dataesr/scientific-paragraphs-categorization huggingface dataset:
From these data, we construct a panel dataset by aggregating the number of investments made in a particular campaign on a single day. Thus, our unit of observation is the number of investments for a given campaign day, with a specific campaign as the cross-sectional dimension and the day as the time dimension. For each campaign, we have as many observations as the duration in days of the campaign, which varies from one campaign to another because many campaigns achieve their limit before the end. Table  1  provides summary statistics for the types of campaigns included in our sample. We provide summary statistics on the panel dataset in Section 5. Overall, 81% of the 89 campaigns achieved their minimum goal. More specifically, all portals except Innovestment were able to complete 100% of the campaigns successfully.
With a high minimum investment ticket (in most cases, 1000 EUR), which represents a self-imposed restriction on capital supply, only two-thirds of the campaigns were completed successfully on Innovestment. This comparably lower success rate could also be due to Innovestment campaigns having defined a relatively high minimum funding threshold of 60,747 EUR on average, which is twice as high as that on Companisto. These higher thresholds might have a positive effect on campaign selection, which is beyond the scope of this article. Table  1  further indicates that the average funding goal is 51,687 EUR (median of 50,000 EUR) and the average funding limit is 216,742 EUR (median of 100,000 EUR). However, our data also vary greatly, as the largest funding limit is 1. 5 million EUR. The average campaign duration is 45 days (median of 36 days), and the average number of backers is 293 (median of 48).
In every specification, we include a measure of innovativeness of the startup (Nbr. Patents) and a measure of development and human capital of the startup (Nbr. Directors). These two variables are time-invariant within each campaign. Furthermore, DoW is a vector of dummies indicating the day of the week, MoY is a vector of dummies for the month of the year, and Year is a vector of dummies for years from 2012 onward, excluding 2011. Finally, Campaign i captures the campaign fixed effects. As our panel data has a time component, it is natural to question whether stationarity could be an issue. Although to the best of our knowledge no formal tests exist for the estimation methodology used here, we checked for unit roots in a linear model (using the command "xtunitroot" in STATA). While some unit-root tests require a strongly balanced dataset, others can be performed with any structure.
Astronomy is a fertile environment with collaborations leading to the elaboration of disciplinary standards. Deploying standards in data centers can be beneficial for long term preservation and it puts the discipline in the open data era. VizieR  [1]  is the CDS service dedicated to catalogues. It gathers data and tables, but also associated data like spectra or images coming from published papers or collaborations with various agencies. VizieR gives access to data through services to the astronomical community and preserves data for the long term. Several years ago, it became clear that it was worth applying for an external evaluation to certify that VizieR is a trustable data repository. The Data Seal of Approval (DSA) [2] is granted to repositories that are committed to archiving and providing access to scholarly research in a sustainable way.
Open Data in VizieR 1. Role of the data center The CDS VizieR catalogue service, created in 1995, provides the astronomical community with free access to data. Ingestion workflows have been in action since the beginning. VizieR uses a standardized description of the resources which is provided by specialized documentalists, in some cases with an initial description provided by the author or the journal. The CDS is clearly a major actor in Open Data in astronomy and beyond. Open data is currently a "hot" concept  [4] , demanded by authorities for publicly funded science data. The first mission of an Open Data Center consists of preserving the data and giving free access. In return users have to cite the data origin (author, article) with a persistent identifier. VizieR provides a bibcode (the bibliography identifier used by the astronomical community  [5] ) and internal identifiers which are both persistent and enable citations and access.
The Open data challenges VizieR, as is the case for every data center, is between the data producers and the consumers which each have their own demands. The producers for VizieR are the agencies which run space and ground-based telescopes, teams which produce large surveys, and the academic journals, with huge data diversity and increasing volume, especially for large surveys catalogues with tables over a billion of records (e. g. Gaia mission). The consumers are the astronomers, research pipelines or software which require data and meta-data quality that we have to ensure. Quality has a cost. For example, it takes resources to find the information necessary to produce the rich meta-data of the Virtual Observatory standards. Responding to these demands is a challenge, which becomes possible through interaction and cooperation with others.
Firstly, cooperation with data producers is needed to improve the ingestion pipeline and build the initial knowledge about the data. Secondly, CDS long term success demonstrates that pooling different skills is beneficial: at CDS, astronomers, documentalists and engineers work together. These collaborations locally and with data producers are needed to jump on the big data bandwagon, which is moving faster and faster. VizieR is a reference service for catalogue dissemination recognized by its funding bodies, its partners and the global astronomical community. The next step was to get certification that VizieR is a trustable repository through an external evaluation. The CDS had already been certified as a member of the ICSU World Data System (WDS -http://www. icsu-wds. org/ [7] ) community of trusted data services for Global Science.
