dataesr/scientific-paragraphs-categorization huggingface dataset:
Keeping the human in the loop Even if we do not know yet what should qualify as a software publication, we do know that we need quality metadata to describe research software, and to be used for citing software artifacts. We argue that this requires human intervention, and that it is not enough to just share software on code hosting platforms like GitHub, or self-archive it on repositories like Figshare or Zenodo. This is why for deposit in HAL and archival in SWH a moderation process is put in place: to ensure that the deposit is a software artifact that reflects a scientific endeavour and that due credit is attributed to all authors of the software without a quality and functionality review of the source code.
Software Identification, reference and citation We follow the Software Citation Principles  (Smith et al. 2016)  to create a citation for software deposits into HAL. In figure  8. we have proposed a citation format containing metadata submitted with the software deposit, which is already available on the HAL platform. In the citation format, two identifiers are used: the first for the research product, the HAL-ID and the second for the software source code itself with the SWH-ID of the root directory containing the complete development tree. While the HAL-ID identifies the metadata and thus the attribution of the research product, the SWH-ID references the exact version of software source code associated to the deposit. Each identifier caters to different use cases. At the moment we are working on a proposal for a specific BibTex @software entry as it was already introduced in BibLateX (Kime, Wemheuer and Lehman, 2019) to provide a better BibTex export on the HAL platform.
Conclusion Decades of experience handling research projects at Inria have shown that a proper moderation process is important to ensure the high quality of the metadata associated to the research software artifacts. To support this process, the collaboration between Software Heritage, Inria and HAL has created tools and guidelines that enable digital archivists to efficiently handle research software deposits, and offers to the HAL users dedicated services for helping preserving and disseminating their software artifacts. We believe that this is an important step forward in the long journey to make software a first class research output in the scholarly ecosystem. On the HAL-CCSD-Inria-SWH collaboration roadmap, there are a few milestones ahead: allowing the deposit of metadata with a link to a code repository which will be archived in SWH or a direct reference to a SWH artifact with the SWH-ID; exporting BibTeX format with a complete @software entry; exporting other software citation formats (e.
Protocol Ab initio modelling provides very precise estimations of energy, volume and strain in defect-bearing minerals, with potential for systematic studies of partitioning between minerals. Calculations have been performed on periodic crystals with the CRYSTAL17 program  (Dovesi et al. , 2005 (Dovesi et al. , , 2018)). CRYSTAL17 uses orbitals as a linear combination of Gaussian functions and computes ground state energy directly from the Hamiltonian, using quantum equations and approximations that allow solving Schrödinger's equation for multielectronic atoms (see  Sherman, 2016). Complete technical description is given in  Dovesi et al. (2018). The B3LYP density functional has been selected as it is simply implemented with a localized Gaussian basis set and has shown excellent agreement with experimental data, including vibrational spectroscopy  (Pascale, 2004; De La Pierre and Belmonte, 2016; Sherman, 2016).
Acknowledgements. Many thanks are due to  Nicolas Rividi ,  Michel Fialin  ( CAMPARIS, Sorbonne Université ) and  Omar Boudouma  for their help and expertise with EPMA and SEM. The authors are indebted to  Roberto Dovesi ,  Fabien Pascale ,  Simone Salustro  and  Marco De La Pierre  for their precious help on CRYSTAL17 inputs. Computations have been made possible by using the  MeSU calculation centre (ISCD)  and the CINES centre. Two anonymous reviewers are gratefully acknowledged for their acute and constructive comments. Review statement. This paper was edited by  Alessandro Pavese  and reviewed by two anonymous referees.
Fluorescence gel imaging and microscopy Proteins were separated by SDS-PAGE and the gel was directly visualized with a Typhoon Trio fluorescence imager (excitation 488 nm emission 555 nm). The optimal amounts of Fluo-DHA for yeast or human cells or mitochondria as well as of purified cytochrome c were determined in function of the IC50 for in vivo experiments and in line with the sensitivity of the fluorescent imager (data not shown). The amount of fluorescence was quantified using the Fiji software. Yeast cells grown overnight in galactose liquid medium were incubated with 100 nM MitoTracker ® Red CMXRos (Molecular Probes) for 30 min, washed and diluted in phosphate buffered saline (PBS) before addition of 3 µM Fluo-DHA and incubation for up to 30 min. Time-lapse fluorescence imaging of live cells was performed using a Nipkow Spinning Disk confocal device (Yokogawa CSU-X1-A1) mounted on Nikon Eclipse Ti E inverted microscope equipped with a 100 X APO TIRF oil immersion objective (Numerical Aperture (NA): 1.
