level results up to raw collision data. The two intermediate levels, one that allows for education and outreach, and one that enables real data preservation and re-analysis, are the levels that require the most thought and sophistication in implementation. It is not enough just to have open data available, it is important that it is findable, accessible, interoperable and reusable.
Future improvements Despite advances of open data in particle physics, its usage remains challenging for any person who does not have training in the vocabulary used by the experimental collaboration. The data format, the methods for selecting and downloading datasets and the preprocessing needed are quite opaque and require extensive documentation to be made usable. Publication of step-by-step validation of analyses and using industrystandard file formats (as opposed to home-grown ones) are two suggestions we find from current non-CMS users of CMS Open Data. The availability of simulated datasets (not just collision data) would also help in disambiguation of complicated processes. It is also worth noting that apart from CMS, data from no other experimental collaboration is currently at a level that can be used for physics studies.
Summary The field of particle physics is at the crossroads. The discovery of a Higgs-like boson, a major accomplishment for the field almost fifty years in the making, completes the predictions for fundamental particles and interactions in the SM. At present no clear guidance is available as to how it will break down. On the other hand, the motivation for the existence of New Physics, given by, for example, the established existence of Dark Matter and Neutrino Oscillations, has not diminished since the discovery of the Higgs-like boson. The immense and far-reaching scope of the LHC physics programme promises to ultimately unveil the New Physics. The exploration of the phase-space by the LHC experiments is far from exhausted even with the available data sets, where Run 3 and the HL-LHC promise to deliver up to 4 ab -1 of integrated luminosity.
Unfortunately, the exploration of the LHC data by way of inclusive or model dependent searches performed to date indicates that no striking resonances have been observed in the accessible dynamic range. In this paper, we posit that New Physics is accessible at the LHC in principle, but that it is inaccessible by current analysis strategies for a variety of reasons, and that its signatures are hidden in the data. We summarise the status of the most significant anomalous experimental results in particle physics, including the most recent results for the flavour anomalies, the multilepton anomalies at the LHC, the Higgs-like excess at around 96 GeV, and anomalies in neutrino physics, astrophysics, cosmology, and ultra-high energy cosmic rays. The anomalies corroborate the need for extensions of the SM, and we pro-vide overviews over possible BSM models.
Another very promising avenue is presented by CERN's open data policy, which provides a testing ground for new search strategies with a quick turnaround. We discussed the challenges for open data in particle physics, including preservation and access of data and software, documentation, and validation mechanisms. The CERN open data policy and the independent FAIR principles are meant to ascertain that open data will be useful to the community. A specific example of open data is given by the CMS collaboration, which releases reconstructed data after six years to be used widely for analyses and is so far the only collaboration sharing its data in this way. CMS open data can be accessed via the CERN Open Data Portal, the CMS Open Data Guide, and two workshops run by the CMS open data team. We summarised CMS users' and external users' recommendations on data complexity and gave an overview over the challenges and measures taken by the CMS open data team to address them.
We conclude that wide access to open data by individuals is necessary to fully exploit the potential of the LHC and that, despite the advances in CERN open data, its public usage remains challenging for individuals. Improvements of data formats, the documentation, and availability of computing resources, are required to enable the community. We find that individuals using public data for their own research does not imply competition with experimental efforts, but rather provides unique opportunities to give guidance for further NP searches by the collaborations. The communication between theorists and experimentalists is paramount, possibly now more than ever. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Acknowledgements We thank  S. Kraml  for useful comments. SK is supported by the  Austrian Science Fund Elise-Richter  grant project number  V592-N27. ND acknowledges the support of  Department of Science and Technology of the Government of India  via the  Ramanujan Fellowship   SB/S2/RJN-070/2018. BB is supported by the  ERC  research grant  NEO-NAT no. 669668. ZB is supported in part by the  MIUR  grant  PRIN 2017X7X85K  and in part by the  SRNSF  grant  DI-18-335. TH is supported in part by the  U. S. Department of Energy  under grant No. DE-FG02-95ER40896. KC is supported in part by  Taiwan Ministry of Sciences and Technology  with grant number  MoST-110-2112-M-007-017-MY3. JT is supported by the  National Science Foundation  under Cooperative Agreement PHY-2019786 (The  NSF AI Institute for Artificial Intelligence and Fundamental Interactions , http://iaifi.