Find research datasets worth reusing

opencc-by-4.0Apr 2022View details →

Doodleverse/Segmentation Zoo UNet models for identifying water in orthomosaic photos of coasts.

Doodleverse/Segmentation Zoo UNet models for identifying water in orthomosaic photos of coasts.   Based on Coast Train data Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/P91NP87I">https://doi.org/10.5066/P91NP87I</a>. These model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. RGB (3 band): red, green, blue Classes are: {0: null, 1: water}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.   2. '.h5' weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function  `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images   3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model   References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym

opencc-by-4.0Apr 2022View details →

Identified journal descriptor (JD), semantic type (ST), and MAUI keywords for PubMed/MEDLINE articles

The Journal descriptor (JD) and Semantic type (ST) of PubMed articles were identified using a tool, called Journal descriptor indexing (JDI). MAUI keywords are identified by the MAUI tool and they can be used as a complement to MESH terms, as MeSH keywords are not always available in all PubMed articles. The methods of building the datasets can be found in the two articles: "Author name disambiguation in MEDLINE based on journal descriptors and semantic types" and " Exploring author name disambiguation on PubMed-scale" The PubMed database used is the 2019 baseline version, the number of articles in the two datasets are as follows: $ wc -l pubmed-paper-jd-st.tsv  29796281 pubmed-paper-jd-st.tsv $ wc -l pubmed-paper-maui-keywords.tsv  26509210 pubmed-paper-maui-keywords.tsv

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Stylianos Chatzimanolis, <a href="https://orcid.org/0000-0001-9008-0611">https://orcid.org/0000-0001-9008-0611</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

Genome-wide sequencing identifies a thermal tolerance related synonymous mutation in the mussel Mytilisepta virgata

The roles of 'silent' synonymous mutations for organisms adapting to stressfully thermal environments are of fundamental biological and ecological interests but poorly understood. To study whether synonymous mutations influence the thermal adaptation of animals to heat stress at specific microhabitats, a genome-wide genotype-phenotype association analysis was carried out in the black mussels Mytilisepta virgata inhabiting different microhabitats. A synonymous mutation of Ubiquitin-specific Peptidase 15 (MvUSP15) was significantly associated with the physiological upper thermal limit of the mussel. The individuals carrying GG genotype (the G-type) at the mutant locus owned significantly lower heat tolerance compared to the individuals carrying GA and AA genotype (the A-type). Furthermore, when heated to sublethal temperature, the G-type exhibited higher inter-individual variations in the MvUSP15 expression, especially for the mussels on the sun-exposed microhabitats. Taken together, a synonymous mutation in MvUSP15 can affect the gene expression profile and interact with microhabitat heterogeneity to influence thermal resistance. This integrative study sheds light on the ecological importance of adaptive synonymous mutations as an underappreciated genetic buffer against heat stress and emphasizes the importance of integrative studies with the consideration of genetic, physiological, and environmental heterogeneity at a microhabitat scale for evaluating and predicting the impacts of climate change.

Reducing risky interactions: Identifying barriers to the successful management of human-wildlife conflict in an urban parkland

Managing activities that result in human-wildlife conflict is a challenging goal for modern scientists and managers. In recent years, the self-motivated feeding of wildlife by humans has garnered popularity but with consequent risks for the health and safety of both parties. This has resulted in calls for management in areas of high contact, e.g., parklands. Traditional controls are typically utilised (i.e., signage, patrols), yet their success is varied, leading to a rise in research aiming to improve them. This research has primarily focused on language and design, with little attention paid to the role that audience type (i.e., international tourists versus locals/residents) may play in their success. Proportions in audience type present can vary both between parks and spatially within a single park, however, controls are usually applied homogeneously with no consideration for how response may vary between these groups. Here, we performed a robust before-after study across two summers using a wild fallow deer population in a public park that are commonly fed by visitors as our model. We deployed controls, following best practices as outlined by the literature, and tested their overall effectiveness. We then identified key areas with differences in visitor type proportions and tested for variation in success between them. We found that the number of visitors feeding the deer significantly decreased overall after the introduction of controls, although interactions were not eliminated entirely. We discovered that the effectiveness of these controls varied with changes in visitor type, with the most positive effects occurring in areas with more international tourists and no significant effect occurring in areas dominated by resident visitors. Notably, of the food offerings remaining, the proportion of foods that could be perceived as 'nutritionally beneficial' increased in both sites, marking overall changes in the behaviours of even those visitors who refused to stop feeding. These findings highlight the importance of target audience research in human-wildlife conflict management. We recommend that authorities aiming to reduce these interactions perform systematic surveys to identify the audience type present and cater controls accordingly to maximise their success.

