cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18710/iljy4u
title: Background data for: An analysis of bar chart usage in corpus data visualization

description: &lt;p&gt;&lt;b&gt;Dataset description&lt;/b&gt;&lt;/p&gt;
&lt;p&gt;This dataset contains information about the use of bar charts for corpus data presentation. It is based on a systematic review covering all papers (&lt;i&gt;n&lt;/i&gt; = 1,183) published in five corpus-linguistic journals up to and including the year 2024 (&lt;i&gt;International Journal of Corpus Linguistics, Corpus Linguistics and Linguistic Theory, Corpora, Research in Corpus Linguistics, and the International Journal of Learner Corpus Research&lt;/i&gt;). In total, the report pool includes &lt;i&gt;n&lt;/i&gt; = 1,061 unique instances of bar charts, and the current dataset documents various properties of these diagrams, including information about their content (e.g. number and types of variables, number of levels per variable) and design (e.g. type of bar chart, scaling information, size, use of color). Further, information is provided about the frequency of use of different types of statistical graphs and statistical tables for each article (&lt;i&gt;n&lt;/i&gt; = 1,183) in the report pool.&lt;/p&gt;
description: &lt;p&gt;&lt;b&gt;Abstract: Related publication&lt;/b&gt;&lt;/p&gt;
&lt;p&gt;A recent survey of graph usage in corpus-based research has shown that the bar chart is the most widely used graph type for corpus data presentation. Motivated by this finding, the present paper offers a systematic review of bar chart usage in corpus-based research articles. It covers all papers (&lt;i&gt;n&lt;/i&gt; = 1,183) published in five corpus-linguistic journals up to and including the year 2024 (&lt;i&gt;International Journal of Corpus Linguistics, Corpus Linguistics and Linguistic Theory, Corpora, Research in Corpus Linguistics, and the International Journal of Learner Corpus Research&lt;/i&gt;). The aim of this survey is to arrive at a better understanding of the kinds of visualization tasks imposed on bar charts. We observe that they most commonly show percentages or absolute/normalized frequencies, and that they are often used for relatively complex visualization tasks involving multiple variables and subgroups. The survey is carried out against the backdrop of known limitations of this graph type and design recommendations found in data visualization guidebooks. Our critical examination of diagrams pays attention to issues that compromise the ability of the viewer to accurately perceive patterns in the data, and minor issues that affect the efficiency of a display. These observations are then distilled into a set of concrete recommendations, which are grounded in current usage and the advice given in the data visualization literature.&lt;/p&gt;
description: MAXQDA, 22.5.0
DOI: 10.18710/t3c1eu/qed7iq
title: 00_ReadMe.txt
DOI: 10.18710/t3c1eu/zfzbzh
title: Stations.txt
DOI: 10.18710/u4raej/mu1jah
title: Hurtigruten_Reports_Metadata.csv

description: Structured metadata of Hurtigruten Group’s annual and ESG reports (2017–2023), including title, year, report type, responsible entity, internal filenames, and verification of download links. Used to trace corporate sustainability narratives in comparison to media discourse.
DOI: 10.18710/vyqfpk
title: Replication Data for: Long-term homogenization of Fennoscandian heathland and tundra vegetation is connected to the expansion of an allelopathic dwarf shrub

description: This dataset contain plant community data (including vascular plants, bryophytes and lichens) from 275 vegetation plots from northern Fennoscandia (northern Finland and northern Norway). Values for each species are percentage cover estimates from 2 x 2 meter plot. Each plot has been surveyed twice, i.e., species have old and new percentage cover from 1964-75 and from 2013-23.

We analyzed temporal changes in community composition and diversity across the study area and in different biogeographic zones, continentality-humidity classes and habitat types. We found a strong homogenization trend across the study area, with plant communities becoming more similar in composition over the decades when all taxa were treated together. The observed homogenization was driven especially by the increased similarity of vascular plant and lichen communities and was largely independent of biogeographic zones or continentality-humidity gradient. Homogenization was particularly associated with the drastic encroachment of the evergreen dwarf shrub Empetrum nigrum in habitat types originally dominated by other species, and with the decrease in lichen cover. In general, our findings suggest that Fennoscandian heathland and tundra vegetation is transforming towards a more homogeneous evergreen dwarf shrub-dominated system, which may threaten ecosystem multifunctionality. Our results highlight the importance of exploring biodiversity among different metrics and growth forms to understand the overall changes in heathland and tundra biodiversity.
