Find research datasets worth reusing

Data for article of validation of a two-stage process for polyhydroxyalkanoates production

The information contained in this data section corresponds to the article titled "Microrespirometric validation of a two-stage process for polyhydroxyalkanoates production from peanut oil and propionate with Cupriavidus necator," published in The Open Chemical Engineering Journal. Three Excel files are attached as data, providing information on the figures related to the growth kinetics and the results of the respirometric experiments.

Dataset for article: Adsorption of the hydrophobic organic pollutant hexachlorobenzene to phyllosilicate minerals

This repository contains data obtained from first principle DFT calculations at the PBE-D3 DFT level by the program VASP  for the research article  Title: "Adsorption of the hydrophobic organic pollutant hexachlorobenzene to phyllosilicate minerals" published in Environmental Science and Pollution Research (2023) 30:36824–36837. Authors: Leonard Böhm, Peter Grančič, Eva Scholtzová, Benjamin Justus Heyde, Rolf-Alexander Düring, Jan Siemens, Martin H. Gerzabek & Daniel Tunega. Please cite that article when using this dataset. The systems in the dataset are models of Me-montmorillonite layers (Me = Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba) interacting with hexachlorobenzene (HCB) molecule. Calculated are interaction energies of optimized geometries of HCB...Me-Mnt complexes. Dateset contains tables with collected calculated adsorption energies and main geometrical paramters. The structure of dataset is following: Rep_Ads_I directory contains directories for HCB molecule, and for complexes of HCB with Li-Mnt to Rb-Mnt. Each directory of complexes contains corresponding directory of isolated Me-Mnt layer. The second directory, Rep_ads_II has the same structure as Rep_Ads_I directory for Me=Mg, Ca, Sr, and Ba. In each directory are the main files for VASP calculations: input geometry data (POSCAR.norm file) optimized geometry  (CONTCAR.norm file) input parameters for VASP (INCAR file) k-points (KPOINT file) complete output files (OUTCAR.norm and vasprun.xml.norm files) POTCAR file with pseudopotentials are not provided due to copyright restrictions. Their type can be found in OUTCAR file or vasprun.xml file. Funding: This work has been supported by German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), grant number 443637168, BO5388/1–1 and Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung, FWF), grant number I 4876–N in the bilateral project ”Clay minerals as sorbents for hydrophobic organic chemicals – ClayHOC”.  The results presented have been achieved using the Vienna Scientific Cluster (VSC), project number 70544. Terms of use: These data are provided "as is", without any warranty. This dataset is provided under the Creative Commons Attribution 4.0 International license.

Simulation input scripts for figures in the article "Experimental parameters for plasma wakefield acceleration in a narrow plasma column"

Simulation input scripts for figures in the article "Experimental parameters for plasma wakefield acceleration in a narrow plasma column" Alter line "my_constants.dx_dz= 10e-6" to chage the misalignment degree

Simulation dataset and plotting scripts used for journal article "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024)

Simulation data underlying all figures presented in "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024) <a href="http://dx.doi.org/10.1080/02786826.2024.2323641" target="_blank" rel="noopener noreferrer">http://dx.doi.org/10.1080/02786826.2024.2323641</a>.  The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.

Tabular and image data of article "Morphing cholinesterase inhibitor amiridine into multipotent drugs for the treatment of Alzheimer's disease"