opencc-by-4.0May 2022View details →

Data from: Phylogenetic distribution and expression pattern analyses identified a divergent basal body assembly protein involved in land plant spermatogenesis

<pre>Data from: Phylogenetic distribution and expression pattern analyses identified a divergent basal body assembly protein involved in land plant spermatogenesis Author information Shizuka Koshimizu1, Naoki Minamino2, Tomoaki Nishiyama3, Emiko Yoro4, Mayuko Sato5, Mayumi Wakazaki5, Kiminori Toyooka5, Kazuo Ebine2,6, Keiko Sakakibara4, Takashi Ueda2,6, and Kentaro Yano1 1 School of Agriculture, Meiji University, Kawasaki 214-8571, Japan 2 Division of Cellular Dynamics, National Institute for Basic Biology, Okazaki 444-8585, Japan 3 Research Center for Experimental Modeling of Human Disease, Kanazawa University, Kanazawa 920-0934, Japan 4 Department of Life Science, Rikkyo University, Tokyo 171‐8501, Japan 5 RIKEN Center for Sustainable Resource Science, Yokohama 230-0045, Japan 6 Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Studies), Okazaki 444-8585, Japan This directory contains 0_README (this file) 1_Marchantia_FL_data 2_Physcomitrella_FL_data 3_Physcomitrella_TEM_images 4_PAML 1_Marchantia_FL_data Raw image data using quantification of spermatid phenotypes in Marchantia. quantification.xlsx: Summary of quantification data. 211111 and 211210: Observation date. ∟Mpbld10-1, Mpbld10-2, and Tak-1: Observed lines. ∟raw data: *.lsm files are raw image data. ∟binary image: *.tif files are binarization images of the Hoechst33342 data. ∟DIC: *.tif files are maximum intensity projection images of the C2 channels (detection of DIC images) of the raw data. ∟Hoechst33342: *.tif files are maximum intensity projection images of the C1 channels (detection of Hoechst33342 signals) of the raw data. 2_Physcomitrella_FL_data Merged data of DIC and Hoechst33342 signal images using quantification of spermatid phenotypes in Physcomitrella. line22-*.png: The images of Ppbld10-22 mutant. line30-*.png: The images of Ppbld10-30 mutant. wt.png: The images of wild type. Number in the images 1: With flagella 2: Without flagella 3_Physcomitrella_TEM_images TEM images of spermatids in the Ppbld10-30 mutant, except for images shown in Supporting Information Fig. S11. 4_PAML Files using analysis by PALM. input.fasta: Input multi fasta file. species_tree.nwk: The gene tree file for the global clock model (rooted using chlorophytes as an outgroup). species_tree_marked.nwk: A gene tree file with marks specifying land plant stem and crown branches as category #1 (for a local clock model). clock1: A directory containing output files of a PAML run with the global clock model (clock = 1) clock2: A directory containing output files of a PAML run with a local clock model (clock = 2). The branches assumed to have a different rate (r1) than the default rate r0 are specified with #1 in species_tree_marked.nwk.</pre>

opencc-by-4.0May 2022View details →

Project files provided as supporting information to the manuscript "Information-theoretical measures identify accurate low-resolution representations of protein configurational space"