description: R statistical environment, 4.4.1
DOI: 10.18710/dqjwab/a8no2j
title: Final_demo_2020_P_Euc_E49_N24.tif
DOI: 10.18710/dqjwab/euvgtw
title: Final_demo_2020_stdev_P_Euc_E54_N14.tif
DOI: 10.18710/dqjwab/fzdq7j
title: Final_demo_2020_P_Euc_E44_N54.tif
DOI: 10.18710/dqjwab/hcfkob
title: Final_demo_2020_stdev_P_Euc_E44_N29.tif
DOI: 10.18710/dqjwab/j9h9xl
title: Final_demo_2020_P_Euc_E54_N14.tif
DOI: 10.18710/dqjwab/jkgivs
title: Final_demo_2020_stdev_P_Euc_E34_N24.tif
DOI: 10.18710/dqjwab/khm4e7
title: Final_demo_2020_stdev_P_Euc_E49_N29.tif
DOI: 10.18710/dqjwab/sijooq
title: Final_demo_2020_stdev_P_Euc_E49_N24.tif
DOI: 10.18710/dqjwab/sxbata
title: Final_demo_2020_P_Euc_E34_N24.tif
DOI: 10.18710/dqjwab/tfebbv
title: Final_demo_2020_stdev_P_Euc_E39_N39.tif
DOI: 10.18710/dqjwab/u0rtet
title: Final_demo_2020_P_Euc_E59_N14.tif
DOI: 10.18710/dqjwab/zcbsk5
title: Final_demo_2020_P_Euc_E64_N14.tif
DOI: 10.18710/hdjrjn/a92mcp
title: Final_demo_2020_P_OthBL_E34_N29.tif
DOI: 10.18710/razr4o/ksc5xt
title: Final_demo_2020_P_Bet_Car_Fra_E39_N39.tif
DOI: 10.18710/caf7ke/jzxv8b
title: Final_demo_2020_P_Fag_E49_N49.tif
DOI: 10.18710/d9mh96/ldl9im
title: Final_demo_2020_P_Pic_E49_N19.tif
DOI: 10.18710/d9mh96/q8xlen
title: Final_demo_2020_stdev_P_Pic_E44_N24.tif
DOI: 10.18710/hdjrjn/d2l3g1
title: Final_demo_2020_P_OthBL_E44_N24.tif
DOI: 10.18710/hdjrjn/zyswol
title: Final_demo_2020_stdev_P_OthBL_E29_N24.tif
DOI: 10.18710/hov3kb/1h8phs
title: Final_demo_2020_P_Con_E49_N54.tif
DOI: 10.18710/hov3kb/2mcxky
title: Final_demo_2020_P_Con_E49_N29.tif
DOI: 10.18710/hov3kb/4opscj
title: Final_demo_2020_P_Con_E29_N19.tif
DOI: 10.18710/hov3kb/7502xg
title: Final_demo_2020_stdev_P_Con_E39_N19.tif
DOI: 10.18710/hov3kb/e9fsey
title: Final_demo_2020_stdev_P_Con_E29_N14.tif
DOI: 10.18710/hov3kb/hs7z7w
title: Final_demo_2020_P_Con_E49_N44.tif
DOI: 10.18710/hov3kb/je3fh7
title: Final_demo_2020_P_Con_E44_N34.tif
DOI: 10.18710/hov3kb/odoqpq
title: Final_demo_2020_stdev_P_Con_E39_N24.tif
DOI: 10.18710/hov3kb/wxekvg
title: Final_demo_2020_stdev_P_Con_E29_N24.tif
DOI: 10.18710/hov3kb/xxkkkk
title: Final_demo_2020_stdev_P_Con_E54_N44.tif
DOI: 10.18710/hov3kb/zrhimj
title: Final_demo_2020_P_Con_E39_N19.tif
DOI: 10.18710/razr4o/edp36e
title: Final_demo_2020_P_Bet_Car_Fra_E29_N39.tif
DOI: 10.18710/razr4o/mcdbpv
title: Final_demo_2020_P_Bet_Car_Fra_E39_N14.tif
DOI: 10.18710/caf7ke/lzk4t3
title: Final_demo_2020_stdev_P_Fag_E49_N44.tif
DOI: 10.18710/g1pdbl/dbzzl7
title: Final_demo_2020_P_Pop_Sal_E54_N24.tif
DOI: 10.18710/g1pdbl/v2prg2
title: Final_demo_2020_P_Pop_Sal_E24_N14.tif
DOI: 10.18710/g1pdbl/vw1eeo
title: Final_demo_2020_stdev_P_Pop_Sal_E49_N14.tif
DOI: 10.18710/g1pdbl/zyq4fm
title: Final_demo_2020_stdev_P_Pop_Sal_E44_N39.tif
DOI: 10.18710/hdjrjn/ldfrnp
