dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and code availability All data used in this study are freely available. The continuous seismic data and raw earthquake data were provided by the Iranian Seismological Center (IRSC, http://irsc. ut. ac. ir/) and the International Institute of Earthquake Engineering and Seismology (IIEES, http://epp. iiees. ac. ir/datarequest/) to the corresponding author. The processed data and codes that support the findings of this study are available from the corresponding author (https://doi. org/10. 5281/ zenodo. 8004726). The Fast-marching surface-wave tomography (FMST) package developed by Nick Rawlinson is available at http://iearth. edu. au/codes/FMST/ and we used http://rses. anu. edu. au/~nick/surftomo. html to obtain 2D Rayleigh-wave dispersion maps of the Iran plateau. The SOLA tomography code we use in this study consists in running the LSQR algorithm of  Paige and Saunders (1982)  with specific input matrix and vectors.
These inputs can be constructed from the (studydependent) sensitivity matrix and target kernels (denoted by G and T in this study) as detailed in the appendix A1 of  Zaroli (2016). The LSQR code  (Paige and Saunders, 1982)  is freely downloadable on the web page of the Systems Optimisation Laboratory (Stanford University): https://web. stanford. edu/group/SOL/software/ lsqr/. For those who prefer a pre-constructed software package for SOLA tomography, one is is available from Christophe Zaroli (c. zaroli@unistra. fr) upon e-mail request.
In very-high-energy (VHE) gamma-ray astronomy, the community is converging towards the use of a common open data format, called "Data formats for Gamma-ray Astronomy", for the high-level data products. This format is in use for ground-based TeV observatories like H. E. S. S, MAGIC or HAWC, some of whom plan to openly release high-level data products. These efforts are parallel to the development and use of open analysis software such as the Gammapy package. This open initiative has shown that it is possible to define common standards even without governance. With the advent of open VHE observatories (e. g. CTAO, KM3NeT) and an increase in both multi-wavelength and multi-messenger studies, such standards should evolve to support all of VHE multi-messenger astrophysics. For these reasons, a new initiative has been created to specify formats of high-level data from very and ultra high energy gamma-ray facilities and from VHE neutrino detectors.
The next generation of more sensitive instruments  [8, 9]  is currently in deployment. The improvement of sensitivities of VHE astrophysical facilities is accompanied by an increasing amount of public data (e. g. [10, 11] ) and open analysis libraries (e. g. [12, 13] ). The opening of research products (publications, data, software) is stimulated by national and transnational roadmaps for Open Science (e. g. [14, 15] ) that recommend or require free access. Some newly created VHE observatories follow these requirements and will openly grant access to data and analysis software. These policies will stimulate even more the multi-wavelength and multi-messenger analyses. As demonstrated in  [16]  with VHE gamma-ray data and in  [17]  using gamma-ray and neutrino data, both with the library Gammapy  [18] , joint analysis of multi-instrument data can be performed with a coherent statistical treatment while dealing with systematics of/between measurements.
Such multi-instrument analyses are becoming a reality thanks to the use of a common open FITS-based1  [19]  data format for the high level or science-ready data (see section 3) of the VHE gamma-ray instruments, called "Data formats for Gamma-ray Astronomy" (or Gamma-ray Open Data Format, GADF)2. The work of data formatting started back in 2010 by TeV experts from H. E. S. S. and CTA following the example of the Fermi-LAT and OGIP formats. And it leads to the first version of GADF in 2016  [20]. Today, the running gamma-ray experiments H. E. S. S. , MAGIC, VERITAS and HAWC are using this data format for some of their data sets. The use of this data format for testing purposes and for scientific publications has shown that its design describes efficiently the VHE gamma-ray data and permits accurate scientific analyses.
After few years of usage of this format, some main user and core contributors (GADF, CTAO, H. E. S. S. , etc) realise the need to evolve because the GADF open initiative started to show some limitations  [21] , both on the format requirements and its organisation. Enriched by the gained experience of the GADF initiative, their main contributors contacted the current and under-deployment VHE gamma-ray and neutrino facilities to establish a new open initiative to build together an extended open data format for the VHE astroparticle experiments, the Very-high-energy Open Data Format (VODF). Now officially supported by eleven VHE facilities, ASTRI, CTAO, FACT, Fermi-LAT, HAWC, H. E. S. S. , IceCube, KM3NeT, MAGIC, SWGO and VERITAS, this newly born initiative has started its activities. This contribution describes its objectives and some of its requirements (section 2), the targeted level of data that are considered to.