The dataset contains the following compressed folder: -Notebooks.zip: This folder contains:   -python_script:         -RESREL.py: script performing the clusterization and computing the relevance resolution curves         -random_curves.py: script generating the random value and computing the corresponding RES-REV curves_s         -Cluster_distance_matrix.py: script returning the distance among clusters for a given partition.   -python_notebook:         -Exploratory_analysis.ipynb:  Analysis performed on the 12-protein_dataset         -DMAPS_ANTI.ipynb: Diffusion Map for the Antibody         -DMAPS_COV_1ake.ipynb: Diffusion Map + Inter-Intra state decomposition of covariance for 1ake Packages required for the usage of these python scripts/notebooks:   -numpy   -pandas   -matplotlib   -seaborn   -multiprocessing   -scipy   ======== RAW DATA ======== The raw data produced and employed in this study are available on a Google Drive folder at the following address: https://drive.google.com/drive/folders/1PasAUCgpR5-gdzUVEdyusgZIayQN0Le9 In this folder, together with the compressed Notebooks.zip folder, one can fin the compressed folder Data.zip, within which the following data are present: -12-protein_dataset:     -md.mdp: the .mdp file used in the MD simulations     -PROTEIN_PDB_CODE:         -Hk_{sel}.npy & Hs_{sel}.npy: the Rel & Res curves, sel=[all, CA, CB]         -RMSD_{sel}.npy: the RMSD matrix, sel=[all, CA, CB]         -npt.gro:protein+water+ions structure @TEO the equilibration (NVT+NPT)         -MSR_df.csv: a dataset containing the following columns                   'area' : area behind the Relevance-Resolution curve;                   'selection': the atomic selection (['all', 'CA', 'CB']) used to compute the RMSD matrix used for the clusterization (and consequently the Relevance-Resolution curves)                   'method': the linkage measure used in the clustering procedure, an integer in [0,6];                   'method_name': the linkage measure used in the clustering procedure, a string in ['average','ward','complete','single','centroid','median','weighted'];                   'rmsd_mean': the mean value of the rmsd vector along the trajectory computed wrt the first frame;                   'rmsd_var': the variance of the rmsd vector along the trajectory computed wrt the first frame;                   'rgy_mean': the mean value of the radius of gyration  along the trajectory;                   'rgy_var': the variance of the radius of gyration  along the trajectory;                   'rmsf_mean': the mean value of the rmsf;                   'rmsf_var': the variance of the rmsf;                   'RMSD_M_mean': the mean value of the RMSD matrix.                   'RMSD_M_var': the variance of the RMSD matrix.   -Random:     -curves.npy= 100K Relevance-Resolution Random curves for M=40001     -curves_s.npy= 100K Relevance-Resolution Random curves for M=15000   -validation_dataset:     -antibody:         -Hk_CB.npy & Hs_CB.npy: the Rel & Res curves         -RMSD_CB.npy: the RMSD matrix         -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space         -Label_{method}.npy: the label vector for n_clusters     -1ake:         -Hk_{sel}.npy & Hs_{sel}.npy: the Rel & Res curves         -RMSD_{sel}.npy: the RMSD matrix         -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space         -Label_{method}.npy: the label vector for n_clusters         -intra_{m}.npy: the intra-cluster covariance matrix         -inter_cov_{m}.npy: the inter-cluster correlation matrix   NOTE ===== The matrices of the cluster distances for adenylate kinase and antibody have been computed through the script Cluster_distance_matrix.py. These matrices have not been included in the dataset because of their large size; the raw data are however available upon request.

RNA allows identifying the consumption of carrion prey

Facultative scavenging by predatory carnivores is a prevalent but frequently underestimated feeding strategy. DNA-based methods for diet analysis, however, do not allow to distinguish between scavenging and predation, thus, the significance of scavenging on population dynamics and resource partitioning is widely unknown. Here, we present a methodological innovation to differentiate between scavenging and fresh prey consumption using prey RNA as a target molecule. We hypothesized that the rapid post-mortem breakdown of RNA in prey tissue should lead to a significantly lower detection probability of prey RNA than DNA when carrion rather than fresh prey is consumed. To test this hypothesis, ground beetles (Pseudoophonus rufipes (De Geer)) were offered either fresh or one-day-old dead Drosophila melanogaster fruit flies (carrion). The detectability of prey RNA and DNA in the beetles' regurgitates was assessed with diagnostic Drosophila-specific RT-PCR and PCR assays at 0, 6, 12, 24 and 48 hours post-feeding. After fresh fly consumption, prey RNA and DNA were detectable equally well at all times. When carrion prey was consumed, the detection strength of prey RNA immediately after feeding was significantly lower than that of prey DNA and reached zero in most samples within 6 hours of digestion. Our findings provide evidence that prey RNA allows distinguishing between the consumption of fresh and scavenged prey, thereby overcoming a long-known weakness of molecular diet analysis. The assessment of prey RNA offers a generally applicable approach for examining the importance of scavenging in food webs to unravel its functional consequences for populations, communities, and ecosystems.