title: Final_demo_2020_stdev_P_OthBL_E29_N29.tif
DOI: 10.18710/hdjrjn/parfv1
title: Final_demo_2020_P_OthBL_E49_N49.tif
DOI: 10.18710/hdjrjn/ss9ozx
title: Final_demo_2020_P_OthBL_E49_N14.tif
DOI: 10.18710/hov3kb/lddjaw
title: Final_demo_2020_stdev_P_Con_E49_N14.tif
DOI: 10.18710/razr4o/3bswd8
title: Final_demo_2020_P_Bet_Car_Fra_E44_N19.tif
DOI: 10.18710/razr4o/3jzdmg
title: Final_demo_2020_stdev_P_Bet_Car_Fra_E54_N29.tif
DOI: 10.18710/razr4o/f6ribg
title: Final_demo_2020_P_Bet_Car_Fra_E44_N14.tif
DOI: 10.18710/razr4o/kg9idl
title: Final_demo_2020_P_Bet_Car_Fra_E34_N39.tif
DOI: 10.18710/razr4o/oztphs
title: Final_demo_2020_P_Bet_Car_Fra_E39_N34.tif
DOI: 10.18710/razr4o/td7pnp
title: Final_demo_2020_P_Bet_Car_Fra_E54_N24.tif
DOI: 10.18710/razr4o/wmfunw
title: Final_demo_2020_P_Bet_Car_Fra_E34_N19.tif
DOI: 10.18710/caf7ke/mvlgxi
title: Final_demo_2020_stdev_P_Fag_E44_N19.tif
DOI: 10.18710/d9mh96/fkmbxc
title: Final_demo_2020_stdev_P_Pic_E54_N24.tif
DOI: 10.18710/d9mh96/hnjwor
title: Final_demo_2020_stdev_P_Pic_E39_N34.tif
DOI: 10.18710/d9mh96/mwvwab
title: Final_demo_2020_stdev_P_Pic_E34_N29.tif
DOI: 10.18710/d9mh96/ogzyxm
title: Final_demo_2020_P_Pic_E24_N24.tif
DOI: 10.18710/d9mh96/umhswd
title: Final_demo_2020_P_Pic_E29_N34.tif
DOI: 10.18710/d9mh96/vjvhjt
title: Final_demo_2020_stdev_P_Pic_E44_N39.tif
DOI: 10.18710/d9mh96/w6geek
title: Final_demo_2020_P_Pic_E49_N44.tif
DOI: 10.18710/dqjwab/0fcwly
title: Final_demo_2020_stdev_P_Euc_E44_N44.tif
DOI: 10.18710/dqjwab/tfdzyy
title: Final_demo_2020_stdev_P_Euc_E49_N14.tif
DOI: 10.18710/g1pdbl/gajydk
title: Final_demo_2020_P_Pop_Sal_E34_N19.tif
DOI: 10.18710/hdjrjn/0qskzp
title: Final_demo_2020_P_OthBL_E44_N34.tif
DOI: 10.18710/hdjrjn/6zzkng
title: Final_demo_2020_stdev_P_OthBL_E24_N14.tif
DOI: 10.18710/hdjrjn/eltpap
title: Final_demo_2020_stdev_P_OthBL_E34_N19.tif
DOI: 10.18710/hdjrjn/mayidq
title: Final_demo_2020_P_OthBL_E54_N24.tif
DOI: 10.18710/hdjrjn/q0tp0k
title: Final_demo_2020_stdev_P_OthBL_E54_N29.tif
DOI: 10.18710/hdjrjn/tpv8pa
title: Final_demo_2020_stdev_P_OthBL_E44_N19.tif
DOI: 10.18710/hdjrjn/w24h5c
title: Final_demo_2020_P_OthBL_E39_N24.tif
DOI: 10.18710/nowphg/7gsfyv
title: Final_demo_2020_P_Que_E44_N39.tif
DOI: 10.18710/razr4o/hrseb0
title: Final_demo_2020_stdev_P_Bet_Car_Fra_E39_N44.tif
DOI: 10.18710/razr4o/owmtjl
title: Final_demo_2020_stdev_P_Bet_Car_Fra_E49_N14.tif
DOI: 10.18710/razr4o/yrymt2
title: Final_demo_2020_stdev_P_Bet_Car_Fra_E49_N19.tif
DOI: 10.18710/caf7ke
title: Fagus (P_Fag) map for 2020