Mass spectrometry Mass spectrometry measurements were performed with an electrospray Triple-TOF 4600 mass spectrometer (ABSciex) coupled to the nanoRSLC ultra performance liquid chromatography system (Thermo Scientific) equipped with a C4-desalting column. For ESI-MS measurements, the instrument was operated in positive and RF quadrupole modes with the TOF data being collected between m/z 400-2990. Collision energy was set to 10 eV and azote was used as collision gas. Mass spectra acquisition was performed after loading and desalting of protein samples on C4-column. The Analyst and Peakview softwares were used for acquisition and data processing, respectively. The protein average masses are calculated from the spectra with a mass accuracy of ±2 Da.
Author contributions AL both performed the experiments and analyzed the data to determine active/toxic doses, measure O2 consumption, record cytochrome spectra and study in gel interaction between cytochromes and Fluo-DHA, with the help of AB-P for O2 consumption measurements, mitochondrial preparations and western blots. CP constructed the yeast mutant strains and performed the microscopy experiments. IK purified the human mitochondria and brought her expertise in human cell culture and mitochondrial proteins. JO, AB-P and AB participated to the initial experiments following the drug screening. DC performed mass spectrometry analyses. BM brought her expertise in the modeling in yeast of anti-malaria drug, NB in mitochondrial biogenesis and JC in malaria. GD designed the experiments and participated to the analysis of data.
Acknowledgements We thank  Laure Crabbe ,  Francesca Giordano ,  Pierrick Le Borgne  and  Anne-Marie Tassin  for useful technical advices and the members of the  MITOSCREEN consortium  for fruitful discussions. We are grateful to  Christopher J. Herbert  for critical reading of the manuscript and for looking over the English. The present work has benefited from the light microscopy facility of  Imagerie-Gif , member of  IBiSA  and supported by " France-BioImaging " and the  Labex "Saclay Plant Sciences ". We thank  Laëtitia Besse  and  Romain Le Bars  for their advice and help for the experiments and the presentation of the results. This work has benefited from the facilities and expertise of the I2BC proteomic platform SICaPS, supported by  IBiSA ,  Ile de France Region ,  Plan Cancer ,  CNRS  and  Paris-Sud University.
Data publishing is a challenging task for privacy preservation constraints. To ensure privacy, many anonymization techniques have been proposed. They differ in terms of the mathematical properties they verify and in terms of the functional objectives expected. Disassociation is one of the techniques that aim at anonymizing of set-valued datasets (e. g. , discrete locations, search and shopping items) while guaranteeing the confidentiality property known as k m -anonymity. Disassociation separates the items of an itemset in vertical chunks to create ambiguity in the original associations. In a previous work, we defined a new ant-based clustering algorithm for the disassociation technique to preserve some items associated together, called utility rules, throughout the anonymization process, for accurate analysis.
In this paper, we examine the disassociated dataset in terms of knowledge extraction. To make data analysis easy on top of the anonymized dataset, we define neighbor datasets or in other terms datasets that are the result of a probabilistic re-association process. To assess the neighborhood notion set-valued datasets are formalized into trees and a tree edit distance (TED) is directly applied between these neighbors. Finally, we prove the faithfulness of the neighbors to knowledge extraction for future analysis, in the experiments.
Introduction Set-valued data is one of the data formats that can be extracted from social networks, it is characterized by associating a set of values to individuals. Set-valued data is common in logs of search engines, search by hashtags, and can be also found in databases such as health databases and market basket records. This work is an extension of  [1]  which studies the privacy-utility trade-off of certain predefined associations in an anonymized set-valued data through the disassociation technique, defined first in  [2]. The aim of this paper is to continue the investigation and reconstruct the disassociated set-valued dataset to be considerate to future data analysis. Describing, diagnosing, predicting and prescribing are the main uses of data. Set-valued data provides multiple opportunities for various data mining tasks.
To illustrate the dilemma of data analysis and privacy preservation in set-valued data, let us consider an example of a mobility data, which stores the GPS location of individuals, each record corresponds to the set of visited cities by an individual. Finding a very high association rule between three cities {{Valetta, Bratislava} ⇒ {Salzburg}} might indicate a high demand for mobility solutions between the cities. On the other side, publishing set-valued data raises the problem of privacy preservation. If an individual, Bob, visited {Salzburg (Austria), Valetta (Malta), Bratislava (Slovakia), Bergamo (Italy)} and an attacker knows that Bob visited both {Valetta, Bratislava}; he/she can predict with high probability that Bob also visited {Salzburg}. Publishing the dataset unrefined, fails to protect the privacy of Bob's mobility data.
From the above example, we can see that it is necessary to extract knowledge from set-valued datasets but at the same time, the privacy of the individuals should be considered wisely. Publishing raw data is a public disclosure of information collected and puts the privacy of individuals at risk. For that reason, privacy-preserving mechanisms should be applied to the data before publishing it. Anonymization is the process that imposes a level of privacy on the data in an attempt to protect the privacy of individuals participating in the dataset. Anonymization techniques that can be deployed to protect the confidentiality of user data is a wide area of research  [3] [4] [5] [6] [7] [8] [9] [10]. Disassociation, is an anonymization technique presented in  [2] , particularly designed for set-valued data.
