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Figure 1 from: Peart RA (2018) Ampeliscidae (Crustacea, Amphipoda) from the IceAGE expeditions. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 145–173. https://doi.org/10.3897/zookeys.731.19948
Figure 1 Map to the distribution of species from Icelandic waters in the genera of Ampeliscidae, highlighting the new species documented.
Figure 1 from: Krapp-Schickel T (2018) Leucothoe vaderotti, a new Atlantic Leucothoe (Crustacea, Amphipoda) belonging to the "spinicarpa-clade" (Crustacea, Amphipoda). ). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 135-144. https://doi.org/10.3897/zookeys.731.19813
Figure 1 Leucothoe vaderotti sp. n. A 1, A 2 antennae Mx 1, Mx 2 maxillae Gn 1, Gn 2 gnathopods Gn 1', Gn 2' gnathopods enlarged.
Figure 3 from: Minelli A, Oggioni A, Pugnetti A, Sarretta A, Bastianini M, Bergami C, Bernardi Aubry F, Camatti E, Scovacricchi T, Socal G (2018) The project EcoNAOS: vision and practice towards an open approach in the Northern Adriatic Sea ecological observatory. Research Ideas and Outcomes 4: e24224. https://doi.org/10.3897/rio.4.e24224
Figure 3 The spiral model - an open research lifecycle involves sharing of each step of the process, including not only scientific papers but also research ideas, data (raw and processed), metadata, methods, software (Minelli et al. 2017).
Figure 4a from: Minelli A, Oggioni A, Pugnetti A, Sarretta A, Bastianini M, Bergami C, Bernardi Aubry F, Camatti E, Scovacricchi T, Socal G (2018) The project EcoNAOS: vision and practice towards an open approach in the Northern Adriatic Sea ecological observatory. Research Ideas and Outcomes 4: e24224. https://doi.org/10.3897/rio.4.e24224
Figure 4a An extract from data collected since 1965 to today in the NAS. - snapshot of the organization of the main table of data
Figure 1 from: Minelli A, Oggioni A, Pugnetti A, Sarretta A, Bastianini M, Bergami C, Bernardi Aubry F, Camatti E, Scovacricchi T, Socal G (2018) The project EcoNAOS: vision and practice towards an open approach in the Northern Adriatic Sea ecological observatory. Research Ideas and Outcomes 4: e24224. https://doi.org/10.3897/rio.4.e24224
Figure 1 The LTER-Italy parent site "Northern Adriatic Sea". The four research sites that compose it, together with the fixed point observatories, are evidenced. 1: Gulf of Trieste and Mambo buoy, 2: Gulf of Venice and Acqua Alta Tower, 3: Po Delta and Romagna Coast and S1-GB and E1 buoys, 4: Senigallia-Susak Transect and TeleSenigallia Pylon (Minelli et al. 2018)
Figure 4c from: Minelli A, Oggioni A, Pugnetti A, Sarretta A, Bastianini M, Bergami C, Bernardi Aubry F, Camatti E, Scovacricchi T, Socal G (2018) The project EcoNAOS: vision and practice towards an open approach in the Northern Adriatic Sea ecological observatory. Research Ideas and Outcomes 4: e24224. https://doi.org/10.3897/rio.4.e24224
Figure 4c An extract from data collected since 1965 to today in the NAS. - 3D visualization of data from the viewpoint in b: the yellow grid represents the sea level, each set of vertical points represents more samplings relying on the same (X, Y) coordinates but at different depths (Z)
Figure 4b from: Minelli A, Oggioni A, Pugnetti A, Sarretta A, Bastianini M, Bergami C, Bernardi Aubry F, Camatti E, Scovacricchi T, Socal G (2018) The project EcoNAOS: vision and practice towards an open approach in the Northern Adriatic Sea ecological observatory. Research Ideas and Outcomes 4: e24224. https://doi.org/10.3897/rio.4.e24224
Figure 4b An extract from data collected since 1965 to today in the NAS. - 2D visualization of custom sampling stations (used from 1965 to 1990, before GPS advent) and real sampling points (until 2015)
ASU-BGU Project - Echohydrology Project
<p>Lehavim LTER data</p>
ASU-BGU -Project - Echohydrology of semi-arid environments
<p>Lehavim LTER - main results and file of the matadata of Lehavim LTER research. </p>
ASU-BGU-Project - Echohydrology of semi-arid environments
<p>Lehavim LTER - main results and file of the matadata of Lehavim LTER research. </p>
Data for paper in JGR-Atmospheres: The role of internal variability in 21st century projections of the seasonal cycle of Northern Hemisphere surface temperature
<p>Datasets for reproducing the results in our study submitted to JGR-Atmospheres.</p>
Interaction models - General findings of iTV projects of the last 5 years (2013-2018)
<p>General findings of iTV projects of the last 5 years (2013-2018), the hardware used and the respective modalities for the interaction. It also includes data filtering, including a table with projects targeted to elderly users.</p> <p> </p>
DATS 6401- Individual Project - Mo Zhou
<p>These are raw datasets I used in my project.</p>
Relationships of research outputs and projects in NUSL, Czech R&D Information System and OpenAIRE: Supporting Data for Master Thesis