The search for novel drugs to address the medical needs of Alzheimer’s disease (AD) is an ongoing process relying on the discovery of disease-modifying agents. Given the complexity of the disease, such an aim can be pursued by developing so-called multi-target directed ligands (MTDLs) that will impact the disease pathophysiology more comprehensively. Herewith, we contemplated the therapeutic efficacy of an amiridine drug acting as a cholinesterase inhibitor by converting it into a novel class of novel MTDLs. Applying the linking approach, we have paired amiridine as a core building block with memantine/adamantylamine, trolox, and substituted benzothiazole moieties to generate novel MTDLs endowed with additional properties like N-methyl-D-aspartate (NMDA) receptor affinity, antioxidant capacity, and anti-amyloid properties, respectively. The top-ranked amiridine-based compound 5d was also inspected by in silico to reveal the butyrylcholinesterase binding differences with its close structural analogue 5b. Our study provides insight into the discovery of novel amiridinebased drugs by broadening their target-engaged profile from cholinesterase inhibitors towards MTDLs with potential implications in AD therapy. Table 1. hBChE inhibitory activities of 5c-d, 7c and 7 g and reference compounds (amiridine hydrochloride and THA); their cytotoxicity profile on SH-SY5Y cell line, and predictions of BBB penetration. Table 2. Relative inhibitions (RIs) of 5c-d and 7 m and reference compound memantine at recombinant human GluN1/GluN2B NMDA receptor expressed in HEK293 cells. Fig_1. Chemical structures of rivastigmine, galantamine, and tacrine as representatives of cholinesterase inhibitors. Approaches to novel drugs for AD treatment on the selected candidates are displayed. Fig_2. Examples of previously published amiridine-based derivatives and design strategy applied in the current study below, using various pharmacophores. Fig_3. Top scored docking pose of 5b (A) and 5d (B) highlighting the key findings responsible for compound activity/inactivity. For the sake of clarity, superimposed ligands are aligned in the Fig. C with respect to key amino acid residue W82 to demonstrate the binding difference. Compounds 5b and 5d are colored in salmon and yellow, respectively. Essential amino acid residues responsible for ligand anchoring are rendered in green. Important interactions of different origin are displayed with dashed black lines. The figure was created with The PyMOL Molecular Graphics System, v. 2.5.2. Scheme 1. Preparation of the amiridine-based compounds 5a-d. Reagents and conditions: a) 2-chloroacetyl chloride (4 eq.), CHCl3, 90 ◦C, overnight, 8, 54%, 11, 90%, 12, 52%; b) CH3CN, K2CO3, KI, reflux, 3 h, 5a, 54%, 5b, 54%; c) amiridine (1.1 eq), CH3CN, K2CO3, KI, reflux, overnight, 5c, 48%, 5d, 41%. Scheme 2. Preparation of intermediate 13 and final compound 6. Reagents and conditions: a) potassium phthalimide, CH3CN, reflux, 3 h, then an excess of NH2NH2⋅H2O, reflux, overnight, 57%; b) DMF, TEA, BOP, room temperature, 2 days, 86%. Scheme 3. Preparation of amiridine-benzothiazole derivatives 7a-m. Reagents and conditions: a) for 7a: 2-chlorobenzothiazole, 110 ◦C, overnight, 32%; for 7b-m: corresponding 2-chlorobenzothiazole, DIPEA, 100 ◦C, overnight, 21–77%.

Figure data for article "Controlling the dynamics of atomic correlations via the coupling to a dissipative cavity"

The files contain the data depicted in the figures of the article "Controlling the dynamics of atomic correlations via the coupling to a dissipative cavity", Phys. Rev. Lett. 134, 073604 (2025) The format of the data and to which figure it corresponds is described in the file "read_me_metadata.txt".

Raw data for Figures 2-4 for journal article: "Experimental Evaluation of the Adhear, a Novel Transcutaneous Bone Conduction Hearing Aid""

This is a data set containing the raw data for figures 2-4 from the journal article: "Experimental Evaluation of the Adhear, a Novel Transcutaneous Bone Conduction Hearing Aid" Original article DOI: 10.1055/a-1308-3888 Original article link: https://pubmed.ncbi.nlm.nih.gov/33260222/   The data is contained within MATLAB  figure (.fig) files, all saved with MATLAB version R2020a.

Data and ARRIVE 2.0 checklist for the original article "Lockbox enrichment facilitates manipulative and cognitive activities for mice"

This repository contains data (XLSX file) related to the original article "Lockbox enrichment facilitates manipulative and cognitive activities for mice", which was submitted for publication to Open Research Europe. Moreover, the ARRIVE checklist including the ARRIVE Essential 10 and the Recommended Set is provided in Version v2.

