Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
234
datasets available to search
ShareScore release 0.7.1
Dataset results
234 results for “SAM”
Simulations of METTL3/METTL14 in complex with SAM+ADE or SAH+m6ADE
<p>The current dataset contains a set of MD simulation trajectories for the METTLL3/METTL14 heterodimer in complex with the set of (co-)substrates or (co-)products SAM+ADE or SAH+m6ADE respectively. Each compressed file contains 16 independent trajectories of 500 ns each. The trajectories are written in .xtc format, and therefore a .pdb file is needed to read them. The parameters file ("md_input.mdp") is also provided. The current dataset also contains the PLUMED file that was used for the DFTB3/MM metadynamics simulations ("plumed.dat").</p>
DE SAM 2018
<p>DE SAM for 2018. </p> <p>Method available from:</p> <p>Emonts-Holley, T., Ross, A., & Swales, J. (2014). A social accounting matrix for Scotland. Fraser of Allander Economic Commentary, 38(1), 84-93. <a href="http://pureportal.strath.ac.uk/en/publications/a-social-accounting-matrix-for-scotland">https://pureportal.strath.ac.uk/en/publications/a-social-accounting-matrix-for-scotland </a></p>
SAM-ENSO Index (SEI)
<p>Note: This v.2 updates the SEI data up to the 01.03.2024 as new SAM and ENSO data became available.</p> <p>The SAM-ENSO climate index (SEI) provided here combines the Southern Anular Mode (SAM) index (Marshall, 2003) with the Oceanic Niño Index (ONI) (Bamston et al., 1997; Huang et al., 2016), taking into account that their opposing phases reinforce each other in their overlapping effects on the wind field around Antarctica (Fogt et al., 2011; McKee et al., 2011; Stammerjohn et al., 2008).</p> <p>\(SEI = {SAM \over std(SAM)} - {ONI \over std(ONI)}\)</p> <p>The SEI has been first employed in Llanillo et al. (2023).</p> <p> </p> <p><strong>References:</strong></p> <p>Bamston, A. G., Chelliah, M., & Goldenberg, S. B. (1997). Documentation of a highly enso-related sst region in the equatorial pacific: Research note. <em>Atmosphere - Ocean</em>, <em>35</em>(3), 367–383. https://doi.org/10.1080/07055900.1997.9649597</p> <p>Fogt, R. L., Bromwich, D. H., & Hines, K. M. (2011). Understanding the SAM influence on the South Pacific ENSO teleconnection. <em>Climate Dynamics</em>, <em>36</em>(7), 1555–1576. https://doi.org/10.1007/s00382-010-0905-0</p> <p>Huang, B., Thorne, P. W., Smith, T. M., Liu, W., Lawrimore, J., Banzon, V. F., Zhang, H. M., Peterson, T. C., & Menne, M. (2016). Further exploring and quantifying uncertainties for extended reconstructed sea surface temperature (ERSST) version 4 (v4). <em>Journal of Climate</em>, <em>29</em>(9), 3119–3142. https://doi.org/10.1175/JCLI-D-15-0430.1</p> <p>Llanillo, P.J., Kanzow, T., Janout, M. and Rohardt, G. (2023): The Deep-Water Plume in the northwestern Weddell Sea, Antarctica: Mean state, seasonal cycle and interannual variability influenced by climate modes. <em>JGR-Oceans (accepted).</em></p> <p>Marshall, G. J. (2003). Trends in the Southern Annular Mode from observations and reanalyses. <em>Journal of Climate</em>, <em>16</em>(24), 4134–4143. https://doi.org/10.1175/1520-0442(2003)016<4134:TITSAM>2.0.CO;2</p> <p>McKee, D. C., Yuan, X., Gordon, A. L., Huber, B. A., & Dong, Z. (2011). Climate impact on interannual variability of Weddell Sea Bottom Water. <em>Journal of Geophysical Research: Oceans</em>, <em>116</em>(5), 1–17. https://doi.org/10.1029/2010JC006484</p> <p>Stammerjohn, S. E., Martinson, D. G., Smith, R. C., Yuan, X., & Rind, D. (2008). Trends in Antarctic annual sea ice retreat and advance and their relation to El Niño–Southern Oscillation and Southern Annular Mode variability. <em>Journal of Geophysical Research</em>, <em>113</em>(C3), C03S90. https://doi.org/10.1029/2007JC004269</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