description: This dataset provides a high-resolution (10 m) pan-European map of forest Fagus (P_Fag) for the year 2020, along with an accompanying standard deviation layer. It is part of the PathFinder collection of forest structure maps, which integrates Sentinel-2 satellite imagery, auxiliary geospatial layers, and National Forest Inventory (NFI) data to deliver detailed forest attribute predictions across Europe. The map supports applications in forest management, biomass estimation, carbon accounting, and ecological modeling. For methodology and data integration details, see the documentation dataset of the PathFinder collection (&lt;a href="https://doi.org/10.18710/OEYKEG" title="Documentation" target="_blank"&gt;https://doi.org/10.18710/OEYKEG&lt;/a&gt;) and the following publication: Miettinen, J., Breidenbach, J. et al. (2025). PathFinder's High-Resolution Pan-European Forest Structure Maps: An Integration of Earth Observation and National Forest Inventory Data. Zenodo. &lt;a href="https://doi.org/10.5281/zenodo.17107267" title="Documentation" target="_blank"&gt;https://doi.org/10.5281/zenodo.17107267&lt;/a&gt;.
description: Fagus (P_Fag)
DOI: 10.18710/caf7ke/2utml0
title: Final_demo_2020_stdev_P_Fag_E44_N44.tif
DOI: 10.18710/caf7ke/qomqoc
title: Final_demo_2020_P_Fag_E29_N34.tif
DOI: 10.18710/caf7ke/tw2zgt
title: Final_demo_2020_P_Fag_E44_N39.tif
DOI: 10.18710/caf7ke/u7qtex
title: Final_demo_2020_stdev_P_Fag_E44_N24.tif
DOI: 10.18710/caf7ke/v9qti7
title: Final_demo_2020_P_Fag_E49_N24.tif
DOI: 10.18710/caf7ke/wh8qn2
title: Final_demo_2020_P_Fag_E54_N19.tif
DOI: 10.18710/d9mh96/0fecsv
title: Final_demo_2020_P_Pic_E49_N49.tif
DOI: 10.18710/d9mh96/aegwvo
title: Final_demo_2020_P_Pic_E54_N29.tif
DOI: 10.18710/d9mh96/aentw1
title: Final_demo_2020_P_Pic_E29_N29.tif
DOI: 10.18710/d9mh96/c2dnue
title: Final_demo_2020_P_Pic_E54_N19.tif
DOI: 10.18710/d9mh96/e9dqo1
title: Final_demo_2020_P_Pic_E34_N14.tif
DOI: 10.18710/d9mh96/gqzxcu
title: Final_demo_2020_P_Pic_E44_N24.tif
DOI: 10.18710/d9mh96/mp8ccl
title: Final_demo_2020_stdev_P_Pic_E54_N34.tif
DOI: 10.18710/d9mh96/u8gqqg
title: Final_demo_2020_stdev_P_Pic_E49_N24.tif
DOI: 10.18710/d9mh96/ycaa9h
title: Final_demo_2020_P_Pic_E39_N24.tif
DOI: 10.18710/d9mh96/ytgzsp
title: Final_demo_2020_stdev_P_Pic_E49_N14.tif
DOI: 10.18710/dqjwab/ymcexc
title: Final_demo_2020_stdev_P_Euc_E49_N49.tif
DOI: 10.18710/g1pdbl/0ur2s4
title: Final_demo_2020_stdev_P_Pop_Sal_E34_N19.tif
DOI: 10.18710/g1pdbl/3pqddv
title: Final_demo_2020_P_Pop_Sal_E49_N44.tif
DOI: 10.18710/g1pdbl/csgwco
title: Final_demo_2020_stdev_P_Pop_Sal_E24_N24.tif
DOI: 10.18710/g1pdbl/giyxya
title: Final_demo_2020_P_Pop_Sal_E39_N39.tif
DOI: 10.18710/g1pdbl/ohknzz
title: Final_demo_2020_P_Pop_Sal_E54_N44.tif
DOI: 10.18710/g1pdbl/tz40ay
title: Final_demo_2020_stdev_P_Pop_Sal_E29_N34.tif
DOI: 10.18710/hdjrjn/1vqlw2
title: Final_demo_2020_P_OthBL_E54_N34.tif
DOI: 10.18710/hdjrjn/2gtzzz
title: Final_demo_2020_stdev_P_OthBL_E44_N39.tif
DOI: 10.18710/hdjrjn/3nh7eq