BioPropaPhenKG on Online Newspapers and Medical Articles

The coronavirus disease (COVID-19) spread rampantly around the world at the beginning of 2020 before the governments of each country could prevent it by making decisions based on medical data analysis. With proper formalization, the terabytes of new textual data available online every day could have been used for the early description and detection of cases of this virus. Since then, the number of Event-Based Surveillance (EBS) applications has increased exponentially. These applications aim to mine channels of unstructured information to detect signs of possible public health events' progression. However, one problem with such systems is the need for expert intervention to define which event will be captured, which relevant terms should be used in the search, and to analyze the events to modify the search procedure constantly. Another problem is that many of these applications do not consider both spatial and temporal characteristics. Addressing such limitations, this datasets presents a novel approach. We propose the use of BioPropaPhenKG to replace such systems. In this dataset, BioPropaPhen was enhanced with information comming from unstructured texts from online newspapers and medical articles. BioPropaPhenKG, its ontology and other useful information can be found in <a href="../records/10911980">https://zenodo.org/records/10911980</a>. The code used for this use case can be found in <a href="https://github.com/Gabriel382/DDPF-Health-Risks">https://github.com/Gabriel382/DDPF-Health-Risks</a> . Finally, the datasets used where UMLS MetamorphoSys, OpenStreetMaps, Wikidata, <a href="https://aylien.com/blog/free-coronavirus-news-dataset">Aylien</a> (data only from November of 2019) and <a href="https://allenai.org/data/cord-19">CORD-19</a> (data only from December of 2019).    To read, you just need to load it with Neo4j:4.4.3. Alternatively, you can open it with docker using the following command:  docker run --interactive --tty --rm \     --publish=7474:7474 --publish=7687:7687 \     --volume=/path-to-data-folder:/data --user="$(id -u):$(id -g)"\     neo4j:4.4.3 \ neo4j-admin load --from=/data/BioPropaPhenKG-Journal-Medical.dump --database "neo4j" --force

Supplementary data of article "Domestication has altered gene expression and secondary metabolites in pea seed coat".

