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5,145 results for “CO₂”
Collection data and molecular datasets for: Defining species-specific seed sourcing strategies for restoration: An example of how to use genetic data to inform seed collections for multiple co-occurring species
<p>Two files for each dataset are provided:</p> <p>Metadata files contain colelcting information for the samples in each molecular dataset as well as the group assignments (species, genetic neighbourhood and sites) used in analyses, saved as an excel spreadsheet.</p> <p>Molecular datasets containing samples and SNPs used in analyses. The data is formatted as a genlight object saved as an RData file that can be read into the R statisical environment and analysed using the 'dartR' package (Gruber et al. 2018).</p>
Realtime Data Collection and Analysis Framework for Collaboration and Co-presence in a Virtual Reality Environment
<p>Title: Realtime Data Collection and Analysis Framework for Collaboration and Co-presence in a Virtual Reality Environment</p> <p>Abstract:</p> <p>As VR technologies continue to evolve and gain popularity, one of their most notable features is connecting with the virtual presence of a person who is not physically present. Understanding the dynamics of user interaction within these environments is crucial, as they can be utilized in various ways, including collaboration, communication, social interactions, or games and entertainment. This paper presents a method for measuring collaboration and co-presence factors of users by developing a real-time data collection and analysis framework. The designed framework focuses on different collaboration and co-presence scenarios and evaluates a comprehensive system for monitoring and analyzing user interactions in VR, employing both physiological sensors and subjective feedback to assess the sense of presence, co-presence, and collaboration quality. Through an extensive literature review, the paper studies how various factors, including avatar realism and communication modalities, influence user engagement and interaction efficacy. The experiment framework’s capability to integrate qualitative and quantitative data provides a deeper understanding of the immersive experience and its impact on collaborative tasks. The results highlight the importance of design choices in VR environments and their implications for human-computer interaction, user performance, and satisfaction. The findings offer practical guidance for developing more effective VR systems for collaborative work and social interaction.</p> <p>Data Description:</p> <p>1. User Interaction Logs:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Timestamped logs of user actions and interactions within the VR environment, including movement data, interaction with objects, and communication instances.</p> <p><span> </span>- Format: CSV</p> <p><span> </span>- Variables: User ID, Timestamp, Action Type, Object Interacted, Coordinates, Duration</p> <p>2. Physiological Sensor Data:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Real-time physiological data collected from users during VR sessions, including heart rate, skin conductance, and EEG data.</p> <p><span> </span>- Format: CSV,</p> <p><span> </span>- Variables: User ID, Timestamp, Heart Rate, Skin Conductance, EEG Channels</p> <p>3. Avatar Realism and Communication Modalities Data:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Data evaluating the impact of avatar realism and communication methods (e.g., voice chat, text chat) on user engagement and interaction efficacy.</p> <p><span> </span>- Format: CSV, Text</p> <p><span> </span>- Variables: User ID, Avatar Type, Communication Modality, Engagement Score, Interaction Quality Feedback</p> <p>4. Collaboration and Co-presence Metrics:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Calculated metrics for collaboration efficiency and co-presence, derived from interaction logs and physiological data.</p> <p><span> </span>- Format: CSV</p> <p><span> </span>- Variables: User ID, Collaboration Efficiency Score, Co-presence Score, Task Performance</p> <p> </p> <p> </p>
Recording of the webinar "Six Pathways to Smart Certification of BioBased Systems" in the frame of the 3-CO project
<p>In this webinar on 7th June 2024 six different Horizon Europe projects presented themselves to the public: 3-CO (as the organiser), BioReCer, SUSTRACK and the BIOBASEDCERT Cluster (Star4bbs, Harmonitor, Sustcert4Biobased).</p> <p>Speakers were: Maarit Haltunen (3-CO), Pedro Villanueva Rey (BioReCer), Gülsah Yilan (SUSTRACK), Costanza Rossi, Luana Ladu, Iris Vural Gürsel (Cluster).</p> <p>Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p>
Fig. 2 in Molecular phylogeny of Indonesian Lymantria Tussock Moths (Lepidoptera: Erebidae) based on CO I gene sequences
Fig. 2. Pairwise sequences divergence based on K2P model versus Transition/Transversion (Ts/Tv).