title: Final_demo_2020_P_OthBL_E44_N19.tif
DOI: 10.18710/hdjrjn/6hnh1p
title: Final_demo_2020_stdev_P_OthBL_E59_N14.tif
DOI: 10.18710/hdjrjn/c9pysc
title: Final_demo_2020_stdev_P_OthBL_E54_N19.tif
DOI: 10.18710/hdjrjn/hvx5nw
title: Final_demo_2020_P_OthBL_E49_N54.tif
DOI: 10.18710/hdjrjn/lqn8yo
title: Final_demo_2020_stdev_P_OthBL_E44_N44.tif
DOI: 10.18710/hdjrjn/lqrasf
title: Final_demo_2020_stdev_P_OthBL_E34_N29.tif
DOI: 10.18710/hdjrjn/rysqtc
title: Final_demo_2020_P_OthBL_E54_N19.tif
DOI: 10.18710/hdjrjn/t667wz
title: Final_demo_2020_P_OthBL_E59_N14.tif
DOI: 10.18710/losnxk/smpbzs
title: Final_demo_2020_P_Pin_E34_N24.tif
DOI: 10.18710/nowphg/dhgimv
title: Final_demo_2020_P_Que_E34_N34.tif
DOI: 10.18710/razr4o/dcyjal
title: Final_demo_2020_stdev_P_Bet_Car_Fra_E34_N34.tif
DOI: 10.18710/razr4o/qujq9u
title: Final_demo_2020_stdev_P_Bet_Car_Fra_E49_N24.tif
DOI: 10.18710/vk8lus/dp0now
title: Final_demo_2020_P_Abi_E34_N39.tif
DOI: 10.18710/vk8lus/tkaeuy
title: Final_demo_2020_P_Abi_E24_N24.tif
DOI: 10.18710/vk8lus/u2gzzy
title: Final_demo_2020_P_Abi_E34_N19.tif
DOI: 10.18710/vk8lus/wb8sex
title: Final_demo_2020_P_Abi_E54_N29.tif
DOI: 10.18710/vk8lus/yp2jiq
title: Final_demo_2020_stdev_P_Abi_E34_N34.tif
DOI: 10.18710/vk8lus/zav6dl
title: Final_demo_2020_stdev_P_Abi_E49_N19.tif
DOI: 10.18710/caf7ke/5jn6sm
title: Final_demo_2020_stdev_P_Fag_E39_N34.tif
DOI: 10.18710/caf7ke/5spshh
title: Final_demo_2020_stdev_P_Fag_E44_N54.tif
DOI: 10.18710/caf7ke/dywaik
title: Final_demo_2020_stdev_P_Fag_E54_N19.tif
DOI: 10.18710/caf7ke/hy0wp0
title: Final_demo_2020_stdev_P_Fag_E39_N19.tif
DOI: 10.18710/caf7ke/kaqmix
title: Final_demo_2020_stdev_P_Fag_E49_N19.tif
DOI: 10.18710/caf7ke/khzxeq
title: Final_demo_2020_stdev_P_Fag_E34_N34.tif
DOI: 10.18710/caf7ke/ma96w9
title: Final_demo_2020_stdev_P_Fag_E29_N14.tif
DOI: 10.18710/caf7ke/oeki2f
title: Final_demo_2020_stdev_P_Fag_E39_N39.tif
DOI: 10.18710/caf7ke/ohlq0n
title: Final_demo_2020_P_Fag_E29_N24.tif
DOI: 10.18710/caf7ke/pf2jw7
title: Final_demo_2020_P_Fag_E44_N24.tif
DOI: 10.18710/d9mh96/ny5rza
title: Final_demo_2020_stdev_P_Pic_E39_N19.tif
DOI: 10.18710/d9mh96/rq6uqr
title: Final_demo_2020_stdev_P_Pic_E44_N49.tif
DOI: 10.18710/d9mh96/vwpdov
title: Final_demo_2020_stdev_P_Pic_E44_N14.tif
DOI: 10.18710/dnb6ud/3gv4pp
title: Final_demo_2020_P_OthCon_E34_N29.tif
DOI: 10.18710/dnb6ud/qr1s4f
title: Final_demo_2020_stdev_P_OthCon_E39_N14.tif
DOI: 10.18710/losnxk/jhky91
title: Final_demo_2020_P_Pin_E29_N19.tif
DOI: 10.18710/nowphg/g7t5nw
title: Final_demo_2020_P_Que_E24_N24.tif
DOI: 10.18710/nowphg/u08b6y
title: Final_demo_2020_stdev_P_Que_E29_N29.tif
DOI: 10.18710/nowphg/uabo1l
title: Final_demo_2020_P_Que_E39_N29.tif
DOI: 10.18710/nowphg/xegydg
title: Final_demo_2020_stdev_P_Que_E44_N29.tif