Guidelines of the open VODF initiative VODF is an open data model and format for VHE gamma-ray and neutrino astronomy. These experiments strongly differ by their detection techniques, their instrumentation and their type of raw data. Ground-based gamma-ray detectors fall into two categories, Imaging Atmospheric Cherenkov Telescopes (IACTs) and Water Cherenkov Detectors (WCDs), whereas neutrino detectors are using optical modules immersed in a km3-scale volume of ice or water. They all share the following high-level properties, after a low-level data processing consisting of calibration, reconstruction and background reduction: • they provide a list of gamma-ray or neutrino candidates characterised by their arrival time, their energy and their celestial arrival direction, • their instrumental response to events can be characterised by the same quantities and have the same type of observational dependencies, such that their associated Instrument Response Functions (IRFs) can be factorised in the same manner, • as these instruments study the same astrophysical processes, the scientific products are similar: sky maps, spectra, light curves, along with associated statistical information like significance or likelihood profiles.
In this context, the formats can be homogeneous and shared. This approach has been tested and has shown its success to generate scientific results and in addition to offer real interoperability between instruments  [16, 22]. The goal of the VODF initiative is to provide a standard set of data models and file formats, starting at the reconstructed event level (science-ready data) as well as higher-level products such as N-dimensional binned data cubes (including sky images, light curves, and spectra) and source catalogues. With these standards, common science tools can be used to analyse data from multiple high-energy instruments. In parallel, several facilities will publicly open their data (e. g. CTAO, KM3NeT) or their archive  [23]  and the use of certified repositories is strongly recommended.
As a consequence, the data should be correctly curated such that they respect as closely as possible the FAIR principles  [24]  (Findable, Accessible, Interoperable and Reusable). Metadata describing the associated data are mandatory. VODF aims to standardise the format of these metadata for each level of data. In the field of astronomy, the community started to think about common standards in order to share and use more efficiently data. Formed in 2002, the International Virtual Observatory Alliance (IVOA)3 has the mission to create and maintain standards to "facilitate the international coordination and collaboration necessary for the development and deployment of the tools, systems and organizational structures necessary to enable the international utilization of astronomical archives as an integrated and interoperating virtual observatory".
PoS(ICRC2023)1510 The Very-high-energy Open Data Format B. Khélifi they offer new possibilities to find and access data with the Virtual Observatory (VO) services (such as Aladin or Topcat). Technical tests have been realised to respect more closely the FAIR principles and to use some of the IVOA standards. The H. E. S. S. test data release  [10]  or the ANTARES data release  [11]  illustrate the new possibilities  [26, 27]  available to the new VHE observatories using standards set by astronomers. Given this context of state-of-art data formating, the open initiative VODF is built on the following guidelines: • building a new VHE open data format common to IACT, WCD and neutrino instruments, • being compliant with the FAIR principles, • following as much as possible the IVOA standards, • working by consensus with open contributions under the supervision of supporting experiments.
g. counts. Advanced Science Data. They consist on the astrophysical products derived from the Binned-Science Data (combining the binned events with the binned IRFs), such as spectral energy distributions, light curves, sky maps, and phasograms. These are in physical units such as fluxes, energy or angles. High-Level Catalog Data. They contain a collection of astrophysical objects or VHE sources with a description of their properties and associations, such as source components, upper-limits on size or flux, observation periods or science alerts. In addition to these formats, the VODF initiative aims to provide tools to check the compliance of a data set with a given format version. These open tools, which use Python and the communitystandard astropy library, could be used by observatories during their data release process and included in their Verification and Validation (V&V) plan.
Organisation of the VODF initiative THe VODF initiative was formed at the beginning of 2023 and is officially supported by eleven VHE experiments: ASTRI, CTAO, FACT, Fermi-LAT, HAWC, H. E. S. S. , IceCube, KM3NeT, MAGIC, SWGO and VERITAS (in alphabetical order). The data format used by the Fermi-LAT experiment was used as a base for the original GADF and has been settled since a long time. Thus, the participation of Fermi-LAT in this initiative will bring in valuable expertise and skills, as well as ensure that the VODF format can serve both ground-based and satellite experiments. With the official delegates of each experiment, a governance document has been written and it describes the functionning of the initiative. The latter possesses a simple and clear organisation: • a Steering Committee composed by one official delegate per experiment, • three Lead Editors, one per experimental technique (IACT, WCD, Neutrino), • and two Conveners.
Jupyter notebooks used to pre-process and polish raw data for data science and machine learning processes are challenging to analyze. Their data-centric code manipulates dataframes through call to library functions with complex semantics, and the properties to track over it vary widely depending on the verification task. This paper presents a novel abstract domain that simplifies writing analyses for such programs, by extracting a unique CFG from the notebook that contains all transformations applied to the data. Several properties can then be determined by analyzing such CFG, that is simpler than the original Python code. We present a first use case that exploits our analysis to infer the required shape of the dataframes manipulated by the notebook.
