dataesr/scientific-paragraphs-categorization huggingface dataset:
The classifiers show a slightly weaker asymmetrical pattern in that Yukuna is also significantly different from its Arawakan relatives ( p = 0. 012), but where Tanimuka is similar to Arawakan ( p = 0. 45), Yukuna is still significantly different from Tukanoan ( p = 0. 006), and Tanimuka and Yukuna show non-significant differences ( p = 0. 126). Of the remaining four datasets, two show weak asymmetric patterns. Noun phrase structure is significantly different for all pairs, except for Yukuna and its Arawakan sister languages, but Tanimuka does seem to have become more similar to (though still significantly different from) Yukuna ( p = 0. 042, the highest p value of the significantly different pairs, electronic supplementary material, table  S4 ). The phoneme-allophone database shows a pattern in which Tanimuka is both similar to Tukano ( p = 1) and Arawak ( p = 0.
084), while Yukuna is different from both ( p = 0. 006 for both), suggesting a possible change within Tanimuka toward the Arawakan profile. The last two datasets show different patterns. Case shows parallel structures throughout the two families, including Tanimuka and Yukuna, as none of the groups are significantly different from each other. The only dataset that shows signs of convergence is the dataset on clausal structure, where Yukuna and Tanimuka are not significantly different from Tukanoan and Arawakan, respectively, and from each other ( p = 0. 054, electronic supplementary material, figures S10-S13 and electronic supplementary material, tables S4-S7). royalsocietypublishing. org/journal/rsfs Interface Focus 13: 20220056 All in all, then, the main signal is asymmetrical, suggesting that Tanimuka has been influenced by Yukuna much more than vice versa.
Comparative sociocultural data NMDS plots from the similarity matrices calculated from the complete sociocultural dataset and the gender-based subset appear in figure  8  and electronic supplementary material, figures S14 and S15. In NMDS plots, stress values below 0. 1 are considered to give an accurate representation of similarities, values between 0. 1 and 0. 2 are useful for distinguishing broad-scale patterns, while stress values above 0. 2 could be misleading  [94]. For this reason, we use the NMDS plots in this paper to explore the data, but we always base our conclusions on PERMANOVA results. Electronic supplementary material, figure  S14  provides a broad overview of the sociocultural patterns in NWA. ET speaking groups form a fairly tight cluster in the top-left quadrant, suggesting a relatively high degree of sociocultural homogeneity among speakers of those languages.
This would make the gendered pattern in the data more acute. The NMDS plot in figure  8  shows the distances for sociocultural practices and knowledge that, according to the    S14 ). We consider this differential pattern as adding further weight to the hypothesis that the Tanimuka paternal lineage came from another group -likely Arawakan-which shifted to an Easten Tukanoan language as they joined the ET social complex through the exchange of women  [14, 96]. Unfortunately, subsetting the dataset to reflect only cultural practices transmitted from mothers to daughters presented some problems in the analysis. In particular, the high degree of homogeneity in women's cultural practices in the NWA (as defined in table  2 ) led to a large proportion of variables with a single value. This forced us to discard all but 15 of the variables, making the NMDS plot uninformative (electronic supplementary material, figure  S15 ).
Such interpretations of myth and oral history have been proposed for the Wakuénai from Venezuela  [98] , relatives to the Curripaco in Colombia and Baniwa in Brazil. However, our sociocultural dataset only shows broad-scale patterns, suggesting a close relationship between Yukuna and Tanimuka and a general similarity among Arawakan and ET-speaking groups from the area (figure  8  and electronic supplementary material, figure  S14 ). Linguistic data provide evidence of the pervasive contact influences on the Yukuna and Tanimuka languages. We observe signals of both convergence and asymmetrical impact on their languages, in which Tanimuka has been influenced by Yukuna much more than vice versa. This can be seen in figure  6 , in which we contrasted prototypical shift and prototypical convergence scenarios.