Civil Society Versus Local Self‐Governments and Central Government in V4 Countries: The Case of Co‐Creation
<p>The rersearch concept.</p> <p>NGOs play a key role in all economies. Their activities include, but are not limited to, environmental, social, advocacy and human rights work. They can work to promote social or political change on a broad scale or very locally. NGOs are critical in developing society, improving communities, and promoting citizen participation. At the same time, NGOs are currently faced with multiple challenges like the growing role of the nongovernmental organization (NGOs) sector, the increasing expectations of public donors regarding performance and social and economic value creation, decrease in number of volunteers or the outflow of qualified paid staff from organizations as well as the precarious employment phenomenon among NGOs. Moreover, NGOs struggle with insufficient financial and programming resources and the staff necessary for accessing additional funds, including private capital. Unlike for-profit entities, their financial condition strongly depends on the support of external donors, public aid, or image and the level of trust in their social activities.</p> <p>Entrepreneurial orientation can be a way to face the above challenges, increasing NGOs’ efficiency and effectiveness in fulfilling and co-creating social goals. However, growing entrepreneurial orientation may have some side-effects which need to be examined; perhaps it makes it more difficult to navigate “mission-market tension”; maybe some side-effects are even positive (cf. Vacekova et al. 2017) Although, there is an intense discussion in the literature on entrepreneurial orientation that may affect NGOs’ performance, so far this topic has been poorly empirically verified. The identified gap in the current state of literature concerns mainly institutional conditions (specifics) influencing the entrepreneurial behavior of NGOs and the relationships of entrepreneurial orientation with the performance of NGOs at different aspect of their functioning. It may also appear crucial presently during the COVID-19 pandemic. Thus the research gap is twofold: 1) how can entrepreneurial orientation help Polish and Czech NGOs to live through the pandemic; 2) what are the possible longer-term impacts of entrepreneurial orientation. The identified research gap is further radicalized by the major exogenous shock of the Covid-19 pandemic, for three reasons. First, the impacts of the pandemic on NGOs, both inthe Western and Central and Eastern Europe, still remain largely unknown. Second, the political and economic responses to the pandemic all over the globe vary widely depending on the prevalent institutional regimes. This means that the governance implications of the pandemic, in different parts of the world, and within Europe in particular, will widely differ. These differences can be profitably illuminated by historical institutionalist lens, which is uniquely suited for appreciating institutional diversity and heterogeneity (cf. Plaček et al., 2018).</p> <p>In the Czech Republic alone, the applicant had already identified a complex typology of public responses to the pandemic (Plaček and Vacekov. et al., 2021). Third, by bringing numerous individuals into various difficult circumstances, the pandemic heightens the societal significance of NGOs, which have the potential to offer much-needed social services. In the Czech Republic, NGOs and other organizations of the third sector are known to be actively involved in the delivery of such services, often at the cost of experiencing considerable financial stress (seehttps://mapaneziskovek.cz/ and The Czech Satellite Account of Non-Profit Institutions). The Western debates about entrepreneurial orientation are insufficient to fill this research gap for Polish and Czech NGOs. Czech and Polish cases are examined together due to some parallels. Both countries are located in East- and Central Europe and have similar historical identities. Both countries belong to the post-communist countries, which determines the economic and social functioning of non-governmental organizations.</p> <p>The transformation after 1989 also shaped a similar institutional framework for their activities. Poland and the Czech Republic joined the European Union in 2004, significantly changing the reality of non-governmental functioning. Among other things, non-governmental organizations gained access to public funds from structural funds. The financial situation of non-governmental organizations in the Czech Republic and Poland strongly depends on European subsidies. In both countries, non-governmental organizations are characterized by a weaker financial condition compared to Anglo-Saxon countries and developed countries of Western Europe. </p> <p><strong>The research results can be used to make recommendations for public authorities on shaping the institutional environment of non-governmental organizations and for NGO leaders to build entrepreneurial orientation in their organizations and raise awareness of its impact on performance, including co-creating social value with local and central government.</strong></p> <p><strong>We must state that recruiting the focus group par‐ ticipants in Poland and Hungary faced difficulties, as potential participants perceived the topics as politi‐ cally sensitive.</strong></p> <p> </p>
Fig. 1 in Past, present and future of host‾parasite co-extinctions
Fig. 1. Cumulative number of documented species extinctions according to IUCN (2014).
Fig. 4 in Co-introduction of ancyrocephalid monogeneans on their invasive host, the largemouth bass, Micropterus salmoides (Lacepedé, 1802) in South Africa
Fig. 4. Composition and abundance of monogenean species at each sampling locality.
Fig. 2 in Molecular phylogeny of Indonesian Zeuzera (Lepidoptera: Cossidae) wood borer moths based on CO I gene sequence
Fig. 2. Scatter plots of K2P model distance for Transition (Ts) versus Transversion (Tv).