Basically, it set-valued data are clustered into homogeneous groups (via horizontal partitioning), and then each cluster is split into record chunks (via vertical partitioning) where items of a set are separated into disconnected subsets to preserve the k m -anonymity privacy constraint. However, the anonymization approach aims are both: to provide a dataset respecting privacy and to make the published data valuable for direct analysis. A trade-off must be found between privacy preservation and the benefits of knowledge extraction from datasets. Guided by the disassociation technique our investigation started in  [1]  by studying and improving the probabilistic preservation of associations in a disassociated dataset. After examination of the probabilistic preservation results, we present an optimization of disassociation for a predefined set of associations, we call utility rules.
The goal is to save the utility rules from vertical partitioning as much as possible, while respecting the k m -anonymity privacy constraint of disassociation. A derivative of the ant-based algorithm for the disassociation has been proposed to group data whilst respecting the utility rules. The new problematic in this extension of the work  [1] , is that in reality, we cannot define every association present in the original dataset as a utility rule in order to optimize its preservation for future analysis. Therefore, the study of association rules in a disassociated dataset cannot be limited to items preserved together without a break up after vertical partitioning. To run an analysis on a disassociated dataset, we must compute a huge set of pre-images of the disassociated dataset, by reconstructing all possible associations between subsets of items disconnected.
This new data format, the disassociated dataset, by breaking down a set of items into disconnected subsets of items, makes the analysis over it hard or at least time consuming to achieve. Primarily, it is useless to publish a dataset that cannot be helpful for data analysis. In this extension, the goal is to publish an anonymized set-valued dataset that falls in the neighborhood of the original one, consequently preserving its original format. In this context, the "neighbor datasets" terminology seems to fall perfectly to our intention. In an effort to make this article self-sufficient, Section 2 recalls and summarizes our previous work  [1]  for probabilistic preservation of utility in a disassociated dataset and its implementation as ant clustering. In Section 3, we present our first contribution where we define "neighbor datasets" as two similar datasets that fall under a certain radius of distance and present a way to asses the distance between the "neighbor datasets" by formalizing the datasets into trees.
Our second contribution in this work, presented in Section 4, is a technique that generates a neighbor datasets to the original, from its disassociated result, which can be seen as a statistical-based re-association. We investigate our solution from two perspectives. First, we look at the preservation of association rules which will reflect the synthetic similarity of neighbor datasets. Second, to generalize our approach, we evaluate the distance between the original and reconstructed datasets to have an overview of the created neighborhood using the distance introduced above. Figure  1  summarizes of the whole solution depicted in this article. All experimental results that confirm the applicability of our approach are presented in Section 5. Finally, Section 6 presents conclusive remarks.
The Privacy-utility Trade-off in Disassociation Giving an exact general definition for the utility of the data in the domain of anonymization is irrational. In this work, a utility rule is an association which is important for accurate analysis, especially for aggregate query answering accuracy. • Let UR = {ur 1 ,. , ur u } be a set of predefined associations, that we refer to as utility rules and are important in future analysis. • Let s(ur, T ) be the support of the utility rule ur in the original dataset. • Let s(ur, T * ) be the support of the utility rule in the disassociated dataset. To evaluate the probabilistic preservation of a utility rule in a disassociated dataset, the confidence of a utility rule is analyzed. Definition (α-confidence). The α-confidence of a utility rule ur is evaluated as follows: α = s(ur, T * ) s(ur, T ).
( 1 ) The term confidence is used to determine the strength of the association between the items of a utility rule ur after disassociation. Statistical queries are based on the support of the associations in question. The α-confidence represents a ratio of the preservation of a utility rule's support of a utility rule, reflected in the final output of the disassociation. In  [1]  the utility of an association in disassociated datasets is evaluated theoretically under the k m -anonymity privacy model, and is proven to be: 1 δ |ur| ≤ α ≤ 1. ( ) 2 From this perspective of privacy-utility trade-off, we are motivated to contribute with a more insightful horizontal partitioning process, tolerant to the predefined utility rules for future data analysis accuracy. The next section provides a general description of the clustering problem and then the role of swarm intelligence algorithms for the improvement of data clustering.
Ant Expert agent a i working for the benefit of the utility rule ur i. Pheromone trail Square matrix, A, representing the density of each utility rule in each cluster, updated through probabilistic picking-up and dropping functions. It is the shareable memory between the ants. Food Data records T |UR from the dataset T , relative to the utility rules such that: T |UR = {r ∈ T | ∃ur ∈ UR and ur ⊆ r} The ant's load load(a i ) contains a data record from T |UR that the ant a i is transporting. Individual ant's job Picking-up and Dropping a load. we transform normal horizontal partitioning for the set of records, which are supersets of at least one utility rule, into a clustering optimization problem. Before describing the algorithm, it is important to identify the challenges of the clustering problem in our context: • A record might enclose multiple utility rules and with partitional clustering, this record should belong to exactly one cluster satisfying one utility rule.