Ethics. This study was approved by the Institutional Review Commit- tee on Human Ethics of the Universidad del Valle in Cali, Colombia (Acta No. 021-010) and the Ethics Commission of the University of Leipzig, Medical Faculty. All human DNA samples used in this study were collected prior to obtaining written informed consent from each participant and all procedures were undertaken in accordance with the Declaration of Helsinki. We obtained further ethical clearance from the ethical committee of the Faculty of Humanities at Leiden University, confirming that the study complied with the ethical standards of said institution. Data accessibility. The linguistic and sociocultural databases are provided in the electronic supplementary material  [102]. Given the sensitive nature of the human genetic data generated in this study, these will not be made publicly available, but deposited to the European Genome-Phenome Archive (EGA; https://ega-archive.
12 187. 56 178. 60 179. 25 177. 41 182. 06 189. 93 179. 80 185. 82 215. 54 201. 65 184. 70 183. 63 204. 72 189. 02 195. 12 Murui 176. 53 161. 28 162. 62 162. 87 163. 93 162. 47 163. 84 161. 43 160. 69 162. 17 176. 89 161. 40 161. 63 161. 64 162. 85 175. 98 180. 49 164. 70 161. 16 159. 83 164. 53 138. 45 Uitoto Achagua Sikuani Guayabero Nukak Piapoco Curripaco Puinave Carijona Desano Piratapuyo Kotiria Kubeo Tukano Siriano Barasana Siona Koreguaje Tanimuka Yukuna Matapi Murui Uitoto donor Figure 4. Heatplot of the mean total length in centimorgans (cM) of DNA segments that each recipient group (rows) copies from each donor group (columns). Ethnolinguistic names are colour-coded by language family as indicated in figure1. royalsocietypublishing. org/journal/rsfs Interface Focus 13: 20220056.
e. , that data should be Findable, Accessible, Interoperable and Reusable  [6] , and in part because larger datasets are needed for much health-related research that cannot be provided by one single data source. In this context, an increasing number of data catalogues are being developed to support: • discovering data, to facilitate access to information and optimise usage of available data; • understanding data, to assess their fitness-for-purpose to address specific research questions; • leveraging integration of data, to address a broad range of research questions, increase statistical power, and enable comparison, cross validation or replication analysis. While these catalogues are of great value, their heterogeneity poses challenges to optimally exploit their richness. This opinion review focuses on catalogues that describe data sources for human subject research such as cohorts, biobanks, registries, administrative and healthcare records.
[12]. An example of a catalogue that provides sufficient information to inform multi-center data analysis is the catalogue of the Maelstrom initiative. Maelstrom is an international collaboration of epidemiologists, statisticians, and computer scientists with a focus on enabling multi-cohort data analysis using data harmonisation for which they provide rigorous guidelines on what metadata to collect  [7]. Their catalogue, https:// www. maelstrom-research. org/, documents cohorts related to a particular research domain, e. g. paediatrics/parent-child, and networks of cohorts and researchers, such as international research consortia/projects, that mostly retrospectively harmonise data following the Maelstrom data harmonisation principles  [13]. This catalogue provides detailed listings of 23 Networks, Individual studies (177 with variables) and Harmonisation projects (7, numbers checked Jan 2022).
Cohort Data Catalogues In Europe, several catalogues have emerged following the convention of Maelstrom, typically as consortia funded by the European Commission, starting with BioSHaRE  [15, 16]. In the terminology of Maelstrom, each such consortium is a Network, and its catalogue a Network catalogue. A recent example is the LifeCycle catalogue (https://catalogue. lifecycle-project. eu/)  [17]. This catalogue provides two entry points. By default, users are led to browse all harmonised variables, based on a keyword classification similar to (but not the same as) Maelstrom. For each variable, users can see if and how variables from partner cohorts in LifeCycle have been mapped. Secondly, users can browse the cohorts to receive general descriptions of the cohort. Currently, the LifeCycle catalogue is being expanded to include metadata from other harmonisation consortia, notably LongITools  [18]  and ATHLETE  [19]  and to be expanded to other consortia in the European Human Exposome Network (https://www.