DOI: 10.18710/nowphg/z6osly
title: Final_demo_2020_P_Que_E24_N14.tif
DOI: 10.18710/vk8lus/7o2ukj
title: Final_demo_2020_stdev_P_Abi_E34_N19.tif
DOI: 10.18710/vk8lus/9ernja
title: Final_demo_2020_stdev_P_Abi_E39_N29.tif
DOI: 10.18710/vk8lus/zs9l3b
title: Final_demo_2020_stdev_P_Abi_E39_N19.tif
DOI: 10.18710/b7lpbd/g6zm1p
title: 00_README.txt
DOI: 10.18710/caf7ke/ag3ccf
title: Final_demo_2020_stdev_P_Fag_E49_N49.tif
DOI: 10.18710/d3rk2i/4yi8ia
title: Circuit_12_PF.csv
DOI: 10.18710/d3rk2i/lbrkid
title: Circuit_13_Current.csv
DOI: 10.18710/d9mh96/c6sd06
title: Final_demo_2020_P_Pic_E49_N14.tif
DOI: 10.18710/d9mh96/kspidk
title: Final_demo_2020_P_Pic_E64_N14.tif
DOI: 10.18710/d9mh96/mdgi39
title: Final_demo_2020_P_Pic_E39_N39.tif
DOI: 10.18710/gikmkm/bdbmno
title: 03_ClusterAnalysis_RScript_Minimizers_20250731.R
DOI: 10.18710/hdjrjn/rtxwqf
title: Final_demo_2020_P_OthBL_E29_N39.tif
DOI: 10.18710/losnxk/ds2uwy
title: Final_demo_2020_P_Pin_E44_N39.tif
DOI: 10.18710/losnxk/p2w6tc
title: Final_demo_2020_stdev_P_Pin_E24_N14.tif
DOI: 10.18710/losnxk/tohro2
title: Final_demo_2020_P_Pin_E54_N34.tif
DOI: 10.18710/mcsuag/49zcpr
title: Final_demo_2020_DBH_E29_N39.tif
DOI: 10.18710/mcsuag/mhaz4l
title: Final_demo_2020_DBH_E24_N19.tif
DOI: 10.18710/nlunzz/7mqkvc
title: 6.Daily_N_leaching.txt
DOI: 10.18710/nowphg/1dzxxz
title: Final_demo_2020_stdev_P_Que_E24_N19.tif
DOI: 10.18710/nowphg/5dfgbc
title: Final_demo_2020_stdev_P_Que_E29_N24.tif
DOI: 10.18710/nowphg/78rspb
title: Final_demo_2020_stdev_P_Que_E44_N14.tif
DOI: 10.18710/nowphg/7m5zdp
title: Final_demo_2020_P_Que_E44_N24.tif
DOI: 10.18710/nowphg/kcddrq
title: Final_demo_2020_stdev_P_Que_E29_N34.tif
DOI: 10.18710/nowphg/lbibcc
title: Final_demo_2020_stdev_P_Que_E54_N24.tif
DOI: 10.18710/nowphg/tor9yb
title: Final_demo_2020_P_Que_E24_N19.tif
DOI: 10.18710/nowphg/uly3n0
title: Final_demo_2020_stdev_P_Que_E34_N14.tif
DOI: 10.18710/nowphg/vhp5fo
title: Final_demo_2020_stdev_P_Que_E49_N44.tif
DOI: 10.18710/nowphg/y2zd7q
title: Final_demo_2020_stdev_P_Que_E39_N19.tif
DOI: 10.18710/nowphg/z6bvs1
title: Final_demo_2020_stdev_P_Que_E34_N29.tif
DOI: 10.18710/vj3d9s/eqmagc
title: Threaten_cluster.csv

description: The file contains all the analyzed examples
DOI: 10.18710/vk8lus/0ipzkd
title: Final_demo_2020_P_Abi_E29_N34.tif
DOI: 10.18710/vk8lus/bmjeuk
title: Final_demo_2020_P_Abi_E49_N54.tif
DOI: 10.18710/vk8lus/jeveli
title: Final_demo_2020_P_Abi_E44_N44.tif
DOI: 10.18710/vk8lus/zvikte
title: Final_demo_2020_P_Abi_E59_N14.tif
DOI: 10.18710/z4svvu/ik634y
title: 04_Algae_consortia_data_Lipid.csv
DOI: 10.18710/d9mh96/a4hqj0
title: Final_demo_2020_P_Pic_E34_N24.tif
DOI: 10.18710/d9mh96/bxeu4u
title: Final_demo_2020_stdev_P_Pic_E29_N39.tif
DOI: 10.18710/d9mh96/dra76h