Ant-based Clustering Effect on Associations In  [1] , the efficiency of the UGAC technique in terms of preservation of the associations represented in the utility rule is evaluated alongside other experiments analyzing the privacy-utility trade-off. Results are very promising for the preservation of the utility rules. In this section, we investigate the effect of ant-based clustering for the predefined utility rules UR on the associations beyond UR. We chose for the experiment the BMS1 dataset, which contains click-stream E-commerce data, and a set of 70 distinct utility rules UR, extracted from the dataset with different characteristics: highest frequency= 1204 representing frequent association and Lowest frequency = 2 representing a very rare association. Only records that are supersets of the utility rules (36141 records from the original set of 149639 records) are clustered and evaluated.
This indicates that for the records that are treated through UGAC, the associations are not deteriorated on the expense of preserving the UR. Finally, we can say that UGAC is reliable for analysis for the utility rules in question and beyond them. The above work, as presented, offers an optimization for disassociating interesting rules in the datasets for analysis. However, a disassociated dataset is not the most appropriate data format for knowledge extraction because it breaks down a set of items into disconnected subsets of items. For this reason, we present in the next section, an algorithm that generates a re-associated dataset from the disassociated one, that we intend to call it a neighbor dataset, by re-associating statistically partitioned subsets to restore the original format of set-valued dataset.
Neighbor Datasets: a Road-map for Distance Evaluation When a dataset is anonymized it cannot hold the exact same records as the original dataset. Yet, for the anonymized dataset to be useful it has to lead analysis in the same direction of the original dataset. In the following, we define a dataset T and its anonymized version T as two neighbor datasets if they fall under a certain radius of distance and consequently lead to close hypothesis when analyzed. Let us first define neighboring for two datasets. To be able to describe datasets as neighbors, two questions arise. First, should the definition of neighboring datasets be contextual or general? Second, how can we mathematically assess this neighborhood degree of two datasets? In the following, we go over the above two dilemmas. From what we briefly described above, we see that the basic definition of neighbor datasets should neither discriminate a contextual nor a general analysis.
Taking into consideration both very general branches of data analysis we surely need to have a synthetic reproduction of the original dataset T. Then, the value of the items should stay intact without generalization, to preserve the specificity and context of outliers. At the same time, the neighbor dataset should be a synthetic representation of T and this means that statistically, the two datasets should represent almost the same overview of the data, leading to a close hypothesis. This section first introduces how set-valued datasets are translated into trees (Sec. 3. 1) and further shows (Sec. 3. 2) how generated trees are used to compute distances between this dataset.
Dataset Formalization This work focuses on unordered records of sets of distinct data. For instance, a summary of visited cities during last month for some travelers, sets of unique words used in web queries for collection of persons, etc. There is no interpretation of the sens of the data neither in terms of semantic nor in terms of values. Data in records are either equal or distinct. They are thus considered as unique. For instance, if the same city is visited twice by a traveler, it will only be counted once in the summary of cities visited during the period under study. Moreover, the order (between records or between innermost data) is not taken into consideration. There is thus no notion of temporality in the data. From a mathematical point of view, the considered data are multisets of finite sets of discrete data.
Tree Edit Distance for Datasets Tree editing distance is a measure that estimates the cost of transforming one tree into another. More precisely, this cost depends on the minimum number of elementary operations (such as deletion, insertion and renaming applied to a node of a tree) weighted by their cost, to move from one tree to another. This notion extends to trees the edit distance (or Levenshtein Distance) between character strings. We are then left to translate in a uniform manner multiset of sets to ordered trees. Each set of items (i. e. , each record) is thus translated into an ordered list of items, thanks to a lexicographic ordering. It results in a multiset of words where each of them contains at most one occurrence of each letter. Similarly, all these words are lexicographically ordered leading in a sequence of words.
Probabilistic Wheel Re-association Algorithm The problem of publishing an anonymized set-valued data is transformed into finding a neighbor dataset that shows true features from the original dataset while being distinct, hence faithful to data analysis. In this section, we propose an algorithm, probabilistic wheel re-association, Algorithm 5, to generate neighbor datasets, given hereafter. Roulette wheel selection is a probabilistic optimization method used for selecting potentially useful solutions for recombination. We profit from the disassociation technique to lead the re-association process. Disassociation by default creates ambiguity between associations present in different record chunks of a cluster, while preserving the accuracy for associations found in the same record chunk. Ambiguity on the level of the associations between the record chunks is our playground for the probabilistic wheel re-association.