humanexposome. eu/). These catalogues are available as open source via the MOLGENIS software suite  [20]. Another recent example is the RECAP Preterm catalogue (https://platform. recap-preterm. eu), containing information about pregnancy cohorts, which also makes use of OBiBa's Mica software to extensively catalogue the studies and data available on the project's platform. A variable classification system (in the vein of Maelstrom's 'Areas of information') was developed  [21]  and the project's variables were mapped to it in order to improve variable searching and harmonisation on the platform. The large boxes define areas of metadata for multi-center cohort study catalogues: the population(s) being samples, e. g. , 'Mothers in Northern Netherlands'; rounds/events of data collection, e. g.
Real World Data Catalogues Also, consortia using RW data have been establishing catalogues to document 'secondary use' data sources. The European Medicines Agency (EMA) in 2021 funded the MINERVA project to define a set of metadata to describe RW data sources. As an introductory step, the MINERVA project executed a search for relevant catalogues to describe RW data sources and we report here a selection of that list  [22, 23]. The catalogue of the IMI-EHDEN project includes a dashboard allowing to display characteristics of the data sources participating in the project, all converted to a same common data model, and graphical and aggregated information about their content. It also allows the comparison of the harmonisation potential between different data sources  [24]. The EMIF catalogue is an example of a catalogue representing both RW and cohort-derived data sources  [25].
It is organised as a set of data research communities, each covering distinct data types, diseases, and users. It does not require pre-specified metadata to be catalogued: each community defines its own data model, to describe the catalogue entries. Despite this customisation facility, it lacks a common schema to integrate easily with external catalogue systems. The ENCePP Resource Database is an initiative of the EMA-sponsored European Network of Centres for Pharmacoepidemiology and Pharmacovigilance, which is freely accessible (https://www. encepp. eu/encepp/resourcesDatabase. jsp). However, it does not provide a description of the data model of the data sources, and EMA funded the above-mentioned MINERVA project as part of the effort to improve the ENCePP catalogue. With this mission, the MINERVA consortium has developed common data elements for RW data catalogues  [26].
This catalogue was based on a conceptual framework from the IMI-ConcePTION Project  [27] , which is also embedded in the IMI-ConcePTION catalogue  [28] , and that we briefly summarise here: a data source is a collection of data banks, e. g. the National Health Registries of Denmark is a data source including the Danish National Patient Registry, the Danish Medical Birth Registry, and others. Each data bank is a collection of data that is mandated and sustained by an organization (in the example, both data banks are mandated by the Danish Health Data Authority) and where data generation is prompted by a class of events called prompts: in the example, data in the Danish National Patient Registry are prompted by discharges from Danish hospitals, and data in the Danish Medical Birth Registry are prompted by births happening in Denmark.
Comparison of Metadata Collected and How They Map onto Each Other, as The Basis for a Unified Conceptual Framework In this section, we analyse the potential to harmonise and/or standardise catalogue contents between catalogues. We therefore analysed existing catalogues from Maelstrom, MINER-VA, IMI-ConcePTION, LifeCycle, BBMRI/ MIABIS, EMIF, ENCePP and IMI-EHDEN (described above). Subsequently, we analysed how similarities could be harnessed towards a harmonised framework for both research cohort and RW data catalogues.
Analysis Towards Common Conceptual Framework In this section, we discuss three areas where we find correspondences between cataloguing cohort and RW data sources: the data collection itself (Table  2 ); the steps to address a research question in one single database (Table  3 ); the steps to enact interoperability across data sources to address a research question (Table  4 ). From the point of view of a catalogue, the most important difference between research cohorts and RW data is the unit of the data collection: in the cohort domain, the unit is the cohort itself, which is also the data collection needed to conduct a study; in the RW domain, the unit is the data bank (e. g. , registry, primary care records, pharmaceutical database), but a second-level unit, the data source, possibly including multiple data banks, is what is used to conduct a study  [27].