title: Final_demo_2020_stdev_P_Pic_E54_N14.tif
DOI: 10.18710/d9mh96/h7a3ij
title: Final_demo_2020_stdev_P_Pic_E29_N29.tif
DOI: 10.18710/d9mh96/lq14tn
title: Final_demo_2020_P_Pic_E54_N34.tif
DOI: 10.18710/d9mh96/ukl3y5
title: Final_demo_2020_P_Pic_E34_N29.tif
DOI: 10.18710/dqjwab/dzmh3j
title: Final_demo_2020_stdev_P_Euc_E29_N29.tif
DOI: 10.18710/dqjwab/htulww
title: Final_demo_2020_stdev_P_Euc_E34_N29.tif
DOI: 10.18710/dqjwab/j7tjpw
title: Final_demo_2020_stdev_P_Euc_E54_N19.tif
DOI: 10.18710/dqjwab/ji1f7u
title: Final_demo_2020_stdev_P_Euc_E44_N24.tif
DOI: 10.18710/dqjwab/mgcxhi
title: Final_demo_2020_P_Euc_E49_N49.tif
DOI: 10.18710/dqjwab/p6npmt
title: Final_demo_2020_stdev_P_Euc_E39_N29.tif
DOI: 10.18710/dqjwab/r3jdst
title: Final_demo_2020_P_Euc_E49_N39.tif
DOI: 10.18710/dqjwab/vcyvbs
title: Final_demo_2020_P_Euc_E24_N19.tif
DOI: 10.18710/hdjrjn/f5xme9
title: Final_demo_2020_P_OthBL_E39_N14.tif
DOI: 10.18710/hdjrjn/koqe6k
title: Final_demo_2020_stdev_P_OthBL_E39_N14.tif
DOI: 10.18710/losnxk/ilkobb
title: Final_demo_2020_P_Pin_E49_N39.tif
DOI: 10.18710/losnxk/mk8xpk
title: Final_demo_2020_P_Pin_E39_N39.tif
DOI: 10.18710/losnxk/s4uhmq
title: Final_demo_2020_P_Pin_E44_N24.tif
DOI: 10.18710/nowphg
title: Quercus (P_Que) map for 2020

description: This dataset provides a high-resolution (10 m) pan-European map of forest Quercus (P_Que) for the year 2020, along with an accompanying standard deviation layer. It is part of the PathFinder collection of forest structure maps, which integrates Sentinel-2 satellite imagery, auxiliary geospatial layers, and National Forest Inventory (NFI) data to deliver detailed forest attribute predictions across Europe. The map supports applications in forest management, biomass estimation, carbon accounting, and ecological modeling. For methodology and data integration details, see the documentation dataset of the PathFinder collection (&lt;a href="https://doi.org/10.18710/OEYKEG" title="Documentation" target="_blank"&gt;https://doi.org/10.18710/OEYKEG&lt;/a&gt;) and the following publication: Miettinen, J., Breidenbach, J. et al. (2025). PathFinder's High-Resolution Pan-European Forest Structure Maps: An Integration of Earth Observation and National Forest Inventory Data. Zenodo. &lt;a href="https://doi.org/10.5281/zenodo.17107267" title="Documentation" target="_blank"&gt;https://doi.org/10.5281/zenodo.17107267&lt;/a&gt;.
description: Quercus (P_Que)
DOI: 10.18710/nowphg/ceqvhc
title: Final_demo_2020_stdev_P_Que_E44_N54.tif
DOI: 10.18710/rbqoac
title: Replication Data for: Postnatal ablation of hippocampal Cajal-Retzius cells impairs spatial representation in CA1 superficial pyramidal cells.
title: CA1 neuronal activity with postnatal ablation of hippocampal Cajal-Retzius cells in freely moving mice.

