dataesr/scientific-paragraphs-categorization huggingface dataset:
2016). Further, even when researchers are willing to adopt common data standards, they might lack the technical knowledge or time. Yet, inspiring examples of overcoming these barriers exist. For example, all national bird ringing schemes originally used their own data storing format, but ultimately agreed on one common output format, creating the European Union for Bird Ringing (EURING,  du Feu et al. 2016). Now, all bird ringing data can be brought together at the European level. The core practical obstacles to effective data reuse and collaboration are the lack of: i) meta-data standards to describe populations, ii) data standards, and iii) a central registry of all the populations  (Culina et al. 2018b ). To find datasets, researchers commonly search the published literature, then contact data owners (who are not always readily reachable, e.
Accepted Article © 2020 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society specific conditions of data collection (e. g. specific field protocols, ecologically relevant conditions), the data structure and vocabularies. Groups/researchers store data in different types of databases and formats, use different vocabularies to name data elements (e. g. different languages) or different coding for the same data element (e. g. some record hatching date as day 1, others as day 0). Thus, data owners usually require much time to extract and compile the data and provide meta-data to meet the user's needs. This process needs to be repeated for each new collaborative project. Reformatting data is not only time consuming, but may also increase the risk of introducing errors.
Cultural and technical barriers must be solved in parallel; and until open data practices become the norm and researchers recognise their benefits, it is crucial to encourage and enable proper data archiving and establish meta-data and data standards. To achieve FAIR data, and to increase and facilitate collaboration and data synthesis, we created Studies of Populations of Individuals -Birds (SPI -Birds) Network and Database. To overcome cultural barriers, we opted for an approach where data owners can decide to keep full control over the use of their data or can make their data open access. This approach where some data are open access and some are not has also been previously successful with the Movebank,  (Kranstauber et al. 2011). Data owners also keep their way of data management (i. e.
how they organize their data) and SPI-Birds converts these primary data into a standard (FAIR) format. All meta-data stored by SPI Birds are open access, as well as the code to convert primary data into the standard format. To date, we count more than 120 members from 21 countries, monitoring over 80 populations of 19 species (Fig. 1 , Fig. 2A ). Currently, the majority of the species are hole-nesting passerines (Table  1 ), but as a part of our long-term goal, we are actively reaching out to researchers that work on other species groups across the world, and so far have incorporated some of these into the database (e. g. owls, seabirds, dunnocks). The only requirement that needs to be met is that most of the birds in a population can be individually recognized (which is commonly achieved by a metal or coloured rings) and that at least one component of the breeding success of these individuals (e.
SPI-Birds [Insert Fig1 here]  primary data format, Box 2) into the standard format. We hope that this standard format will be adopted by both new and existing research groups to archive their data. The existing groups will be more likely to start using the standard format once their old data have been converted into it by SPI-Birds. We further plan to extend this format (and create new standards) to accommodate other information (e. g. genetic, hormonal, colouration, ecotoxicological, behavioural data). Currently, each population's meta-data clearly indicate whether this additional information has been collected, and the corresponding data can be stored at SPI-Birds (although not yet standardized). For example, physiological or personality data has been collected in almost 30 populations (Fig.
Accepted Article © 2020 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society discussion of how fields in the primary data are coerced into corresponding fields in the standard format) to ensure maximum accuracy. Pipeline construction sometimes requires several iterations before an accurate pipeline is created. At this point, the pipeline can be confidently applied. The primary data and data in the standard format are stored within the secure SPI-Birds data hub (Fig. 3A ), at the file server cluster of the Netherlands Institute of Ecology (NIOO-KNAW) and backed up seven times a week. The pipelines are publicly available via GitHub. [Insert Fig3 here] For all populations with ongoing data collection primary data are updated to a new version every year and may include not only additional data collected over the additional year but also corrections of errors found in earlier data.
Understanding data and their limitations As discussed above, datasets come with errors and limitations. While SPI-birds increases data integrity and quality, the standard data may still contain errors, and data from different populations might still not be entirely comparable. To enable users to understand how the primary data and standardized data were derived and to highlight potential limitations in the dataset, we provide several documents (as a part of the meta-data). These include the description of the study site (e. g. location, size, habitat type), data collection protocols, and the list of any initial quality checks on the primary data conducted by a data owner. This way, users can better understand how the primary data were derived. Next, we provide a detailed description of decisions and assumptions made during the conversion of data from primary to standard format (with all the pipelines openly available via GitHub), details on quality checks conducted by SPI-Birds and the resulting quality report.
