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#dataPaper

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Alexander Schniedermann

Remember Ludwig Fleck's socio-intellectual forms of science? In 1935, he described the novelty of findings along different document types that can be found in science. Today, many links between document types and intellectual functions can be identified. Think of #Preprints, #SystematicReviews #MetaAnalysis #Guidelines #Handbooks #SoftwarePaper #DataPaper etc.
(I put a little typology here: arxiv.org/abs/2406.15154)

arXiv.orgAnalysis of the Publication and Document Types in OpenAlex, Web of Science, Scopus, Pubmed and Semantic ScholarThis study compares and analyses publication and document types in the following bibliographic databases: OpenAlex, Scopus, Web of Science, Semantic Scholar and PubMed. The results demonstrate that typologies can differ considerably between individual database providers. Moreover, the distinction between research and non-research texts, which is required to identify relevant documents for bibliometric analysis, can vary depending on the data source because publications are classified differently in the respective databases. The focus of this study, in addition to the cross-database comparison, is primarily on the coverage and analysis of the publication and document types contained in OpenAlex, as OpenAlex is becoming increasingly important as a free alternative to established proprietary providers for bibliometric analyses at libraries and universities.
NIOO-KNAW

New NIOO publication: Sampling data of macro-#invertebrates collected in #grasslands under restoration succession in a lowland stream-valley system. #biodiversity #datapaper #Drenthe #carabidae #pyramidtraps #pitfalls
doi.org/10.3897/BDJ.12.e125462

Biodiversity Data JournalSampling data of macro-invertebrates collected in grasslands under restoration succession in a lowland stream-valley systemPublication of data from past field studies on invertebrate populations is of high importance, as there is much added value for them to be used as baselines to study spatiotemporal population and community dynamics in these groups. Therefore, a dataset consisting of occurrence data on epigaeic invertebrates collected in 1996 was standardised into the Darwin core format and cross-checked in order to make it publicly available following FAIR data principles. With publication, it can contribute to the biodiversity assessment of terrestrial invertebrates, thereby improving the availability and accessibility of much-needed historical datasets on macro-invertebrates.Here, we present sampling event data on invertebrates from four grasslands taken out of agricultural production over the span of several decades, effectively displaying a chronosequence on the effects of agricultural extensification. The data were collected by means of a standardised sampling design using pyramid traps, pitfall traps and soil samples.The raw data presented in this data paper have not been published before. They consist of 20,000+ records of nearly 70,000 specimens from 121 taxonomic groups. The data were collected using a standardised field study set-up and specimens were identified by taxonomic specialists. Most groups were identified up to family level, with eight groups identified up to species level. The occurrence data are complemented by information on plant composition, meteorological data and soil physical characteristics. The dataset has been registered in the Global Biodiversity Information Facility (GBIF): http://doi.org/10.15468/7n499e
Continued thread
Kathe Todd-Brown

This is a bit of an odd duck as a #DataPaper. Traditional research articles showcase some new development in the field and connect it to a reproducible line of evidence. #DataPapers on the other hand are relatively new and focus on the data collection where the data itself is the main development. The promise here is that the data is broad enough and robust enough to be of general interest to other researchers... let's dig in. 3/n

Continued thread
Annika Rockenberger (she/they)

Read about #NorKorr on our blog norkorr.hypotheses.org/, check out our code, scripts, and data on GitHub github.com/norkorr and our project bibliography on Zotero zotero.org/groups/2214573/nork.
We have a #DataPaper in the making, focusing on the #EdvardMunch correspondence, and we plan to submit a paper to the upcoming #DHNB2024 conference!

norkorr.hypotheses.orgNorKorr – Norwegian Correspondences – Linking Letters in Norwegian Collections
Jose

So we have a new #datapaper out on #biophysical & #agronomic parameters for #maize in #Ghana. This paper will be useful for folk doing #RemoteSensing #EarthObservation #CropModelling in #smallholder #agriculture systems
doi.org/10.5194/essd-14-5387-2

essd.copernicus.orgLocation, biophysical and agronomic parameters for croplands in northern Ghana<p><strong class="journal-contentHeaderColor">Abstract.</strong> Smallholder agriculture is the bedrock of the food production system in sub-Saharan Africa. Yields in Africa are significantly below potentially attainable yields for a number of reasons, and they are particularly vulnerable to climate change impacts. Monitoring of these highly heterogeneous landscapes is needed to respond to farmer needs, develop an appropriate policy and ensure food security, and Earth observation (EO) must be part of these efforts, but there is a lack of ground data for developing and testing EO methods in western Africa, and in this paper, we present data on (i) crop locations, (ii) biophysical parameters and (iii) crop yield, and biomass was collected in 2020 and 2021 in Ghana and is reported in this paper. In 2020, crop type was surveyed in more than 1800 fields in three different agroecological zones across Ghana (the Guinea Savannah, Transition and Deciduous zones). In 2021, a smaller number of fields were surveyed in the Guinea Savannah zone, and additionally, repeated measurements of leaf area index (LAI) and leaf chlorophyll concentration were made on a set of 56 maize fields. Yield and biomass were also sampled at harvesting. LAI in the sampled fields ranged from 0.1 to 5.24 m<span class="inline-formula"><sup>2</sup></span> m<span class="inline-formula"><sup>−2</sup></span>, whereas leaf chlorophyll concentration varied between 6.1 and 60.3 <span class="inline-formula">µ</span>g cm<span class="inline-formula"><sup>−2</sup></span>. Yield varied between 190 and 4580 kg ha<span class="inline-formula"><sup>−1</sup></span>, with an important within-field variability (average per-field standard deviation 381 kg ha<span class="inline-formula"><sup>−1</sup></span>). The data are used in this paper to (i) evaluate the Digital Earth Africa 2019 cropland masks, where 61 % of sampled 2020/21 cropland is flagged as cropland by the data set, (ii) develop and test an LAI retrieval method from Earth observation Planet surface reflectance data (validation correlation coefficient <span class="inline-formula"><i>R</i>=0.49</span>, root mean square error (RMSE) 0.44 m<span class="inline-formula"><sup>2</sup></span> m<span class="inline-formula"><sup>−2</sup></span>), (iii) create a maize classification data set for Ghana for 2021 (overall accuracy within the region tested: 0.84), and (iv) explore the relationship between maximum LAI and crop yield using a linear model (correlation coefficient <span class="inline-formula"><i>R</i>=0.66</span> and <span class="inline-formula"><i>R</i>=0.53</span> for in situ and Planet-derived LAI, respectively). The data set, made available here within the context of the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) initiative, is an important contribution to understanding crop evolution and distribution in smallholder farming systems and will be useful for researchers developing/validating methods to monitor these systems using Earth observation data. The data described in this paper are available from <span class="uri">https://doi.org/10.5281/zenodo.6632083</span> <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx38">Gomez-Dans et al.</a>, <a href="#bib1.bibx38">2022</a>)</span>.</p>
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