description: NWB files that include the processed and raw in-vivo recording data and behavioral tracking data, which are organized by animal ID. All metadata is included in the .nwb files. See detailed instructions in the .txt file.
description: python, 3.11
description: spikeinterface, 0.101.0
description: PyNWB, 3.11
description: NeuroConv, 0.8.0
DOI: 10.18710/vk8lus/beojmd
title: Final_demo_2020_P_Abi_E34_N14.tif
DOI: 10.18710/vk8lus/ihek1g
title: Final_demo_2020_P_Abi_E24_N19.tif
DOI: 10.18710/vk8lus/qgd5ec
title: Final_demo_2020_P_Abi_E54_N24.tif
DOI: 10.18710/vk8lus/tjssjm
title: Final_demo_2020_stdev_P_Abi_E54_N14.tif
DOI: 10.18710/vk8lus/ys5vob
title: Final_demo_2020_P_Abi_E39_N34.tif
DOI: 10.18710/caf7ke/bqa1se
title: Final_demo_2020_P_Fag_E49_N39.tif
DOI: 10.18710/dqjwab/4kl5zx
title: Final_demo_2020_stdev_P_Euc_E44_N14.tif
DOI: 10.18710/dqjwab/8sxtum
title: Final_demo_2020_stdev_P_Euc_E54_N44.tif
DOI: 10.18710/dqjwab/gbifvu
title: Final_demo_2020_stdev_P_Euc_E34_N34.tif
DOI: 10.18710/hdjrjn/ccp66e
title: Final_demo_2020_stdev_P_OthBL_E44_N24.tif
DOI: 10.18710/hdjrjn/jkqcak
title: Final_demo_2020_P_OthBL_E39_N39.tif
DOI: 10.18710/hdjrjn/p15imj
title: Final_demo_2020_stdev_P_OthBL_E34_N14.tif
DOI: 10.18710/hdjrjn/ruiggu
title: Final_demo_2020_P_OthBL_E39_N44.tif
DOI: 10.18710/hdjrjn/uidxkg
title: Final_demo_2020_stdev_P_OthBL_E29_N39.tif
DOI: 10.18710/losnxk/81mesd
title: Final_demo_2020_P_Pin_E44_N49.tif
DOI: 10.18710/vk8lus/3fwksd
title: Final_demo_2020_P_Abi_E49_N14.tif
DOI: 10.18710/vk8lus/4rwhoj
title: Final_demo_2020_stdev_P_Abi_E44_N49.tif
DOI: 10.18710/vk8lus/czubsz
title: Final_demo_2020_stdev_P_Abi_E54_N29.tif
DOI: 10.18710/vk8lus/xzhh7g
title: Final_demo_2020_P_Abi_E44_N54.tif
DOI: 10.18710/1eiovf/wn3v5v
title: cc_counts_R13.txt
DOI: 10.18710/9owatk
title: Replication Data for: Learning to use GenAI in higher education: knowledge, experiences, and needs among staff following a GenAI professional development course

description: Pre-post data from survey - staff in higher education who went through a GenAI course. Includes TPACK constructs, age groups, affiliation, age, teaching or not.
DOI: 10.18710/d3rk2i/zary9w
title: Circuit_7_PF.csv
DOI: 10.18710/dnb6ud/fetb2u
title: Final_demo_2020_P_OthCon_E39_N29.tif
DOI: 10.18710/dnb6ud/qlileb
title: Final_demo_2020_P_OthCon_E34_N24.tif
DOI: 10.18710/ehmqu7/7vuqs8
title: IRIF1.csv
DOI: 10.18710/krl520/ymyjhj
title: :unav

description: :unav
DOI: 10.18710/losnxk/d2jska
title: Final_demo_2020_stdev_P_Pin_E54_N34.tif
DOI: 10.18710/losnxk/fm3u1q
title: Final_demo_2020_stdev_P_Pin_E29_N34.tif
DOI: 10.18710/losnxk/hkj5ff
title: Final_demo_2020_stdev_P_Pin_E49_N49.tif
DOI: 10.18710/losnxk/hukwqu
title: Final_demo_2020_stdev_P_Pin_E29_N29.tif
DOI: 10.18710/losnxk/qnaooo
title: Final_demo_2020_stdev_P_Pin_E54_N19.tif