ACF database on the predictors of time to recovery and non-response to SAM treatment in the MANGO trial
<p>This database contains the variables used to analyse the predictors of time to recovery and non-response to treatment of SAM in Burkina Faso. These results are have been submitted to review in Plos One in November 2020.</p>
Conversion of fluoride and chloride catalized by SAM-dependent fluorinase in Nocardia brasiliensis
<p>Data sets show the following reactions:</p> <p>- Fluorinase catalized conversion of fluoride and SAM to 5'-FDA and L-methionine (Explanation file: Figure 2).</p> <p>- Fluorinase catalyzed conversion of chloride and SAM to 5’-ClDA and L-methionine in the presence of L-amino acid oxidase (Explanation file: Figure 3).</p>
SAM_BOMEX_OUTPUT
<p>This is a simulation result for the paper titled: Life Cycle Evolution of Inhomogeneous Mixing in Shallow Cumulus Clouds in JGR: Atmosphere. </p><p>Information for the publication will be added after the review process.</p><p>Content: </p><ol><li><a href="https://zenodo.org/api/records/10211789/draft/files/SAM_profile.tar/content">SAM_profile.tar</a> is a group of profile data (.nc format) for all 12 cases with different boundary layer humidity and aerosol concentrations to make Fig.2. Descriptions of the variables are included.</li><li><a href="https://zenodo.org/api/records/10211789/draft/files/prof.tar/content">prof.tar</a> is a processed set of profiles from individual clouds for all cases used to make Figs.1a, 7, 8, 9, and 10. the number after the file name prof indicates: (1, in-cloud, 2, environmental shell, 3: mixing region, 4: cloud core, and 5: cloud edge.)</li><li><a href="https://zenodo.org/api/records/10211789/draft/files/all_cloud_volume_data.zip/content">all_cloud_volume_data.zip</a> is the Python Dataframe of total cloud average data to make Figs. 1c, 4, 5, and 6. Each row represents an individually detected cloud, and each column shows the total cloud volume-averaged properties of the respective name.</li></ol>
Enzyme Substrate Classification Dataset for SDRs and SAM-MTases
<p>This dataset contains sequence information, three-dimensional structures (from AlphaFold2 model), and substrate classification labels for 358 short-chain dehydrogenase/reductases (SDRs) and 953 S-adenosylmethionine dependent methyltransferases (SAM-MTases).</p> <p>The aminoacid sequences of these enzymes were obtained from the UniProt Knowledgebase (https://www.uniprot.org). The sets of proteins were obtained by querying using InterPro protein family/domain identifiers corresponding to each family: IPR002347 (SDRs) and IPR029063 (SAM-MTases). The query results were filtered by UniProt annotation score, keeping only those with score above 4-out-of-5, and deduplicated by exact sequence matches.