Co-Creation verzweigter Szenarien mit H5P
<p>Der Vortrag "Co-Creation von Branching Scenarios mit H5P" ist Teil der Reihe "Serviceeinheiten rund um das Thema digitales Unterrichten" und adressiert wirtschaftsberufliche Lehrkräfte. Der Vortrag beinhaltet neben einer theoretischen Einordnung des Branching Scenarios einen ersten Überblick bezüglich der Gestaltungsmöglichkeiten, welche die Open-Source-Software H5P (angehenden) Lehrkräften für die Entwicklung von Branching Szenarien bietet (Contentformen, Oberfläche). Weiterhin werden aktuelle Forschungsbefunde zur Lernförderlichkeit von H5P-Inhalten skizziert und erste praxisbezogene Einblicke in die Konstruktion eines Branching Scenarios aufgezeigt (Editormodus in H5P). Der Vortrag schließt mit einem Ausblick auf co-creative Entwicklungsprozesse und eine Einbindung in den Projektkontext WÖRLD, in welchen der Vortrag integriert ist.</p>
Co K-edge XAS files
<p>38 files uploaded as .avg files for Co K-edge XAS data reported in <strong>Diffusion- and pH-dependent reactivity of layer-type MnO<sub>2</sub>: Reactions at particle edges versus vacancy sites</strong> by Yuheng Wang<sup>1</sup>, Sassi Benkaddour<sup>1</sup>, Francesco Femi Marafatto<sup>1</sup>, Jasquelin Peña<sup>1*</sup></p> <p><sup>1 </sup>Institute of Earth Surface Dynamics, University of Lausanne, CH-1015 Lausanne, Switzerland</p> <p>*Corresponding author: jasquelin.pena@unil.ch</p> <p>Note: File names correspond to names used in the manuscript except that the % sign has been excluded from the loading value.</p>
Purification of ACVR1 for co-crystallisation with LDN-193189 and other compounds
<p>Purification of constitutively active ACVR1 for co-crystallisation with LDN-193189 and three other compounds.</p>
Ant and termite assemblages along a tropical forest disturbance gradient in Sabah, Malaysia: A study of co-variation and trophic interactions
<b>Description: </b><p>Termite community composition from soil pits and deadwood</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/103"><b>Ant and termite assemblages along a tropical forest disturbance gradient in Sabah, Malaysia: A study of co-variation and trophic interactions</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=38">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Functional traits</b> (Worksheet Function)</p><p>Dimensions: 36 rows by 3 columns</p><p>Description: Functional traits associated with each genus</p><p>Fields: </p><ul><li><b>Genus</b>: Genus ID (Field type: Taxa)</li><li><b>Functional.group</b>: Humification gradient (Field type: Categorical Trait)</li></ul><br></li><li><p><b>Soil pit data</b> (Worksheet SoilPits)</p><p>Dimensions: 954 rows by 35 columns</p><p>Description: Termite community composition from soil pits</p><p>Fields: </p><ul><li><b>2nd.order.point</b>: SAFE Project sample site (Field type: Location)</li><li><b>Quadrat.number.(in.my.study)</b>: Quadrat number (Field type: ID)</li><li><b>Date</b>: Date of sample collection (Field type: Date)</li><li><b>Pit.number</b>: Pit number within the plot (Field type: Replicate)</li><li><b>Time</b>: Time samples were collected (Field type: Time)</li><li><b>No..of.termites</b>: Number adult termites (Field type: Abundance)</li><li><b>No.juvenile.termites</b>: Number juvenile termites (Field type: Abundance)</li><li><b>Unknown</b>: Number of damaged individuals or individuals that can't definitively be assigned to genera (Field type: Abundance)</li><li><b>Dicuspiditermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Schedorhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Prohamitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Malaysiotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Mirocapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Hypotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Procapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Homatermes.undescribed.genus</b>: Number of individuals (Field type: Abundance)</li><li><b>Termes</b>: Number of individuals (Field type: Abundance)</li><li><b>Syncapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Pericapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Microcerotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Macrotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Globitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Lacessititermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Pseudocapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Homallotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Oriencapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Oriensublitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Labritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Euramitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Rhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Nasutitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Bulbitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Odontotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Heterotermes</b>: Number of individuals (Field type: Abundance)</li></ul><br></li><li><p><b>Deadwood data</b> (Worksheet Deadwood)</p><p>Dimensions: 138 rows by 23 columns</p><p>Description: Termite community composition from deadwood</p><p>Fields: </p><ul><li><b>2nd.order.point</b>: SAFE Project sample site (Field type: Location)</li><li><b>Quadrat.number.