Finally, we publish a series of 'technical reports' on the SPI-Birds website, where we discuss a range of topics related to methodological conventions (e. g. conversion from one type of tarsus measurement method to another type) and potential biases induced by methodological approaches to data collection (e. g. impact of the frequency at which nests are checked on the estimation of laying date, the impact of nest box design on the vital rates, such as survival of young,  Lambrechts et al. 2010).
Lessons learnedcreating a community data hub The need to adopt global meta-data and data standards in ecology and evolution is growing  (Poisot et al. 2019 , Schneider et al. 2019). We strongly believe, and our example supports, that the best way to achieve the adoption of global standards is to first create standards for welldefined communities  (Poisot et al. 2019)  We suggest four key points in establishing a common database and community data standards in the long-tail of science: (1) How to start. Aim to identify researchers/groups that belong to your research community. This is largely a snowballing processonce you locate several members, ask them to identify others. Ideally, organize a kick-off meeting to discuss the aims, distribution of tasks, and further steps. From our experience, it is important to have at least several research groups keen on the project at the start.
Accepted Article © 2020 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society of our continuous growth. Third, organise workshops/meetings where the community physically (or virtually) comes together. (3) Funding. Plan to allow for different funding scenarios. We find that it is best to plan finances in steps (if no long-term large funding is available at the very start). Make sure that the first stepwhat you want to achieve at the minimumis financially covered at the start. This must include securing a permanent, long-term platform to archive the datasets. After that, plan in five-year (or similar) steps. Here, make sure that in the worst-case scenario (no further funding secured) each step is maintainable with a minimal financial and personnel commitment.
For example, our first step was to integrate data on hole-nesting passerines in Eurasia, and this period was financially covered by a grants held by participating individuals, and volunteer contributions from several members. After this initial phase the SPI-Birds database can be kept functional with a minimal investment (e. g. storage capacity). In the next step, we plan to increase our scope. At this stage, our project has already proven successful, which makes it more attractive for longer-term support (e. g. European open science funds). Finally, we ask (but do not mandate) that those whose research plans rely on the collective power of datasets hosted at SPI-Birds to allocate some of their resources to the SPI-Birds initiative. Vision for an integrated future SPI-Birds is a large-scale initiative that integrates data on individual-based studies of breeding birds and connects researchers that collect data in these populations.
Accepted Article © SPI-Birds can also serve as a platform to enable better resource allocation between research groups. For example, while a data owner might have the data, they might lack funds to analyse them. On the other hand, a data user might have funds or even apply for funds based on these data. SPI-Birds can thus help pull the resources (data and funds) together, thereby enabling scientific projects, and progress, where it may otherwise be unlikely to occur. We also encourage use of SPI-Birds data in student projects. Finally, during the unforeseen international crisis, such as caused by a novel corona virus during writing of this contribution, SPI-Birds provided an excellent platform to update and document field situations and to mitigate the unbalanced effects of the crisis on research groups.
(c) Comparisons of evolutionary potential Evolutionary potential depends on the genetic architecture of traits. From a quantitative genetics perspective, this architecture is summarized in G, the additive genetic (co)variance matrix. Comparisons of evolutionary potential across populations or species enable us to evaluate the generality of evolutionary constraints  (Agrawal & Stinchcombe, 2009)  and to gain insight into the evolution of the underlying genetic architecture  (Steppan, Phillips, & Houle, 2002 , McGlothlin et al. , 2018). For example, using longterm datasets with pedigree information,  Teplitsky et al. (2014)  assessed the expected constraints on evolutionary responses of morphological traits in ten populations of seven wild bird species. Based on estimated G matrices and selection gradients for four morphological traits, their results suggest that genetic correlations may reduce the expected rate of evolution by 28% on average, even for traits such as morphological traits, that are generally thought to have a high evolutionary potential.
In terms of the evolution of genetic architecture,  Delahaie et al. (2017)  showed that the genetic architecture of life history and morphological traits is relatively conserved across populations of blue tits Long tail of science -Dispersed scientific research that is conducted by many individual researchers/teams. Data produced in the long tail tend to be small in volume, and less standardized within the same field of study. The majority of scientific funding is spent on this type of research. Open Data -Data that anyone is free to use, reuse and redistributesubject, at most, to the requirement to attribute or share-alike, https://creativecommons. org/licenses/by-sa/2. 5/ FAIR data -FAIR data are equivalent to open data. FAIR data are structured and described in a way that supports their Findability, Accessibility, Interoperability, and Reusability, for both machines and humans.