Introduction The ever-increasing usage of data-driven decision processes led to data science (DS) and machine learning (ML) permeating several areas of everyday life, reaching outside the boundaries of computer science and software engineering. Ensuring correctness of these processes is particularly important when they are employed in critical areas like medicine, public policy, or finance. In contrast to robustness verification of trained ML models  [9] , data pre-processing of the DS/ML has received little attention. As raw data is often inconsistent or incomplete, pre-processing programs, typically implemented in Jupyter notebooks, apply transformations to polish it to the point where it can be visualized or used for training. Errors and inconsistencies at this stage can silently propagate downwards in the DS/ML chain, leading to incorrect conclusions and below-par models  [1].
Verification of notebooks can take different directions, such as detecting data leakages (i. e. , sharing of information between the training and test datasets  [7] ) or warning about the introduction of bias or skews  [11]. Regardless, Jupyter notebooks are challenging to analyze: code comes in blocks that can be executed in any order with repetitions, and data is manipulated through calls to a vast number of library functions with complex and possibly overlapping semantics. This paper proposes an abstract interpretation approach to simplify the implementation of verification techniques for Jupyter notebooks containing DS/ML programs. We propose an abstract domain that tracks transformations made to dataframes, that is, the in-memory tables containing the input data, in a unique graph. The latter contains data transformations as nodes that are linked by edges encoding the order in which they are applied.
The final graph produced at the end of the analysis is a control flow graph (CFG) containing only dataframe transformations: analyses such as the ones mentioned above can be implemented as fixpoints over this CFG, instead of tackling the more complex Python code. This paper is structured as follows. Section 2 discusses related work. Section 3 introduces PyLiSA, the analyzer used to evaluate our domain, and LiSA, the framework it relies on. Section 4 defines the abstraction for dataframe values. We then explore a first use-case in Section 5, where we use our abstraction for inferring the shape of the dataframes used by a program. We then conclude with a preliminary experiment on a real DS notebook in Section 6.
Related Work Obtaining formal guarantees on the safety and fairness of ML models has been a subject of recent widespread interest  [9]. Our work builds upon this large ecosystem and proposes a verification framework for the data pre-processing stage of the ML pipeline. The closest body of work is  [7]  which also analyses DS notebooks along with their peculiar execution semantics and proposes an abstraction to detect data leakage. Our approach towards the shape inference of input data follows the work of  [8]  and extends it to support inputs to programs which contain datasets. Similarly, our objective of inferring input data usage directly derives from the compound data structure usage analysis presented in  [10]  and adds the ability to track the usage of selections of datasets. The objective of detecting bias/skew introduction is inspired from the mlinspect tool proposed in  [4].
This tool builds a directed acyclic graph (DAG) of operations (like filters or projections) applied to the data by analyzing the code and using framework-specific backends (like scikit-learn). After analyzing the DAG, it suggests potential sources of bias/skew. Although this is promising, it only places syntactic checks and cannot concretely detect which operations cause these problems. Lastly,  [5]  is an automated data provenance system for Python. However, it requires executing the code which is not always be feasible when large datasets are involved.
LiSA and PyLiSA LiSA  [3, 6]  (Library for Static Analysis) is a modular framework for developing static analyzers based on the abstract interpretation theory. LiSA analyzes CFGs whose statements do not have predefined semantics: instead, users of the framework define custom statement instances implementing language-specific semantic functions, enabling the analysis of a wide range of programming languages and the development of multilanguage analyses. The analysis infrastructure is partitioned into three main areas: call evaluation, memory modeling and value analysis. Each area corresponds to a separate analysis component, that operates agnostically w. r. t. how the others are implemented. At first, calls are abstracted by the Interprocedural Analysis, that leaves the remaining components with call-less programs.
The Dataframe Graph Domain We present an abstraction able to capture the structure of the dataframes manipulated in Python code. Intuitively, we employ a graph structure to keep track of all operations that involve dataframes, with edges encoding the order in which they are performed. The graph thus represents the state of each dataframe at a given program point. Nodes of this graph can be referenced by variables of the program. The latter thus refers to the dataframe corresponding to the sub-graph obtained with a backward DFS starting from the node. We adopt a two-level mapping: program variables point to labels, and the latter are mapped to the nodes. This enables simple handling of dataframe aliasing (i. e. , two variables will be mapped to the same label) and updates (i. e. , by changing the nodes pointed by a label, we indirectly update all variables pointing to the label).