(in.my.study)</b>: Quadrat number (Field type: ID)</li><li><b>Date</b>: Date of sample collection (Field type: Date)</li><li><b>Wood.sample</b>: Wood piece within Quadrat (Field type: Replicate)</li><li><b>Time</b>: Time samples were collected (Field type: Time)</li><li><b>No..of.termites</b>: Number adult termites (Field type: Abundance)</li><li><b>No.juvenile.termites</b>: Number juvenile termites (Field type: Abundance)</li><li><b>Unknown</b>: Number of damaged individuals or individuals that can't definitively be assigned to genera (Field type: Abundance)</li><li><b>Dicuspiditermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Schedorhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Homallotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Bulbitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Macrotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Globitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Nasutitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Heterotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Syncapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Malaysiotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Parrhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Pericapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Rhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Aciculitermes</b>: Number of individuals (Field type: Abundance)</li></ul><br></li></ol><p><b>Date range: </b>2010-04-21 to 2010-05-25</p><p><b>Latitudinal extent: </b>4.6353 to 4.7520</p><p><b>Longitudinal extent: </b>116.9542 to 117.6288</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Isoptera<br> -  -  -  -  - <i>Aciculitermes</i><br> -  -  -  -  - [<i>Euramitermes</i>]<br> -  -  -  -  - <i>Homallotermes</i><br> -  -  -  -  - <i>Hypotermes</i><br> -  -  -  -  - <i>Mirocapritermes</i><br> -  -  -  -  - <i>Procapritermes</i><br> -  -  -  -  - <i>Prohamitermes</i><br> -  -  -  -  - <i>Pseudocapritermes</i><br> -  -  -  - Rhinotermitidae<br> -  -  -  -  - <i>Heterotermes</i><br> -  -  -  -  - <i>Parrhinotermes</i><br> -  -  -  -  - <i>Rhinotermes</i><br> -  -  -  -  - <i>Schedorhinotermes</i><br> -  -  -  - Termitidae<br> -  -  -  -  - <i>Bulbitermes</i><br> -  -  -  -  - <i>Dicuspiditermes</i><br> -  -  -  -  - <i>Globitermes</i><br> -  -  -  -  - <i>Labritermes</i><br> -  -  -  -  - [<i>Lacessititermes</i>]<br> -  -  -  -  - <i>Macrotermes</i><br> -  -  -  -  - <i>Malaysiotermes</i><br> -  -  -  -  - <i>Microcerotermes</i><br> -  -  -  -  - <i>Nasutitermes</i><br> -  -  -  -  - <i>Odontotermes</i><br> -  -  -  -  - <i>Oriencapritermes</i><br> -  -  -  -  - <i>Oriensubulitermes</i><br> -  -  -  -  - <i>Pericapritermes</i><br> -  -  -  -  - <i>Syncapritermes</i><br> -  -  -  -  - <i>Termes</i><br> -  -  -  -  - <i>Hodotermes</i><br> -  - [Homatermes.undescribed.genus]<br></div><p></p>
Expanding CO Shells in the Orion A Molecular Cloud - Additional Figures
<p>Additional figures and captions for the paper "Expanding CO Shells in the Orion A Molecular Cloud", Feddersen et al. (2018). CO channel maps of shells are in fset2.zip, CO position-velocity diagrams are in fset3.zip, and integrated CO and infrared images are in fset4.zip</p>
Co-crystal structures of USP5 Zf-UBD and weak binding compounds
<p>Determination of crystallization conditions which allow growth of well-diffracting co-crystals of USP5 zinc finger ubiquitin binding domain (Zf-UBD) and compounds shown to bind weakly by <a href="https://zenodo.org/record/1283325#.Wz5VXtJKjIV">19F NMR</a> and <a href="https://zenodo.org/record/1311686#.W0il_tJKjIU">SPR assays</a> & to solve the co-crystal structures to determine if electron density of ligands can be seen in the binding pocket of the protein domain.</p>
Ideas and feedback collected during the INVITE Co-creation Workshop
<p>In the framework of the INVITE Co-Creation Workshop, a diverse group of OI stakeholders engaged in a series of co-creative brainstorming and ideation sessions to co-define demand-driven designs and features for INVITE’s pilots and OI2 Lab. During the workshop six tables were set up to discuss six themes, each of which addressed key aspects of the pilots, services and tools to be deployed through the OI2 Lab. The discussions followed a semi-structured approach and were guided by six key questions, unique to each theme. With that in mind, this dataset comprises of both qualitative and quantitative data, as derived from the participants’ responses to each of these six questions per theme along with any additional ideas. </p>