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by David Peter Shorthouse, <a href="https://orcid.org/0000-0001-7618-5230">https://orcid.org/0000-0001-7618-5230</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

opencc-by-4.0Jun 2022View details →

Data from: Using functional traits to identify conservation priorities for the world's crocodylians

Over half of all crocodylians are threatened with extinction, with many species considered to be functionally extinct throughout their historical ranges. How such losses impact crocodylian functional diversity (FD) and its conservation is poorly understood, but can be investigated using measurable traits. Where such information is unavailable, phylogenetic diversity (PD) has been proposed as an effective strategy to capture FD, but this assumption is not well tested. We constructed a global trait database for 28 extant crocodylians [uploaded here], and quantified their functional diversity, distinctiveness and groupings. Functionally distinctive crocodylians include the most and least evolutionarily distinct species, likely because unusual trait values and combinations can be produced by both evolutionary isolation and evolutionarily recent adaptive radiation. We identified four functional groups of crocodylians, within which species have similar ecological roles.  Highly distinctive species, such as the gharial, fit less well into functional groups. We combined functional distinctiveness and threat ranking scores to prioritise species for conservation. The metric is termed EcoDGE (Ecologically Distinct and Globally Endangered), and we tested how well it operates to preserve crocodylian FD amongst extant Crocodylia. Under current extinction scenarios, 32-38% of crocodylian FD might be lost within 100 years. Losses increase to 48% if all threatened species go extinct, with Asia identified as a hotspot of threatened FD in crocodylians. Highly-threatened distinctive species lead this extinction scenario, exacerbated by clusters of species sharing traits linked to high extinction risk. These traits include habitat specialism and lower reproductive investment; in contrast, species that tolerate climate extremes are more resilient. Prioritising at-risk species based on functional distinctiveness and threat status appears to be an effective way to conserve present-day crocodylian diversity. Conservation prioritisation based on the EcoDGE metric performed well to minimize projected losses in FD. Among extant crocodylians, FD losses are projected when high diversity overlaps with high extrinsic threats. We then examined the utility of PD-based measures as surrogates for FD conservation, to better understand the advantages and drawbacks of such substitutions. A PD-based measure such as the EDGE metric, provided an effective tool to capture functional diversity in our tests.

opencc-by-4.0May 2022View details →

Mean relative abundance of fungal and bacterial ASV identified in vegetables and fruits.

Supplementary table S1. Mean relative abundance of fungal and bacterial ASV identified in vegetables and fruits.

Data From: Advancing fence datasets: Comparing approaches to identify fence locations and specifications in southwest Montana

Fencing is a major anthropogenic feature affecting human relationships, ecological processes, and wildlife distributions and movements, but its impacts are difficult to quantify due to a widespread lack of spatial data. We created a fence model and compared outputs to a fence mapping approach using satellite imagery in two counties in southwest Montana, USA to advance fence data development for use in research and management. The model incorporated road, land cover, ownership, and grazing boundary spatial layers to predict fence locations. We validated the model using data collected on randomized road transects (n = 330). The model predicted 34,706.4 km of fences with a mean fence density of 0.93 km/km2 and a maximum density of 14.9 km/km2. We also digitized fences using Google Earth Pro in a random subset of our study area in survey townships (n = 50). The Google Earth approach showed greater agreement (K = 0.76) with known samples than the fence model (K = 0.56) yet was unable to map fences in forests and was significantly more time intensive. We also compared fence attributes by land ownership and land cover variables to assess factors that may influence fence specifications (e.g., wire heights) and types (e.g., number of barbed wires). Private lands were more likely to have fences with lower bottom wires and higher top wires than those on public lands with sample means at 22 cm and 26.4 cm, and 115.2 cm and 110.97, respectively. Both bottom wire means were well below recommended heights for ungulates navigating underneath fencing (≥ 46 cm), while top wire means were closer to the 107 cm maximum fence height recommendation. We found that both fence type and land ownership were correlated (χ2 = 45.52, df = 5, p = 0.001) as well as fence type and land cover type (χ2 = 140.73, df = 15, p = 0.001). We provide tools for estimating fence locations, and our novel fence type assessment demonstrates an opportunity for updated policy to encourage the adoption of "wildlife-friendlier" fencing standards to facilitate wildlife movement in the western U.S. while supporting rural livelihoods.