A First Application: Shape Inference Tracking dataframe transformations through D # is just the beginning. In general, successive analyses targeting D # instead of starting from the Python code can still be formalized As a first experiment, we selected the "Coronavirus (COVID-19) Visualization & Prediction" 3 dataframe, one of the most popular notebooks aggregating data from different sources on Kaggle, a public repository of Jupyter notebooks for DS. The graph produced when analyzing such code is published on a GitHub Gist 4 as it is too large for this manuscript. Note that the implemented analysis supports additional pandas constructs w. r. t. the ones presented in this paper, that have been omitted as they do not contribute further to the intuition behind the domain. In the graph, these take the form of additional node kinds, whose intuitive meaning is explained in the Gist's introduction.
Conclusion This paper presents an abstract interpretation approach to analyze Python programs employed in data science and machine learning. Such programs manipulate dataframes, that is, complex in-memory tables collecting data that can be used to guide decision processes or train machine learning models. We designed an abstract domain that extracts the operations performed over dataframes, building a graph that encodes the order in which they are performed. Such a graph can be the subject of further analyses, inferring several properties such as the shape of the dataframes read by the program, or the absence of data leakages between training and testing phases of a machine learning process. As a guiding example of how to exploit our domain, we defined a simple abstract interpretation that computes, for each file read by the source program (and thus present inside the graph), the set of columns that are either accessed before being assigned, or defined through an assignment.
We provided an early implementation of both domains in PyLiSA, a LiSA frontend for Python programs. There are plenty of future directions that our work can take. As this work is still ongoing, the obvious first line of axis is to prove the soundness of the proposed semantics 3 https://www. kaggle. com/code/therealcyberlord/coronavirus-covid-19visualization-prediction, version 722. 4 https://gist. github. com/lucaneg/9621f3296b7b47b12c5ee1c52066b3d1. abstractions, followed by an investigation on their completeness to further improve the domain. Besides, inferring the shape of dataframes is not the only useful analysis one can employ for DS software. In fact, after strengthening shape inference to incorporate rows and cell properties, we aim at providing abstractions to detect data leakages and biases.
The spotted lanternfly is an invasive pest for which we lack individual movement data due in part to the difficulty posed by individual identification. We developed a computer-aided method to identify individual adult spotted lanternfly using wing spot patterns from photos processed in the software I3S and demonstrated the method's accuracy with lab and field validations. Based on 176 individuals in the lab, we showed that digitizing the spots of one wing allowed a 100% reliable individual identification. The errors due to user input and the variation in the angle of the image were largely negligible compared to inter-individual variations. We applied this method in the context of a mark-recapture experiment to assess the feasibility of this method in the field. We initially identified a total of 84 unique spotted lanternflies, 31 of which were recaptured after four hours along with 49 new individuals.
Lab validation 2. 1. 1 Aim The objective of the lab validation was to test, based on images of labeled SLF, whether there is enough inter-individual variation and minimal digitization error to make individual photo-identification possible in SLF, and automate individual identification using digitization scores. We used the program I3S Classic version 4. 0 (9) and partly followed the technique developed by Sacchi and collaborators on lizards  (16) , and reused for the development of photo-identification on other taxa  (11, 17). Specifically, we used the same method of comparing pairwise dissimilarity scores for two digitizations of the same image, digitization of two images of the same individual, and digitization of different individuals, but we modified the statistical analysis of the scores produced (see below).
Image processing All images were processed in I3S Classic version 4. 0 (20). This program allows the digitization of spot patterns of an animal within an area determined by three reference points that are used to align the images. Together the digitized spot patterns and reference points create a fingerprint file. I3S then compares pairs of fingerprints by superimposing reference points and calculating the distance between pairs of spots. It generates an index of dissimilarity that is the sum of the distance between each spot pair divided by the square of the number of spot pairs (20). As a result, pairs of SLF images with low dissimilarity scores have similar spot patterns. We limited digitization to the left wing of each individual rather than both wings, given that the number of spots on a single wing typically spans 12-30, a range recommended in I3S to optimize both the identification and the amount of time necessary to digitize images (20).
The top-down set of images was digitized and used as the reference database. The top-down images were then digitized a second time by another user and matched against the reference database to measure the reproducibility of digitization by two different users using the "batch compare" feature of I3S. "Batch compare" computes scores for all pairwise combinations between the tested images and the reference database. The side-angle set of images was then digitized and matched against the reference database using "batch compare" to measure the method sensitivity to the angle of the image that may distort distances between points.
We then assessed the ability to detect recaptures by matching T 4 images (candidate) to T 0 images of the same tree and the other tree (reference, Figure  1C ), as well as matching fingerprints from Tree 2 at T 0 (candidate) against Tree 1 at T 0 (reference). In the I3S software, identification of recaptures involves a judgment call based on a visual comparison of all pairs of images, and it is how we determined recaptures, independently of their scores.