The criteria The Data Seal of Approval (DSA) is dedicated to scholarly research data repositories. It is a trustbased certification, simpler to get than the ISO one. It does not include audit visits but is based on a 2 EPJ Web of Conferences 186,  02003 (2018)  https://doi. org/10. 1051/epjconf/201818602003 LISA VIII self-assessment of 16 criteria, which is then examined by the DSA Board which grants the Seal. The label is provided for 3 years before needing renewal. The DSA worked with the WDS under the auspices of the Research Data Alliance to align their certification criteria and processes, resulting in an updated catalogue of common criteria in 2016, which better underlines the importance of data reuse. The two organisations established a common Certification Board in 2017 a common Certification Board.
DSA evaluation The DSA proposes an auto-evaluation with a compliance scale composed of 5 levels depending on the implementation status for each criterion. Among the levels, one is "Not applicable. " For instance, in VizieR the criteria concerning confidentiality and ethics is not applicable because VizieR data are not subject to disclosure risks. The DSA board (now the common DSA-WDS one) examines the responses and the documentation provided by the data center. Peer evaluation is performed by one member of DSA and one from WDS. The reviewers write a report to the Board which decides whether to award the Seal. During the VizieR evaluation, which was under the sole DSA Board responsibility at that time, we got comments from the DSA reviewers on our initial application. It was helpful assistance to improve the documentation.
Describing the Information System with OAIS DSA proposes to use OAIS (Open Archive Information System) to describe the data center processes. OAIS is registered as an ISO norm (14721:2012); it defines a reference model and an editing guideline to describe an Information System (including the system, architecture, staff). OAIS is dedicated to open archives. Data and meta-data are inseparable in the OAIS concepts, they are gathered into information packages and evolve together during the whole data life cycle. In the next sections we will describe some of the OAIS concepts: • the Information System described in six OAIS entities (section 3. 1) • the three types of information packages of the OAIS norm (section 4) 3 VizieR described in the OAIS norm.
The six entities are completed by "Management" which is outside of the Information System. At CDS, we have the Scientific Council composed by an international committee that evaluates the CDS scientific impact. They audit the CDS each year and write a report which is a guideline for the next year. Figure  1  shows the VizieR information System described in the OAIS-like norm. VizieR benefits from for than 20 years of experience, complemented by the even longer term expertise of the CDS in astronomical data management and dissemination which has been built up since its creation in 1972. In particular VizieR benefits from well established workflows for data description and of CDS expertise in the implementation of the Virtual Observatory standards. Both were key elements of VizieR in the description used to obtain certification.
VizieR Workflows according to the CDS data curation The data provenance as well as the content (including meta-data) are defined by the Administration entity of the CDS, which is composed of the astronomers and the governing body. Depending of the origin, the data are submitted by authors or documentalists into the "Entry" entity. Documentalists are the building block of the data ingestion. They offer assistance to authors for their data submission and then check and complete the description or fully write the description. Finally, the astronomers validate the documentalist's work and allow access to the public database.
5 EPJ Web of Conferences 186,  02003 (2018)  https://doi. org/10. 1051/epjconf/201818602003 LISA VIII Associated data  [8]  such as images or spectra, often in FITS format, are described with the Ob-sCore Data Model  [9]  of the Virtual Observatory. The ObsCore meta-data of the FITS documents is separated from the ReadMe and provided as key-value mapping. The tables are submitted in the CDS ascii format with aligned columns. The CDS provides tools for authors to migrate CSV, FITS or VOTable into the CDS ascii format. This format is adapted for long term preservation; it is independent of the technology and is described with a byte-by-byte section in the ReadMe file. All the columns of the tables are described with their unit, data type and a description text.
Archival Storage Data are preserved by the "Archival Storage" entity. Data and meta-data are the Archival Information Package (AIP) described in the OAIS norm. The AIP contains all data and meta-data available locally. In VizieR, it includes the basic and rich meta-data with the identifiers, but also some private data useful to rebuild the system, like the history of the ingestion processes, the email correspondence with authors, etc. The AIP are the result of curation by the CDS documentalists. This process is the core of VizieR, and is required to provide useful data and added-value. Among the meta-data, a configuration file completes the ReadMe file. It includes: • the United Content Description (UCD  [10] ) for each column which are used by VO enabled software; • links to other VizieR catalogues or external links; • target name resolution to add positional computed columns; • plot visualization for time series; • photometric system and filters for photometry columns.