<p>Research data for master thesis related to relationships of research outputs and projects in three resources: Czech National Repository of Grey Literature (www.nusl.cz), Czech Research and Development Information System (www.rvvi.cz) and OpenAIRE (www.openaire.eu). The dataset relates to relationships between research outputs and research projects. Collected in July 2019.</p>
ENERIT module outcomes for the 3 pilot sites on which the module has been deployed during the HIT2GAP project
<p>This dataset consists of data generated by the ENERIT module (Energy Management module) during its running on three pilot sites of the HIT2GAP project (Challenger (France), Nanogune (Spain), Wilanow (Poland)). </p> <p>Each dataset consists of 3 subsets:</p> <ol> <li>Monthly Energy Consumption Data collected from Jan 2016 to August 2019 (42 Months)</li> <li>Yearly Significant Energy Uses identified at the sites</li> <li>Yearly Improvement Opportunities identified for each site</li> </ol> <p>Metadata Description:</p> <p>1. Monthly Energy Consumption:</p> <p>Type (<em>Fuel type</em>)</p> <p>Location <em>(Building reference)</em></p> <p>Meter (<em>Meter title)</em></p> <p>From (<em>Start date for reading)</em></p> <p>To (<em>End date for reading)</em></p> <p>Cost<em> (€)</em></p> <p>Energy<em> (kWh)</em></p> <p>Primary energy <em>(kWh)</em></p> <p>CO2 (<em>kgCO2</em>)</p> <p> </p> <p>2. Yearly Significant Energy Uses</p> <p>Name</p> <p>Location (<em>Building reference)</em></p> <p>Year</p> <p>Significant Energy Use <em>(is this Energy Use considered Significant)</em></p> <p>Energy Flow Entry <em>(Should it appear in the Energy Flow diagram)</em></p> <p>Details</p> <p>Objectives<em> (Description of what is planned to save energy for this Energy Use)</em></p> <p>Target <em>(Target saving - text)</em></p> <p>Elec Target<em> (Target – numeric)</em></p> <p>Therm Target<em> (Target – numeric)</em></p> <p>Annual Elect Energy</p> <p>Annual Elec Cost</p> <p>Annual Elec CO2</p> <p>Annual Therm Energy</p> <p>Annual Therm Cost</p> <p>Annual Therm CO2</p> <p>Water Quantity</p> <p>Water Cost</p> <p>Linked Elect Meters (<em>Meter descriptions that data is automatically populated from)</em></p> <p>Linked Therm Meters (<em>Meter descriptions that data is automatically populated from)</em></p> <p> </p> <p>3. Yearly Improvement Opportunities</p> <p>Reference: Identifier</p> <p>Title:</p> <p>Location (<em>Building reference)</em></p> <p>Year</p> <p>Type <em>(</em><em>Type of improvement Opportunity)</em></p> <p>Category</p> <p>Sub-Category</p> <p>Significant Energy Use <em>(Energy use that this Improvement Opportunity was assigned against)</em></p> <p>Details </p> <p>Requestor (<em>user who raised the IO)</em></p> <p>Energy Manager<em> (Site Energy Manager)</em></p> <p>Est. Elec. Energy<em> (kWh</em>)</p> <p>Est. Elec. Cost <em>(€)</em></p> <p>Est. Elec. CO2 (kgCO2)</p> <p>Est. Therm. Energy<em> (kWh)</em></p> <p>Est. Therm. Cost <em>(€)</em></p> <p>Est. Therm. CO2 <em>(kgCO2)</em></p> <p>Actual Elec. Energy</p> <p>Actual Elec. Cost</p> <p>Actual Elec. CO2</p> <p>Actual Therm. Energy</p> <p>Actual Therm. Cost</p> <p>Actual Therm. CO2</p> <p>Comments</p> <p>Co-Benefit Details</p> <p>Capital Cost<em> (€)</em></p> <p>Estimated Payback<em> (Years)</em></p> <p>Payback <em>(Years)</em></p> <p>Unit (Energy)</p> <p>Unit (Currency)</p> <p>Unit (CO2)</p> <p>Status</p> <p>Complexity</p> <p>EHS</p> <p>Energy Use</p> <p>Maintenance</p> <p>Verification Method</p> <p>Notes for Assignee</p> <p>Currently With (Person currently responsible for IO)</p> <p> </p>
Two dimensional projections of simulated events recorded by an AT-TPC
<p>This dataset contains 8000 unlabeled and 2400 labelled events from a simulated proton scattering experiment on 46Ar. </p> <p>The labels are 0 for proton events, and 1 for carbon events. </p>
Figure 4 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
Figure 4 - Richness of taxa based on field data (observations). The number includes hybrids. Coordinates are expressed in metres, UTM WGS84 33N. Province abbreviations: AV = Avellino, BN = Benevento, CE = Caserta, NA = Naples, SA = Salerno.
Figure 3 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
Figure 3 - number of field data (observations) in the 10 km × 10 km cells of the grid on 31 December 2016. Coordinates are expressed in metres, UTM WGS84 33N. Province abbreviations: AV = Avellino, BN = Benevento, CE = Caserta, NA = Naples, SA = Salerno.
Figure 2 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
Figure 2 - Richness of taxa based on literature data. The number includes hybrids but also species sensu lato. Coordinates are expressed in metres, UTM WGS84 33N. Province abbreviations: AV = Avellino, BN = Benevento, CE = Caserta, NA = Naples, SA = Salerno.
Figure 1 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
Figure 1 - Number of bibliographic records (citations) in the 10 km × 10 km cells of the grid on 31 December 2016. Coordinates are expressed in metres, UTM WGS84 33N. Province abbreviations: AV = Avellino, BN = Benevento, CE = Caserta, NA = Naples, SA = Salerno.
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.