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6,782 results for “project”
Figure 1 from: Pérez-Luque AJ, Zamora R, Bonet FJ, Pérez-Pérez R (2015) Dataset of MIGRAME Project (Global Change, Altitudinal Range Shift and Colonization of Degraded Habitats in Mediterranean Mountains). PhytoKeys 56: 61-81. https://doi.org/10.3897/phytokeys.56.5482
Figure 1 - Schematic representation of the two main hypothesis of the project: altitudinal migration (a) and colonization of marginal areas (b) of Quercus pyrenaica forests.
Figure 5 from: Pando F, Dueñas M, Lado C, Telleria MT (2016) The Flora Mycologica Iberica Project fungi occurrence dataset. MycoKeys 15: 59-72. https://doi.org/10.3897/mycokeys.15.9765
Figure 5 - Geographic distribution. Records grouped by provinces, not georeferenced. Provinces where non-georeferenced records provide substantial information missing in georeferenced records are encircled in red.
Figure 3 from: Pando F, Dueñas M, Lado C, Telleria MT (2016) The Flora Mycologica Iberica Project fungi occurrence dataset. MycoKeys 15: 59-72. https://doi.org/10.3897/mycokeys.15.9765
Figure 3 - Geographic distribution of the georeferenced records. The darker the color, the higher is the record density.
Figure 8 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 8 - The patchiness of survey coverage in Europe illustrated by the distribution map of Plantago lanceolata taken from GBIF in 2016. This species is one of the commonest and most widespread in Europe, it should occur in almost all areas of this map, but in fact the data traces out the borders of countries and area who have published data on GBIF.
Figure 9 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 9 - Information flows between EU BON and LTER Europe, as envisaged on the 3rd EU BON Stakeholder Roundtable in Granada on 9-11 December 2015.
Figure 6 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 6 - Individual Metacat instances can be connected to DataOne which replicates public files. Thus the data is still available if a single instance goes offline. https://search.dataone.org/#data/page/0
Figure 5 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 5 - PPBio has installed a Metacat instance for their researchers to upload and make publicly available the results of work related to biodiversity in the Western Amazon. https://ppbiodata.inpa.gov.br/metacatui/
Figure 3 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 3 - The implementation of Darwin Core Archive in Plazi to transfer treatment data. Observation data described with Darwin Core terms.
Figure 4 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 4 - The public data repository provided by the Knowledge Network for Biocomplexity (KNB). https://knb.ecoinformatics.org/#data/page/0
Figure 1 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 1 - ARPHA consists of two integrated workflows: in ARPHA-XML, the manuscript is written and processed via the ARPHA Writing Tool, and in ARPHA-DOC, the manuscript is submitted and processed as document file(s).
Standardized Project Gutenberg Corpus
<p><strong>Standardized Project Gutenberg Corpus</strong><br> version: SPGC-2018-07-18<br> number of books: 55905<br> uncompressed size: 3GB (counts) + 18GB (tokens)</p> <p><strong>Publication</strong><br> <a href="https://arxiv.org/abs/1812.08092">https://arxiv.org/abs/1812.08092</a><br> [ journal link ]</p> <p><strong>Project Site</strong><br> <a href="https://pgcorpus.github.io/">https://pgcorpus.github.io/</a></p> <p><strong>Github</strong><br> <a href="https://github.com/pgcorpus/gutenberg">https://github.com/pgcorpus/gutenberg</a></p>
Supplementary material 4 from: Azzaro M, Packard TT, Monticelli LS, Maimone G, Rappazzo AC, Azzaro F, Grilli F, Crisafi E, La Ferla R (2019) Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 441-475. https://doi.org/10.3897/natureconservation.34.30631
: Data type: statistical data
Supplementary material 5 from: Azzaro M, Packard TT, Monticelli LS, Maimone G, Rappazzo AC, Azzaro F, Grilli F, Crisafi E, La Ferla R (2019) Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 441-475. https://doi.org/10.3897/natureconservation.34.30631
: Data type: measurement
Supplementary material 3 from: Azzaro M, Packard TT, Monticelli LS, Maimone G, Rappazzo AC, Azzaro F, Grilli F, Crisafi E, La Ferla R (2019) Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 441-475. https://doi.org/10.3897/natureconservation.34.30631
: Data type: measurements
Supplementary material 2 from: Azzaro M, Packard TT, Monticelli LS, Maimone G, Rappazzo AC, Azzaro F, Grilli F, Crisafi E, La Ferla R (2019) Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 441-475. https://doi.org/10.3897/natureconservation.34.30631
: Data type: parameters data
Supplementary material 1 from: Azzaro M, Packard TT, Monticelli LS, Maimone G, Rappazzo AC, Azzaro F, Grilli F, Crisafi E, La Ferla R (2019) Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 441-475. https://doi.org/10.3897/natureconservation.34.30631
: Data type: measurement
Smartphone-Based Incentive Framework for Dynamic Network-Level Traffic Congestion Management Project H3
<p>Task 2 numerical experiment results</p>
Citizen science project descriptions as science communication texts - the good, the bad, and the ugly
<p>This study aimed to determine to what extent do CS project descriptions actually contain the kinds of information relevant to prospective participants and whether this information is conveyed in a comprehensible and attractive manner. To this end, we conducted a qualitative content analysis of a random sample of 120 English-language project descriptions stored in the CS Track database. The coding rubric used for this study is based on the ten-step template for writing engaging project descriptions we recently designed and published. The sample was produced as follows: After creating a dataset containing only English-language project descriptions, we excluded all descriptions which consist of less than 100 or more than 500 words. Texts of less than 100 words cannot be expected to contain a significant amount of information. Project descriptions of more than 500 words are less likely to be read in their entirety than shorter texts and thus ill-suited to the task of capturing the readers’ interest and prompting them to join the project in question. Finally, we applied the ‘random’ function of RStudio to randomly select 120 texts from the resulting dataset of 1283 descriptions. The qualitative content analysis was performed in two consecutive steps. First, in order to ensure that the coding rubric is fit for purpose and all categories within it well-defined and demarcated, all three members of the research team independently coded 40 project descriptions. After discussing the results and making slight modifications to the coding rubric, each team member coded roughly one third of the remaining 80 descriptions. </p> <p>Preliminary results suggest that the majority of project descriptions in our sample fail to mention how citizen scientists will benefit from participating, what kind of training they will receive, how their contributions will be acknowledged, and whether they will have access to project results. Furthermore, the project’s goals, its target audience, and the tasks volunteers will be expected to complete are very often not described explicitly and clearly enough. For instance, very few project descriptions contain concrete information on required skills and equipment or on the time commitment associated with participation.</p> <p>This dataset contains the coding rubric used to analyse project descriptions and a visualisation of preliminary results.</p>
GoldenEye project video 1
<p>Goldeneye H2020 project video 1</p>
Code for MATSim-NYC project
<p>This file includes the code and parameters for the baseline MATSim-NYC model, network calibration, and other additional features. All the required input files are saved in the input folder. </p> <p>The synthetic population as well as the data dictionary are also incorporated. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.