For this reason, both the MINERVA and the ConcePTION catalogues have both levels of data collections separately described. Another important difference is the population. In the case of a cohort, the population is the set of persons whose data is collected, while the population of a data bank may not be included in the data bank itself: for example, in the case of a death registry, the population is not the deceased, but the persons whose death, were it to happen, would be recorded in the data bank. In general, the population is not the set of persons who have experienced the prompt, but the set of persons whose prompts, were it to happen, would prompt a record in the data bank. In Table  3 , five dimensions are listed. In a catalogue of cohorts, most of the information relative to a study can be described independently of the research question: with the exception of large general cohorts, the institution using the data is very close to the process of data collection, the research question belongs to a set of pre-specified questions incorporated in the design of the cohort, and both selection of the study population and study variables are embedded in the cohort data model.
On the other hand, in the RW domain, the selection and study variables observed on the data source population to conduct a study are not observed directly, but rather are derived based on the collected data, a process referred to in the literature Often international coding systems also as measuring  [29]  or phenotyping  [30] : if a study must select persons with diabetes, an algorithm to identify diabetes from the data banks of each data source needs to be specified  [31]. Since every study has different selection criteria and study variables (associated with the specific research question), to enumerate such information in a catalogue it is convenient to include study-specific sections. Indeed, the choice of phenotype may depend on the research question: for a surveillance study, a more sensitive phenotype is preferrable; to select a population with a disease, a more specific phenotype may be advisable.
On top of this, the derivation of study variables must be enacted based on the CDM  [33, 34] , possibly using prompt-tailored algorithms  [35, 27]. In summary, cohort catalogues can be nested in RW catalogues where: • Cohorts are a special case of a data source with a single data bank, having a set of research questions embedded in the data bank design, and having as population the set of persons whose data are included in the cohort; • Collection events can be considered a special case of prompts; • Harmonisation of items is a special case of conversion to a common data model, since in RW additional information about the data sources must be stored, i. e. , how to derive study variables to address research questions.
We speculate that it is much easier to catalogue information of interest that is required by a specific research group than for a diverse and more distant user community that is hard to engage. Finally, we have observed in other scientific databasing efforts that having one central effort creates potential single points of failure when funders and institutions priorities shift, while networks of such database efforts are easier to sustain [http://www. insdc. org/]. Meanwhile,  small catalogues developed for a particular research project are also hard to sustain over the long term, potentially wasting cataloguing efforts. Moving forward, we believe we should thankfully embrace existing data curation in distributed cataloguing efforts. However, we also believe it timely to promote the sharing and reuse of catalogue contents to enable data consumers to navigate the catalogue contents more easily, enabling integrated or cross catalogue searches, reduce duplicated efforts, improve quality and completeness and ease sustainability of cataloguing contents in case the original catalogue hosting is discontinued.
Clear Data Licence Without a licence that explicitly allows metadata sharing across catalogues, copyright laws and database laws will prohibit it and it might be even unclear who would be the legal entity to approach for permission. General copyright law will reserve all rights to the creator, which depending on the local legislation, might be the creator of the catalogue or the original content provider whose data is being catalogued or a combination thereof. Similarly, database laws will reserve rights to a structured database to the creator of that database.
Obviously, links back to the aggregator must then be shown so that the aggregator is also rewarded for its efforts, with the potential for more users and greater impact. An example of this is being developed in EUCAN-connect, where crosssearch between the Maelstrom, LifeCycle, Birth cohorts and RECAP Preterm catalogues is being enabled [https://catalogue. eucanconnect. eu/]. Another example of this is being piloted in the European Joint Programme for Rare Disease [https://github. com/ejp-rd-vp], where a 'search widget' has been developed to enable database users to do 'live' searches that also include the other databases in their network, via a single user interface.