Table S1. Excel- GO_terms_MF_selected_WGCNA_modules. Table S2. Excel- GO_terms_MF_DEGs_UP_and_DOWN. Table S3. Excel- GO_terms_MF_DEGs_summary. Table S4. Excel- List of DEGs involved in flavonoid pathway found in WILD gene set. Table S5. Protein recoveries calculated for individual pea protein samples. Numbers 1, 2, 3 denote treatment groups corresponding to seed developmental stages (D1, D2 and mature seeds, respectively). Letters a–d denote biological replicates within the treatment groups. Table S6. Excel- Annotation of proteins differentially expressed in wild and domesticated pea seed coat samples. Table S7. Primary metabolites identified by spectral similarity library search and/or co-elution with authentic standards in pea seed coats aqua methanolic extracts. Metabolite analysis relied on GC-EI-Q-MS analysis after derivatization of the lyophilized extracts with methoxamine hydrochloride (MOA) and N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA). Table S8. Primary metabolites detected in the aq. methanolic extracts of mature Cameor seed coats demonstrating statistically significant up- and down-regulation in comparison to those of wild JI261. Table S9. Primary metabolites of mature JI92 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of wild JI261. Table S10. Primary metabolites of mature JI1794 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261. Table S11. Primary metabolites of mature JI64 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261. Table S12. Mass analyzer settings applied for QqTOF-MS experiments in analysis of seed coat (cell wall) hydrolyzates and reference authentic standards. Table S13. Cell wall-bound metabolites extracted from the seed coats of wild (JI64, JI1794, JI261) and domesticated (Cameor, JI92) peas upon alkali hydrolysis of corresponding isolated and purified cell wall material. Table S14. Excel- Coordinates of markers in S-plot obtained from OPLS-DA analysis (FIA-ESI-HRTMS, negative ionization, lock mass uncorrected). Table S15. List of identified significantly differential metabolites rising during seed coat development. Table S16. List of identified significantly differential metabolites decreasing during seed coat development (positive ionization mode). Table S17. List of identified metabolites with significantly higher content in wild compared cultivated genotypes in older developmental stages (D5-6). Table S18. Excel- Expression of genes encoding enzymes of monolignol pathway in seed coats (SC) and embryos (E) of domesticated (Cameor, JI92 and Pisum abyssinicum PI358617) and wild (JI64, JI1794, JI261) peas over five seed developmental stages (13, 17, 20, 23, 28 DAP, labelled as 1-5). PAL: phenylalanine ammonia-lyase, C4H: cinnamate-4-hydroxylase, 4CL: 4-coumaroyl: CoA ligase, HCT: hydroxycinnamoyl CoA:shikimate hydroxycinnamoyltransferase, COMT: caffeic acid O-methyltransferase, CSE: caffeoyl shikimate esterase, CAD: cinnamyl alcohol dehydrogenase, CCR: cinnamoyl CoA reductase, CCoAMT: caffeoyl CoA-3-methyltransferase, F5H: ferulate-5-hydroxylase Table S19. Studied metabolites of phenylpropanoid pathway. Table S20. Instrument settings used in the proteomics LIT-Orbitrap-MS and -MS/MS experiments. Table S21. Procedures and specific settings for data processing and post-processing of the proteomics data. Table S22. Gas chromatographic (GC) separation conditions and electron ionization-quadrupole-mass spectrometry (EI-Q-MS) settings for GC-EI-Q-MS analysis of the primary metabolites in pea seed coats. Table S23. Chromatographic conditions used for UHPLC separation of seed coat (cell wall) hydrolyzates and reference authentic standards. Table S24. Variable parameters of MS/cIMS/MS measurements. Figure S1. The dynamics of gene expression between studied developmental stages within all genotypes (a) or among genotypes in particular developmental stages (b). Figure S2. Twelve representative groups of transcription factors described within 20 gene modules of pea SC. Visualized by Cytoscape 3.9.0. Figure S3. SDS-PAGE electropherograms of the total protein fractions isolated from the seed coats of JI92 (a, c, e) and JI64 (b, d, f) seeds before and after tryptic hydrolysis. Numbers 1, 2, 3 denote seed developmental stages: DS1, DS2 and mature seeds, respectively. Letters a-d denote biological replicates. The aliquots (10 μg) of samples before hydrolysis (a, b), the incompletely digested aliquots left on filter unit after peptide elution (c, d) and aliquots of tryptic hydrolysates (corresponding to 5 μg of protein), (e, f) were loaded on gels. Inter-gel normalization relied on the total density of the Protein Ladder (PageRuler™ Prestained Protein Ladder #26616, 10–180 kDa) lane (St); the ND (non-digested) sample represents a reference protein not subjected to hydrolysis. Figure S4. The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups) (c) identified in domesticated JI92 seed coats at developmental stages D1, D2 and D6. The tryptic digests (n =&thinsp;3) obtained from seed coats were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode. Figure S5. The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups, c) identified in wild pea JI64 seed coats at D1, D2 and D6 stages. The tryptic digests (n =&thinsp;3), obtained from pea seedlings, were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode. Figure S6. Principal component analysis (PCA) with score plot representation (a) accomplished for seed coat proteins differentially expressed at developmental stages D1 and D2 and in the mature state (D6) and hierarchical clustering with a heatmap representation (b). Figure S7. Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D1. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of domesticated JI92 and wild JI64, respectively. Figure S8. Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D2. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of the domesticated JI92 and wild JI64, respectively. Figure S9. Prediction of sub-cellular localization of the proteins more expressed in the developing seeds of JI92 and JI64 with the BUSCA prediction tool. Figure S10. Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated Cameor by principal component analysis (PCA). Figure S11. Representation of the differences in the metabolic profiles of the mature seed coats obtained from the wild JI261 and domesticated Cameor by the t-test with Volcano plot representation (a) and the top 30 differentially abundant metabolites demonstrating the most pronounced differences of corresponding GC-MS signals associated with seed dormancy (b). Figure S12. Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated JI92 by principal component analysis (PCA) with score plot representation (a) and hierarchical clustering with heatmap representation (b). Figure S13. Principal component analysis (PCA) illustrating distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261. Figure S14. Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild pea genotypes, JI64 and JI261(control). Figure S15. Evaluation of the differences in the patterns of the cell wall-bound metabolites obtained from mature seed coats of wild JI261 and domesticated Cameor: principal component analysis (PCA) with score plot representation (a), hierarchical clustering with heatmap representation (b) and t-test analysis with the Volcano-plot representation (c). Figure S16. Statistical analysis (t-test with Volcano plot representation) characterizing the differences between the levels of mature seed coat cell wall-bound metabolites of Cameor compared with those of wild JI261. Figure S17. Principal component analysis (PCA) illustrates the distribution of mature seed coat metabolic profiles of domesticated JI92 and wild JI261. Figure S18. Principal component analysis (PCA) shows the distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261, control. Figure S19. Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild genotypes, JI64 and control JI261. Figure S20. Annotated cell wall-bound metabolites extracted from the seed coats of the dormant wild pea genotype JI261 and the seed coats from several pea genotypes varying in their dormancy (Cameor, JI92, JI64, and JI1794) upon alkali hydrolysis of corresponding isolated and purified cell wall material.  Figure S21. Ion mobility separation of m/z 299.0841. Figure S22. Ion mobility separation of m/z 701.1907.  Figure S23. Ion mobility separation of m/z 619.1041. Figure S24. Ion mobility separation of m/z 631.1017. Figure S25. Ion mobility separation of m/z 641.1139. Figure S26. Ion mobility separation of m/z 771.1346.  Figure S27. Reconstructed chromatograms of p-hydroxybenzoic and salicylic acids in DS5 of dormant JI64 and domesticated landraces JI92 (LC/HRTMS, negative ionization mode). Figure S28. Module-trait relationship depiction showing the correlation between expression of the gene modules and the abundance of identified metabolites of the monolignol pathway.