</p> <p>The structures were submitted to the publicly available AlphaFold2 protein structure predictor (J. Jumper et al., Nature, 2021, 596, 583) using the ColabFold notebook (https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.1-premultimer/batch/AlphaFold2_batch.ipynb, M. Mirdita, S. Ovchinnikov, M. Steinegger, Nature Meth., 2022, 19, 679, https://github.com/sokrypton/ColabFold). The model settings used were msa_model = MMSeq2(Uniref+Environmental), num_models = 1, use_amber = False, use_templates = True, do_not_overwrite_results = True. The resulting PDB structures are included as ZIP archives</p> <p>The classification labels were obtained from the substrate and product annotations of the enzyme UniProtKB records. Two approaches were used: substrate clustering based on molecular fingerprints and manual substrate type classification. For the substate clustering, Morgan fingerprints were generated for all enzymatic substrates and products with known structures (excluding cofactors) with radius = 3 using RDKit (https://rdkit.org). The fingerprints were projected onto two-dimensional space using the UMAP algorithm (L. McInnes, J. Healy, 2018, arXiv 1802.03426) and Jaccard metric and clustered using k-means. This procedure generated 9 clusters for SDR substrates and 13 clusters for SAM-MTases. The SMILES representations of the substrates are listed in the SDR_substrates_to_cluster_map_2DIMUMAP.csv and SAM_substrates_to_13clusters_map_2DIMUMAP.csv files.</p> <p><br> The following manually defined classification tasks are included for SDRs: NADP/NAD cofactor classification; phenol substrate, sterol substrate, coenzyme A (CoA) substrate. For SAM-MTases, the manually defined classification tasks are: biopolymer (protein/RNA/DNA) vs. small molecule substrate, phenol subsrates, sterol substrates, nitrogen heterocycle substrates. The SMARTS strings used to define the substrate classes are listed in substructure_search_SMARTS.docx.<br> </p>
Figure 4. Pachydyptes simpsoni holotype, SAM P14157 in A review of Australian fossil penguins (Aves: Sphenisciformes)
Figure 4. Pachydyptes simpsoni holotype, SAM P14157: A, right radius in ventral view, left carpometacarpus and left phalanx II-1 in dorsal view; B, head of right humerus in dorsal view; C, left coracoid in dorsal view; D,?twelfth cervical vertebra in ventral view.
Figure 8. Sphenisciformes indet. partial right humerus, SAM P10863 in A review of Australian fossil penguins (Aves: Sphenisciformes)
Figure 8. Sphenisciformes indet. partial right humerus, SAM P10863: A, dorsal view; B, ventral view.
Figure 5. Pachydyptes simpsoni paratype, SAM P14158 in A review of Australian fossil penguins (Aves: Sphenisciformes)
Figure 5. Pachydyptes simpsoni paratype, SAM P14158: proximal end of right radius in (A) ventral view and (C) dorsal view; partial right humerus in (B) ventral and (D) dorsal views.
Linked collectors and determiners for: Denver Botanic Gardens, Sam Mitchel Herbarium of Fungi.
Natural history specimen data linked to collectors and determiners held within, "Denver Botanic Gardens, Sam Mitchel Herbarium of Fungi". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/a4f29a62-d170-4775-b29a-09deaf138ee4">https://bionomia.net/dataset/a4f29a62-d170-4775-b29a-09deaf138ee4</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a4f29a62-d170-4775-b29a-09deaf138ee4">https://gbif.org/dataset/a4f29a62-d170-4775-b29a-09deaf138ee4</a>. Formatted as a Frictionless Data package.
Figure 83. SAM A35586, reproductive system. A in A taxonomic revision of Paradoris sea slugs (Mollusca, Gastropoda, Nudibranchia, Doridina)
Figure 83. SAM A35586, reproductive system. A, general view, scale = 1 mm. B, general view (organs moved), scale = 1.8 mm.
Figure 82. SAM A35586. A in A taxonomic revision of Paradoris sea slugs (Mollusca, Gastropoda, Nudibranchia, Doridina)
Figure 82. SAM A35586. A, left, outermost teeth, scale = 20 µm. B, right, outermost teeth, scale = 30 µm. C, innermost teeth, scale = 30 µm. D, right lateral teeth, scale = 30 µm. E, radula, scale = 300 µm.