Data Provision The data provided by VizieR are the Diffusion Information Package (DIP) described in the OAIS norm. In OAIS, the DIP includes the information package and the dissemination protocols. The Virtual Observatory standards including data model, formats and protocols constitute a framework which is a good example of what can be a DIP. As an example, the VO registries  [11]  index the astronomical resources in a common list of resources independent of the data center. All resources have an identifier (ivoid) and are linked with a description. The registry technology is based on the OAI-PMH protocol, a standard used for instance in the digital library context or in EUDAT B2FIND registry (https://www. eudat. eu/services/b2find), which allows federation with other resources.
Conclusion VizieR DSA certification was facilitated by more than 20 years of experience with well established workflows which are already in place for data management and description. The implementation of the Virtual Observatory standards was the second pillar that enabled us to document the CDS response to the criteria. These standards match well with the dissemination requirements of the OAIS norm. The documentation in VizieR is provided by specialized documentalists. Their work is crucial to the high quality of the service and to gathering the information needed to build and rebuild the system. The full certification process required about 3 person month. It was team work to which all the team members contributed. While there was no immediate update of the system there was better documentation, which was necessary to obtain certification.
METHODOLOGY We selected Swedish as language of analysis due to the contradictory observation attested on grammatical gender (see Section 2. 1) and its availability of first hand data. For each nominal classification task, we train a classifier on a set of word vectors which are labeled with their corresponding nominal classes. The process is divided into three steps: vector generation (word embedding), data labeling, and classification. During the first stage, word embedding, a corpus of raw sentences with word segmentation is fed to a word embedding model. The word embedding model assigns a vector to each word in the corpus. This vectors assignment is done in such a way that semantic similarities of words is modeled by the correlation between their corresponding vectors, i. e. , the semantically similar words are assigned to the similar vectors.
Then, in the data labeling step, a dictionary is used to associated a subset of word vectors with their corresponding nominal classes. These are the word vectors associated with some nouns in the dictionary whose nominal classes could be extracted from the dictionary. Later on, in Section 4, we elaborate how the nominal classes are extracted from the dictionary. This gives us a list of word vectors labeled with nominal classes. This list is partitioned into train, and test sets, to be used in the classification step. Finally, in the classification, we train a classifier on the pairs of word vectors and nominal classes. The classifier takes word vectors as input and predict the nominal classes in its output. The train data is used to train the classifier, and the test set is used to evaluate the classification model.
Since, this research aims to study the information provided by word vectors for different nominal classification tasks, we use simple classification methods without performing complicated tuning step. This is why we don't keep a part of data as development set to tune the classifier. The evaluation is on the basis of the performance of word vectors on each of the nominal classification tasks, e. g. , the higher the accuracy of the classification is, the more the information about the nominal classes is encoded into the word vectors. In the next section, we elaborate the tools, models, and data used in each of these steps, word embedding, data labeling, and classification.
EXPERIMENTAL SETTINGS Our word vectors are generated via RSV (Real-valued Syntactic Word Vectors) model for word embedding  (Basirat and Nivre, 2017). RSV extracts a set of word vectors from an unlabeled data in three major steps: First, it builds a co-occurrence matrix whose elements are the frequency of seeing words together. The elements of this matrix are the frequency of seeing words in the domain of different context words. The columns are associated with words and the rows are associated with contexts. Each column forms a high dimensional word vector that describes the word with respect to its occurrence frequency in different contexts. In the second step, the elements of the high dimensional column vectors are transformed in such a way that the distribution of the vectors is more close to the Gaussian distribution with zero mean.
Finally, in the third step, it forms the low dimensional data from the top K right singular vectors of the transformed co-occurrence matrix. Within this process, the RSV model has the following parameters: • Context type: the context of a word may refer to the preceding words (symmetric), following words or include both directions (asymmetric). • Context size: how many words does the system count in the context. As an example, the most popular setting is one preceding word. • Dimensionality: the quantity of dimensions the model may use to represent the word vectors. The amount of dimensions is generally positively correlated to the accuracy, but negatively correlated with the processing time and memory. Within our experiments we set context type and context size as the immediate preceding word, as proposed by  (Basirat and Nivre, 2017).
While Neural Network is the more elaborate type of classifier but also costly in terms of processing time. Our computational model is fed with two types of data, which both originate from the Swedish Language Bank (Språkbanken) at the University of Gothenburg. 2  The word vectors are generated from a corpus of Swedish raw sentences. This corpus is compiled by Språkbanken and involves data from Swedish Wikipedia (available at Wikipedia Monolingual Corpora, Swedish web news corpora  (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013)  and Swedish Wikipedia corpus). The OpenNLP sentence splitter and tokenizer are used for normalizing the raw corpus. 3 We replace all numbers with a special token NUMBER and convert uppercase letters to lowercase forms. Due to the high ratio of compound nouns in Swedish  (Carter et al.