Raw diffraction images of plant vacuolar iron transporter VIT1 (with Zn and Co)
<p>Diffraction images of full-length VIT1 related to PDB codes <a href="https://www.rcsb.org/structure/6IU3">6IU3</a> (Zn-bound) and <a href="https://www.rcsb.org/structure/6IU4">6IU4</a> (Co-bound). All data were collected from loop-harvested (micro)crystals on BL32XU, SPring-8 using EIGER X 9M detector.<br> <br> 6IU3: Helical (20, 45, or 90°/crystal) datasets were collected automatically using ZOO system at a wavelength of 1 Å.<br> 6IU4: Small-wedge (10°/crystal) and helical (120°/crystal) datasets were collected at a wavelength of 1.28 Å.<br> <br> The crystals belonged to space group <em>C</em>222<sub>1</sub> with unit cell parameter a~47, b~290, c~46 Å. All datasets were processed and merged using KAMO pipeline with XDS.</p> <p>Related entries: <a href="https://zenodo.org/record/2532134">metal binding domain</a>, <a href="https://zenodo.org/record/2532138">data for phasing by Hg-SIR</a> <br> </p>
Data repository for the paper titled: "The intriguing co-distribution of the copepods Calanus hyperboreus and Calanus glacialis in the subsurface chlorophyll maximum of Arctic seas"
<p>Data and R-code for publication: “The intriguing co-distribution of the copepods <em>Calanus hyperboreus </em>and <em>Calanus glacialis </em>in the subsurface chlorophyll maximum of Arctic seas”.</p> <p> </p> <p>By Moritz S Schmid and Louis Fortier</p> <p> </p> <p>Attached are R data files of copepod lipids, copepod vertical distributions, and chl <em>a </em>profiles, as well as R-code.</p> <p>The following sea ice data was used: Nimbus-7 SMMR and DMSP SSM/I-SSMIS passive microwave data, available at : https://nsidc.org/data/nsidc-0051.</p> <p>The following ocean color data was used: MODerate resolution Imaging Spectroradiometer (MODIS), Aqua satellite, available here: https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua</p> <p> </p> <p>Regards</p>
The outcomes of carbon-oxygen white dwarfs accreting CO-rich material
<p>MESA inlist associated files for <a href="https://ui.adsabs.harvard.edu/#abs/2019MNRAS.483..263W/abstract">The outcomes of carbon-oxygen white dwarfs accreting CO-rich material</a></p>
Dataset for "Co-Localization of Microstructural Damage and Excessive Mechanical Strain at Aortic Branches in Angiotensin-II Infused Mice"
<p>This dataset contains geometries and simulation files for the manuscript "Co-Localization of Microstructural Damage and Excessive Mechanical Strain at Aortic Branches in Angiotensin-II Infused Mice", to be published in the journal "Biomechanics and Modeling in Mechanobiology".</p> <p>For each animal described in the study, the following data are uploaded:</p> <p>[mouse name]_Strain.vtp contains the Eulerian strain estimation for each abdominal aorta mapped back on the ex vivo undeformed configuration.</p> <p>[mouse name]_Scan.vtp is the abdominal aorta in the ex vivo undeformed configuration, as scanned using PCXTM imaging.</p> <p>[mouse name]_Exitron.vtp is the distribution of contrast agent infiltration along the aorta (can be superimposed to the corresponding ‘scan.vtp’ file), which serves a surrogate for vascular damage.</p> <p>The aortic partitioning described in the paper’s method section was performed on all of the above .vtp files for every animal. .vtp files can be visualized in the open-source Paraview or similar software, and used for centerline calculations using the open-source vmtk software.</p>
Raw and processed anomalous diffraction data for crystals of metal-free R2lox soaked with Mn, Fe and Co or Zn
<p>Raw and processed anomalous diffraction data for crystals of metal-free <em>Geobacillus kaustophilus</em> R2-like ligand-binding oxidase (R2lox) soaked with 5 mM each MnCl<sub>2</sub>, (NH<sub>4</sub>)<sub>2</sub>Fe(SO<sub>4</sub>)<sub>2</sub> and CoCl<sub>2</sub> or ZnCl<sub>2</sub>. Data were collected on two crystals each after soaking with Mn, Fe and Co or Mn, Fe and Zn, respectively. For each crystal, one dataset each was collected at the Fe K edge (7162 eV), Mn K edge (6589 eV) and Co K edge (7721 eV) or Zn K edge (9664 eV; X-ray energies corresponding to the theoretical K edge + 50 eV). Data were collected at 100 K on a Pilatus 6M detector at beamline X06SA of the Swiss Light Source (SLS, Villigen, Switzerland) on September 23, 2012, and processed with XDS and XSCALE. All raw and processed diffraction data for each crystal are compressed into one file. </p>
ScienceDex guides
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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.