Meta-data -Data that describe datasets. Meta-data comprise information explaining the purpose and origin of data, methods used to acquire them, the structure of the data, time references, geographical location, brief description of the study site(s), creator, access conditions and terms of use. Data owner -A person or institution that has collected the raw data and/or is hosting the primary data. Data user -A person interested in using the data owned by the data owner. Data owners can be data users of someone else's data. Raw data -Data as collected in the field. Primary data -Data stored locally by each research group. Primary data might differ from raw data because of (1) errors made during transcribing raw data into primary data or (2) correction of obvious errors in raw data during transcribing them into primary data (3) primary data contain some derivate of.
Connecting researchers and data SPI-Birds Network and Database (www. spibirds. org) is a grassroots initiative that connects researchers working on populations of birds in which individuals are uniquely marked, and thus can be recognized (at capture, or by sight). The main goals are to: (1) increase the coordination Accepted Article © 2020 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society and collaboration between research groups; (2) host the registry of populations and equalise the visibility among research groups; (3) buffer against data loss and provide long-term access to datasets; (4) ensure data quality and integrity; and (5) facilitate data use, and give appropriate credit for data use. To achieve these goals we: (a) derive meta-data attributes that describe populations (Box 2); (b) centrally archive version-controlled primary data from research groups, with attached conditions of data use; (c) derive data standards with controlled vocabularies and convert primary data format into a standard format; (d) conduct data quality checks; (e) run a series of technical reports on the impact that protocols for data collection may have on derived variables; (f) provide expert advice to researchers setting up new populations; and (g) provide an online interface to find and request data, and maintain outreach activities.
Data use: discovery, provisioning, terms of usePopulations hosted at SPI-Birds can be searched via SPI-Birds website based on meta-data (e. g. species studied, country, length of data collection, variables measured). Once the relevant populations have been identified, data can be requested using the SPI-Birds request form(Fig   3C). Unless the data owners have made their data fully open access, data requests are sent for approval to the data owner. If approval is given, standardized data from the requested population(s), accompanied by the standard quality check report, are delivered to the user, and the data owner is informed about the data sharing. When the user requests multiple datasets, all datasets (in a standard format) are compiled and sent to the user. Each dataset comes with a specific terms of data use, and these are stated in its meta-data.
To give credit to those who have been collecting/managing the raw data, as a minimal requirement for data use (i. e. even when the data owner does not request any other conditions of data use) we ask that the data owner(s) and funding source(s) that they state in the meta-data, are explicitly acknowledged upon data use (e. g. in the acknowledgment section of an article). We also require acknowledgment of the SPI-Birds Network and Database, and citation of this paper. Further, SPI-Birds encourage citation of the Accepted Article © 2020 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society dataset source (or related publication) via DOIs (digital object identifiers). The detailed Data.
2020 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society further developments of the database we plan to: (1) cover additional populations, species, and a wider geographical area; (2) integrate and standardize other data types (e. g. hormonal, behavioural); (3) connect with ongoing centralized efforts to map the full spectrum of different types of data on birds that can complement each other. Here, the main collaborators are scientific groups that centralize the collection of complementary types of data (e. g. Movebank, Kranstauber et al. 2011, Fiedler and Davidson 2012; EURING, du Feu et al. 2016; the great tit HapMap project, Spurgin et al. 2019). Within this scope, we can connect individual-level data hosted at SPI-Birds to other types of data on the same individuals based on their unique ID and provide even more comprehensive information on individuals across their full life-cycle.
(d) Resolving methodological issuesLong-term individual level studies often vary in protocols, applied methodologies and approaches to data collection. Using many long-term datasets may help identify such variation, and point towards those variables that can have potentially significant impacts on how results are interpreted, especially at the between-study level. Møller et al. (2014)  targeted one important, strongly varying component of long-term hole-nesting bird studies: nest-box design. Their study included reproductive data of four bird species: blue and great tits, and pied and collared flycatchers. They have found a positive relationship between nest-box floor area and clutch size in great tits, and between box material (wood vs. concrete) and clutch size in blue tits. These results indicate that variation in study design at the between-population level shouldAccepted Article© 2020 The Authors.
: Connecting researchers and data SPI-Birds Network and Database (www. spibirds. org) is a grassroots initiative that connects researchers working on populations of birds in which individuals are uniquely marked, and thus can be recognized (at capture, or by sight). The main goals are to: (1) increase the coordination Accepted Article © 2020 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society and collaboration between research groups; (2) host the registry of populations and equalise the visibility among research groups; (3) buffer against data loss and provide long-term access to datasets; (4) ensure data quality and integrity; and (5) facilitate data use, and give appropriate credit for data use. To achieve these goals we: (a) derive meta-data attributes that describe populations (Box 2); (b) centrally archive version-controlled primary data from research groups, with attached conditions of data use; (c) derive data standards with controlled vocabularies and convert primary data format into a standard format; (d) conduct data quality checks; (e) run a series of technical reports on the impact that protocols for data collection may have on derived variables; (f) provide expert advice to researchers setting up new populations; and (g) provide an online interface to find and request data, and maintain outreach activities.