Whole-transcriptome sequencing identifies neuroinflammation, metabolism and blood-brain barrier related processes in the hippocampus of aged mice during perioperative period

Aim: Perioperative neurocognitive disorders (PND) occur frequently after surgery and anesthesia, especially in aged patients. Previous studies have shown multiple PND related mechanisms in the hippocampus, however, their relationships remain unclear. Meanwhile, the perioperative neuropathological processes are sophisticated and changeable, single period study could not reveal the accurate mechanisms. Thus, multiperiod whole-transcriptome study is necessary to elucidate the gene expression patterns during perioperative period. Methods: Aged C57BL/6 mice were subjected to exploratory laparotomy under sevoflurane anesthesia. Whole-transcriptome sequencing (RNA-seq analysis) was performed on the hippocampi from control condition (Con), 30 minutes (Day0), 2 days (Day2) and 7 days (Day7) after surgery. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes analyses, quantitative Real-Time PCR, immunofluorescence and fear conditioning test were also performed to elucidate the pathological processes and modulation networks during the period. Results: Through RNA-seq analysis, 328, 3597 and 4179 differentially expressed genes (DEGs) were screened out in intraoperative period (Day0 vs Con), early postoperative period (Day2 vs Day0) and late postoperative period (Day7 vs Day2). The involved GO biological processes were divided into 9 categories, and positive-regulated processes were more than negative-regulated ones. Seventy-four transcription factors were highlighted. The potential synaptic and neuroinflammatory pathways were constructed for Neurotransmitter, Synapse and Neuronal alteration categories with 9 DEGs (Htr1a, Rims1, Ezh2, etc.). The metabolic and mitochondrial pathways were constructed for Metabolism, Oxidative stress and Biological rhythm categories with 9 DEGs (Gpld1, Sirt1, Cry2, etc.). The blood-brain barrier and neurotoxicity related pathways were constructed for Blood-brain barrier, Neurotoxicity and Cognitive function categories with 10 DEGs (Mmp2, Itpr1, Nrf1, etc.). Conclusion: The results revealed gene expression patterns and modulation networks in the aged hippocampus during perioperative period, which provide insights into overall mechanisms and potential therapeutic targets for prevention and treatment of perioperative central nervous system diseases, such as PND, from the genetic level.

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Terrence Demos, <a href="https://orcid.org/0000-0003-0522-3708">https://orcid.org/0000-0003-0522-3708</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Erica Krimmel, <a href="https://orcid.org/0000-0003-3192-0080">https://orcid.org/0000-0003-3192-0080</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

opencc-by-4.0Jul 2022View details →

Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 MNDWI images of coasts.

Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 MNDWI images of coasts. Based on SWED*** data https://openmldata.ukho.gov.uk/ These Residual-UNet model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. MNDWI (1 band): mndwi MNDWI = (Green - SWIR) / (Green + SWIR)    Green = pixel values from the green band    SWIR = pixel values from the short-wave infrared band Reference: Xu, H. "Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery." International Journal of Remote Sensing 27, No. 14 (2006): 3025-3033." (ESRI, 2018) Classes are: {0: null, 1: water}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.   2. '.h5' weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function  `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images   3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym *** https://www.sciencedirect.com/science/article/abs/pii/S0034425722001584

opencc-zeroJul 2022View details →

Implementing blended learning for clinician teachers: Identifying their needs and its impact on faculty development initiatives

Learning Technologies has been a fast-growing field in Health Professions Education (HPE). Approaches to teaching, learning and assessment has been increasingly influenced by learning technologies which requires HPE teachers to adapt their teaching practices and with that identify areas for professional development. The implementation of blended learning in HPE, has shown improvements in student performance. However, it seems as if there are challenges with the implementation of a blended learning approach and that there might be some needs that clinical teachers have that are not being addressed in order to implement blended learning successfully. We used a qualitative exploratory design to identify clinician teachers' needs. Semi-structured, individual interviews were conducted with a total of eight (n=8) module coordinators in the third year of the MBChB programme, Stellenbosch University, Faculty of Medicine and Health Sciences. Results indicated the need for continuous technical and pedagogical support which refers to a longitudinal faculty development approach. Additionally, faculty development should include the support in structuring and rethinking the blended curriculum, as well as assisting in the clinicians' development in their role and identity as a clinical teacher. These results reveal the importance of faculty development as a targeted longitudinal approach

opencc-by-4.0Aug 2021View details →

NMR_raw_data for "Native Metabolomics Identifies the Rivulariapeptolide Family of Protease Inhibitors"

Raw 1D/2D NMR data and chemical structures for compounds 1 - 6 of the study "Native Metabolomics Identifies the Rivulariapeptolide Family of Protease Inhibitors".

opencc-by-4.0Aug 2022View details →

Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test [DATA]

Data and metadata for the publication "Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test", published in Earth and Space Science.