opencc-by-4.0Jan 2022View details →

Metabolomics Data Dictionary (Metabolon Inc. ) - from PMC articles (OA)

A collection of metabolite and chemical names reported by Metabolon Inc. in the supplementary tables of open-access full-text articles from the PMC database. Many entries can be duplicate because of chemical name variants reported in different articles.

Dataset related to article "Performance of dual-energy subtraction in contrast-enhanced mammography for three different manufacturers: a phantom study"

The dataset provided here contains the raw data used in the study discussed in this article. The primary objective of the study was to conduct a comparative analysis of the performance of dual energy subtraction (DES) images acquired using contrast-enhanced mammography (CEM) systems produced by three different manufacturers. The comparison, facilitated by a CEM-specific phantom, focused on the assessment of radiation dose and image quality. Composed of three separate CSV files, the dataset is structured as follows: 1) "Dose-related data": This file provides exposure parameters (including A/F combination, tube voltage and exposure) and the corresponding mean glandular dose (MGD for low-energy (LE) and high-energy (HE) images. Data are given for each CEM system and automatic exposure mode (AEC). 2) "CNR-related data": Encapsulated in this file are data extracted from the phantom DES images. These include measurements of the mean pixel value (MPV) for each iodinated contrast detail, as well as the MPV and standard deviation (SD) for the surrounding background. These data were used to calculate the contrast-to-noise ratio (CNR) for the iodinated contrast details. 3) “Residual CNR data”: This file includes MPV and SD data extracted from phantom DES images, which were useful for calculating the residual CNR after cancellation of the normal background tissue by the DES algorithm.

Dataset for article "W18O49 Nanowhiskers Decorating SiO2 Nanofibers: Lessons from In Situ SEM/TEM Growth to Large Scale Synthesis and Fundamental Structural Understanding" in Crystal Growth & Design, Des. 2024, 24, 1, 378–390

Supported dataset for publication "W18O49 Nanowhiskers Decorating SiO2 Nanofibers: Lessons from In Situ SEM/TEM Growth to Large Scale Synthesis and Fundamental Structural Understanding" in Crystal Growth & Design Des. 2024, 24, 1, 378–390, https://doi.org/10.1021/acs.cgd.3c01094. The directory Data contains the figures (png, jpg), and vido (AVI) source data files for the manuscript.

Dataset related to the article titled "Prediction of elastic modulus of basaltic rocks using machine learning methods."

Dataset related to the article titled "Prediction of elastic modulus of basaltic rocks using machine learning methods."