For instance, with regard to grammatical gender, we solely cover nouns annotated as uter and neuter since only these two classes fulfill the conditions of grammatical gender. Furthermore, the frequency and quantity of peripheral nouns such as vacklande and blank is not significant compared to the whole dataset (1%). Hence, we leave these patterns of variation for further studies to explore. We also filter out those nouns that are not in word vectors' vocabulary set. The result of our filtering is shown in Table  1. The distinction of grammatical gender and common/proper nouns is rather straightforward, as the information is annotated in SALDO. However, the count/mass distinction is not a category transparently specified in the dictionary. Thus, we follow the formal syntactic definition and consider that only count nouns may have plural inflection  (Chierchia, 1998; Chierchia, 2010).
RESULTS The results of our experiments are evaluated according to the Rand index  (Rand, 1971 ) (accuracy) and F-score  (Ting, 2010). To obtain the general accuracy of the model on the entire dataset, the Rand index is calculated by dividing the total number of correctly retrieved tokens by the total number of retrieved tokens. In the remaining parts of this paper, Rand index and accuracy are used interchangeably. As as side note, we do not compare directly our results with other studies as we are not aware of methodologies and approaches being applied in a way similar to ours. Due to the lack of balance between the investi-gated classes (e. g. , 71% uter words vs 29% neuter words), we use majority label prediction, called Zero rule, as our baseline. In this case, our accuracy baseline for each of the nominal classifications is equal to relative size of the larger class, i.
e. , recall is obtained by dividing the total number of correctly retrieved tokens with the total number of correct tokens in the dataset. The two measures evaluate different facets of the output, thus they are merged into the F-score, which is equal to the harmonic mean of the precision and recall, 2 recall×precision recall+precision. Furthermore, we also provide two figures for every class of nouns we targeted. First, we display how the noun classes are distributed in the distributional semantic space formed by the word vectors. Second, we show the histogram of the entropy of the neural network's output for each class of nouns. The entropy measures the uncertainty involved in the neural network's output to identify the noun classes. High values of the entropy can be interpreted as more uncertainty in the classifier's outputs, which itself show the weakness of the information provided by the input word vectors with regard to the nominal classes.
Common/proper nouns Table  3 ) summarizes the classification results obtained from both LDA and the neural network to the distinction between common and proper nouns. We see that both classifiers outperform our baseline accuracy (85. 5%). In terms of accuracy, the neural network is more accurate than LDA. However, most of this achievement is due to the unbalance data distribution which biases the neural network toward the bigger population. In term of f-scores, we see that LDA is more accurate than the neural network in identifying the proper nouns and it is as accurate as the neural network in identifying the common nouns. It is also worth noting that LDA is much more efficient than neural network when the processing time is of importance. The high accuracy obtained from LDA show that the word vectors should be linearly separable with regard to this nominal classification.
This basically shows that the neural network's prediction on the mass nouns is almost always wrong, i. e. , mass nouns are always classified as count noun by the neural network. However we see that the LDA's recall on the mass nouns (76. 9%) is significantly higher than the baseline's recall (0. 0%) and the neural network's recall (7. 1%). This is because of the generative nature of LDA that always give a chance to all classes, regardless of their size, to appear in the prediction task. However, this might not always work well specially when the data is not properly distributed with regard to the classes. In this case, the classifier will result in a small value of precision, as we see for LDA's precision on the mass nouns. In general, the weak performance of the classifiers on the count/mass classification task shows that the word vectors have almost no information about this feature of the nouns.
ACKNOWLEDGEMENTS Our work on this paper was fully collaborative; the order of the authors names is alphabetical and does not reflect any asymmetry in contribution. We are grateful for the fruitful discussion with the audience of the  Special Session on Natural Language Processing in Artificial Intelligence at the 10th International Conference on Agents and Artificial Intelligence in Funchal, Madeira. We would like to express our gratitude to our colleagues  Linnea Öberg ,  Karin Koltay ,  Rima Haddad , and  Josefin Lindgren  for their comments and support. We also appreciate the constructive comments from the anonymous referees. We are fully responsible for any remaining errors.
In Vivo Fibered confocal Fluorescence Microscopy (FcFM) for rMVa Visualization in the skin In vivo FCFM was performed with a fibered confocal fluorescence microscope (Cellvizio ® , Mauna Kea Technologies ® , Paris, France). A probe that records the green fluorescence signal at 488 nm by sweeping the site of interest was applied two times during 30 s  (36). GFP + objects were counted in each frame (size > 20 μm 2 ) using ImageJ ® software  (37). The analysis was performed on the 50 frames containing the highest number of GFP + objects for each observed site. For each time-point, the number of fluorescent cells of up to three injection sites was measured and is expressed as the mean ± SD of GFP + cells. histopathology Skin and LN samples were fixed at 4°C in 4% paraformaldehyde for 24 h. Samples were stored in PBS at 4°C.