Interoperability Recommendations To implement interoperability across catalogues, technical standardisation is also desirable. Practically, while catalogues are usually proposed as a method to implement FAIR principles for datasets, the contents of the catalogue itself must also adhere to the FAIR principles  [6]. In the past decade, converging recommendations have emerged to improve metadata catalogues [FAIRsFAIR on metadata catalogue interoperability, https://zenodo. org/record/5744913#. YeE-B3VjMLzc]. Also, recent projects in the context of the European Open Science Cloud (EOSC), the European Health Data Space (EHDS), and Research Data Alliance (RDA) produce relevant recommendations, such as via the TEHDAS (towards European health data space) project [https://tehdas. eu] and the SHAring Rewards and Credit Interest Group  [36]  with practical recommendations, FAIR assessment templates, and lessons learned such that FAIR criteria should be implemented gradually, and that training, support and rewards should be considered.
Persistent Identifiers When data records are copied across catalogues, it is essential that data curators can check where the original records reside. In addition, the same resource (cohort, data bank) might be catalogued multiple times with different names/acronyms. Persistent identifiers such as the DOIs or Handle Identifiers could be used to unambiguously identify all these different types of records. These systems provide a separation between the identifier and the location of the (digital) object that they refer to and have agreements to maintain the service in the long term. The identifiers should be opaque and unique, where the structure and the content of the identifier is devoid of any significance about the object that they describe. This allows the identifier to stay the same, while the object that it describes can change.
For instance, if we use the name of a data bank as an identifier, this causes problems when the data bank changes its name and website or if another organization creates a new data bank with the same name. Any catalogue that uses the identifier can use it to retrieve the current name and link to the website and maintain all references to the data bank. At the same time, a catalogue system can easily tell the two data banks apart based on their different identifiers. There are many persistent identifier services available starting for a few hundred dollars a year, however the costs are in identity maintenance. We envision that large organisations, such as the European Medicine Agency via their ENCePP system [https://www. encepp. eu/] and BBMRI-ERIC via their biobank directory [http://directory.
Common Data Elements and Ontologies As described above, we can easily observe overlap between the catalogues. Also, we believe that a combination of Maelstrom, LifeCycle, BBMRI-ERIC and MINERVA provides the coverage that can provide a basis to standardise these concepts. However, it would be very labour-intensive to search, compare and combine the contents from multiple catalogues manually today. Standardisation in using the same metadata elements and code systems/ontologies where possible can greatly alleviate this challenge. And ideally, metadata items should be coded inputs, using values from an ontology, instead of free-text that allows non-standard metadata values (although in our experience, there is added value for free-text in descriptive notes, and to chart candidate codes before a code system has been established).
In recent years, general agnostic catalogue standards have emerged, but they lack details about the domain (in our case, cohorts, RW data). Recommended standards include DCAT [Data Catalogue -https:// www. w3. org/TR/vocab-dcat-2/], and a specific implementation thereof called FDP [FAIR data point, https://www. fairdatapoint. org/]. DCAT defines: (1) the 'catalog' which is a dataset in which each individual item is a metadata record describing some resource; (2) the 'resource' which represents a dataset, a data service or any other resource; (3) the 'dataset', a collection of data published or curated by a single agent; (4) the 'distribution' and 'data service' that provide e. g. download file and/or operations via programming interfaces, and (5) the 'catalogue record' primarily concerning the registration information, such as who added the item and when.
For example, Maelstrom could use DCAT to describe each cohort and network as a 'resource' (including details such as the id, title, landing page, issuer) and use 'datasets' to detail datasets within the cohorts (including details such as the title, keywords, contact point) including links how to access additional information such as lists of variables (as 'distribution'). In addition, we want to mention BioSchemas [https://bioschemas. org/]  [40] , an extension to schema. org used by Google and other search engines that provides concepts such as DataCatalog with properties such as description, keywords, provider, citation, licence, variableMeasured. However, to be of real value more specific details would be needed as described in section 3. Therefore, ideally the catalogue community would come together to define common data elements and code systems for concepts.