This standard format is described in detail on the SPI-Birds GitHub repository (Culina et al. 2019). It is designed to cover the data fields that are common across most individual-based bird studies, and is aligned with the (Wilkinson 2016)9ed by the Ecological Meta-Data Language (EML,Jones et al. 2019) and the principles of FAIR data(Wilkinson 2016). The standard format is dynamic and can be furtherAccepted Article© 2020 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society extended or adjusted to accommodate the breeding biology (e. g. cooperative breeders) of species yet to be included into the database. SPI-Birds creates tailored pipelines to convert data from each research group/contributor (i. e.
Identification of Candidate Genes in the QTL Region Responsible for Rc3 Sperm Phenotype We explored the Mafq1 interval to identify candidate genes whose presence in the spretus haplotype within a B6 background could be responsible for the hypofertility phenotype. Table S1 compiles all sequences described in different databases as protein coding genes (n = 71), processed transcripts (n = 37), pseudogenes (n = 50) and non coding RNAs (n = 48). We also documented their expression profile, when available, at the RNA and protein level, in order to focus on genes expressed in the testis and preferably at the spermatid stage to explain the dysfunction of the acrosome. Some genes have already been invalidated in mouse models (n = 28), and although some of these knockout (KO) models expressed phenotypes that are overall related to reproduction, notably, none of them could recapitulate the particular features observed in the Rc3 mouse.
The supposed or validated function of the protein has also been documented in the literature. Finally, we counted the single nucleotide polymorphisms (SNPs) with putative functional impact between spretus and B6, particularly in the open reading frames where they could be responsible for structural changes affecting protein interactions. Therefore, we applied these filters to select the most relevant candidates genes. We identified 8 genes highly expressed in the testis: Trip12, Fbxo36, Spata3, Tex44, Pde6d, Efhd1, Dnajb3 and Mroh2a, of which two are exclusively expressed in the testis (Spata3 and Tex44). Because the differences in the expression level (mRNA) depended on the databases, we performed qRT-PCR experiments in order to analyze the expression of several candidate genes in the testis.
One of these candidate genes could be related to the Hipk4, Sirt1 or Dpy9l2 genes, whose invalidation affects acrosome biogenesis in mice or humans. The production and fine genetic characterization of partially overlapping strains defined a genetic interval associated with the above-described phenotypes. The number of genes is important in this interval (71 coding genes); therefore, we then applied several filters in order to prioritize those that are more likely to cause the phenotypes observed during our study. According to the expression database that was consulted, 22 genes out of a total of 71 are reported to be expressed in the testis (see Table  S1 ). The inactivation of 14 of them did not result in spermatogenesis defects (for references, see Table  S1 ). Of these 14 genes, the case of Dnajb3 is questionable.
The testicular expression of the Glrp1 gene is described in one database but not in another and no orthologous gene is described in humans. The filters that we applied make it possible to exclude some genes such as those that are not expressed in the testis, those for which the KO does not show a spermatogenesis phenotype and even those without significant SNPs. These filters make it possible to highlight two genes that are probably involved in the observed phenotype. These are Spata3 and Tex44, which present an expression profile restricted to the testis, and particularly in the spermatid. Our verification by qRT-PCR indicated the post-meiotic expression (at the spermatid stage) of Spata3, which confirmed the data found in the expression databases. The expression profile at the protein level given by Protein Atlas indicates their presence only at the spermatid stage in human testis.
Phenotyping by High Frequency Ultrasonography Evaluation of in Vivo Fertilization To evaluate the implantation rate, mice from B6 (reference strain) and Rc strains were crossed and phenotyped at the small animal imaging facility of the Cochin Institute using high frequency ultrasonography (VEVO 770, Visulasonics, Toronto, Canada). Each female was used one time to collect phenotypic data from the primo-gestation. Briefly, a chemical hair remover was used to eliminate abdominal hair. Ultrasonographic contact gel was used to ensure contact between the skin surface and the transducer. Body temperature, electrocardiographic and respiratory profiles were monitored using the device's integrated heating pad and monitoring device (THM150, Indus Instruments, Webster, TX, USA). The implantation rate was determined early in the gestation, at E7.