Physical and technical characterization of main agricultural plastics articles used for protected cultivation systems

The folder "Thermal and spectroscopic data on the characterization of mulch films.rar" includes differential scanning calorimetry (DSC), thermogravimetry (TG), Fourier-transform infrared spectroscopy (FTIR), and water contact angle (WCA) raw data and images relevant to the characterization of conventional and biodegradable mulch films. 7 mulching films consisting of 3 different conventional linear low-density polyethylene (LLDPE, coded as PE) films and 4 different biodegradable (PBAT-based, coded as BIO) films are analysed.In particular, data regarding M-BIOIT-15-black-0, M-PEIT-15-black-0, M-BIOEL-15-black-0, M-PEEL-15-black-0, M-PEEL-50-transparent-0, M-BIODE-30-black-0 and M-BIOFI-15-black-0 are included.

opencc-by-4.0Nov 2024View details →

Dataset for the article "When possessor and argument indexes coincide. A typological survey" (Alessandra Barotto, Andrea Sansò)

The dataset stored here contains the data underlying the article “When Possessor and Argument Indexes Coincide” (Alessandra Barotto, Andrea Sansò). The dataset comes with a README file providing an explanation of the decisions made.

opencc-by-4.0Nov 2024View details →

nanoCT data for the samples published in the article doi 10.1002/jemt.24746

nanoCT data for the samples published in the article "Dealing with Missing Angular Sections in NanoCT Reconstructions of Low Contrast Polymeric Samples Employing a Mechanical In Situ Loading Stage" -  Rafaela Debastiani, Chantal Miriam Kurpiers, Enrico Domenico Lemma, Ben Breitung, Martin Bastmeyer, Ruth Schwaiger, Peter Gumbsch <a href="https://doi.org/10.1002/jemt.24746">https://doi.org/10.1002/jemt.24746</a> Samples: Sample A_180°_phase Sample A_140°_pos0°_phase Sample A_140°_pos0°_absorption Sample A_140°_pos45°_phase Sample A_140°_pos45°_absorption Sample A_140°_pos90°_phase Sample A_140°_pos90°_absorption Sample B_140°_pos0°_phase Sample B_140°_pos90°_absorption Sample B_140°_pos90°_phase Sample B_coated_140°_absorption Sample B_coated_140°_phase   Phase = Zernike phase contrast Absorption = absorption contrast

opencc-by-4.0Oct 2024View details →

Dataset for "Exploring the Potential of Various Cyclodextrin-based Derivatives in Enzyme Supramolecular Engineering" research article

The dataset for the paper titled "Exploring the Potential of Various Cyclodextrin-based Derivatives in Enzyme Supramolecular Engineering". The dataset includes the following items: 1. "alpha-CD-TES_Characterization" xls file (1 file) including 5 datasheets; These data sheets provide raw characterization data regarding the synthesis of the alpha-CD-TES molecule including 1H NMR, 13C NMR, FTIR, ESI-MS, and MALDI. 2. "beta-CD-TES_Characterization" xls file (1 file) including 5 datasheets; These data sheets provide raw characterization data regarding the synthesis of the beta-CD-TES molecule including 1H NMR, 13C NMR, FTIR, ESI-MS, and MALDI. 3. "gamma-CD-TES_Characterization" xls file (1 file) including 5 datasheets; These data sheets provide raw characterization data regarding the synthesis of the gamma-CD-TES molecule including 1H NMR, 13C NMR, FTIR, ESI-MS, and MALDI. 4. "Activities" xls file (1 file) including 6 datasheets; These datasheets contain the raw data of the enzymatic activities measured for both LipMRD9 and A50 enzymes for each set of stability experiments reported in the manuscript or the supporting information documents. 5. "Layer growth - A50" zip file including 5 files: The unprocessed SEM micrographs of A50 enzyme shielding with alpha/beta/gamma-CD-TES building blocks (after 75 min reaction) and the corresponding size measurements in an xls file. 6. "Layer growth - LipMRD9" zip file including 15 files: The unprocessed SEM micrographs of the shielded LipMRD9 enzyme with α-, β-, and γ-CD-TES building blocks (after 30, 60, 90, and 120 min reaction) and the corresponding size measurements (using ImageJ software) saved in separate xls files.

opencc-by-4.0Nov 2024View details →

Dataset for an article "Developing best practices for conducting STEM engineering projects in higher education"

These articles are part of the dataset used in the article.  These articles are anonymized manuscript drafts that were not published in the journal with other articles. Read more from LUMAT-B: <a href="https://urn.fi/urn:nbn:fi:hulib:editori:lumatb.v9i17"> https://urn.fi/urn:nbn:fi:hulib:editori:lumatb.v9i17 </a>