Figure 6. Fossil tadornine bones compared with modern Tadorna tadornoides SAM B.39591 in Descriptions and phylogenetic relationships of two new genera and four new species of Oligo-Miocene waterfowl (Aves: Anatidae) from Australia
Figure 6. Fossil tadornine bones compared with modern Tadorna tadornoides SAM B.39591. Tadorna tadornoides: A,C. proximal right carpometacarpus; and H, dorsal view cranial half coracoid. Fossils referred to Australotadorna alecwilsoni: B, D, E, proximal right carpometacarpus; F, distal right tibiotarsus SAM P.36762 in anterior view; G, cranial part right coracoid (SAM P.24531) in dorsal aspect; I, cranial part right coracoid (SAM P.43137) in dorsal aspect. Fossils referred to an undetermined tadornine from Alcoota: J, left radius UCMP 65985 in dorsal aspect; and right carpometacarpus NT P.2913 in K, ventral; L, dorsal; and M, caudal views. Scale bars = 10 mm. See main text for abbreviations.
SAM simulation output
<p>This is the simulation output of the submitted manuscript "Between Broadening and Narrowing: How Mixing Affects the Width of the Droplet Size Distribution" to the Journal of Geophysical Research: Atmospheres. </p> <p>tar file includes model output, including all variables used in the paper. The description of each variable is indicated in the nc file header. Each nc file is an output of a single time step in the whole model domain, where the simulation timestep is indicated as (time) at the end of each file (e.g., 000001440). The model timestep (dt) is 0.5s. </p> <p>Files named SCMS_REVISON_200_(time)_mod.nc are the results from simulation with a linear eddy model (LEM) and SCMS_REVISION_NOLEM_200_(time)_mod.nc are the results from the simulation without a LEM.</p> <p> </p>
RNA Ensembles From Solvent Accessibility Data: Application to the SAM-I Riboswitch Aptamer Domain
<p>SASA-derived ensembles of the -SAM and +SAM states of the SAM-responsive riboswitch.</p>
King scallop (<i>Pecten maximus</i>) growth monitoring - SAMS IMTA Lab
<p> A small number of hand-dived King scallops (Pecten maximus) was deployed at SAMS IMTA lab and monitored for its growth and performance in suspended cultivation, alongside existing seaweed cultivation.</p> <p>The dataset contains repeated measures of 8 individual scallops (A1-4, B1-4) for growth in shell length and -height over time (Jan 2023 - ongoing).</p>
Queen scallop (<i>Aequipecten opercularis</i>) growth monitoring - SAMS IMTA Lab
<p>Queen scallop (Aequipecten opercularis) spat naturally settled on shellfish grow-out strutcures deployed at SAMS IMTA Lab; in particular within nestier trays holding King scallops. Spat was translocated into separate nestier trays deployed beneath King scallop trays in August 2023. Every 3-4 months, scaled photographs were recorded of the on-growing Queen scallops, with images being uploaded to ImageJ for measurement of individual's shell height and their growth over time (Aug 2023 - July 2024).</p>
UU Webinar 2: Sam Sellar on time and value in digitalised higher education
<p>Title: The investment of time: Divergent theses on the value of higher education</p> <p>Abstract: This webinar will focus on what is valuable in and about higher education. It will then discuss this in relation to Edtech and deliberate where Edtech can and cannot contribute to supporting the sector.</p> <p>Speaker: Sam Sellar</p> <p>Bio: Sam Sellar is Dean of Research (Education Futures) and Professor of Education Policy at the University of South Australia. Sam’s research focuses on education policy, large-scale assessments and the datafication of education. Sam also works closely with teacher organisations around the world to understand the impact of digitalisation on teacher professional autonomy. His most recent book is titled Algorithms of education: How datafication and artificial intelligence shape policy (University of Minnesota Press), co-authored with Kalervo N. Gulson and P. Taylor Webb.</p> <p>Link to Sam’s website: https://people.unisa.edu.au/Sam.Sellar </p> <p>Date of event: 22 June 2023</p>
Figure 6. Sphenisciformes indet. left humerus, SAM P7158 in A review of Australian fossil penguins (Aves: Sphenisciformes)
Figure 6. Sphenisciformes indet. left humerus, SAM P7158: A, dorsal view; B, ventral view.
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.