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33,754 results for “Enhancers”
Images and plotting code : Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite
<p>These are images to demonstrate the effectiveness of the algorithm and plotting code : Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite. Readme.pdf will provide specific instructions about these folder compression packages.</p>
Gene Enhancer Predictions in C2C12 (Mouse Myoblast)
<p>This file contains the complete enhancer prediction output of the analysis done in this study published on Nature Communications:</p> <p><a href="https://www.nature.com/articles/s41467-025-57758-x" target="_blank" rel="noopener">https://www.nature.com/articles/s41467-025-57758-x</a></p> <p>using the Activity by Contact Model following the pipeline described here: <a href="https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction">https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction</a></p> <p>To be used in this analysis, a genomewide Hi-C interaction matrix was generated with <a>Juicer </a> v1.6, expression counts for each gene were generated with STAR v2.7.10b and <a>Rsubread</a> v2.8.2, and the sequence alignment maps of ATAC-seq and H3K27ac ChIP-seq were generated with NextGenMap v0.5.5, all using raw sequencing reads downloaded from <a>SRA</a>: Hi-C (SRR16220088), RNA-seq (SRR074113 and SRR074114), <a>ATAC-seq (SRR2999996) and H3K27ac ChIP-seq (SRR358589, SRR358590, and SRR358591).</a></p> <p><a>The chromosome coordinates are of the GRCm38 - mm10 assembly, and the gene IDs are from ENSEMBL annotation.</a></p> <p>Please use the Version 3, and for more information please refer to our publication that used these enhancer predictions.</p>
Enhancing the health and wellbeing benefits of biodiversity citizen science
Open the record for dataset details and reuse information.
MD simulations files for: Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning
<div>Colvar files and related python scripts of Ala2 and Ala10 simulations.</div>
Data supporting the publication "Optimized Sandwich and Topological Structures for Enhanced Haptic Transparency"
<p>This dataset includes experimental modal responses of sandwich and topological structures, which are designed to enhance haptic transparency. </p>
A machine learning model reveals expansive downregulation of ligand-receptor interactions enhancing lymphocyte infiltration in melanoma with developed resistance to Immune Checkpoint Blockade
<p>Data repository containing the data to reproduce the results and findings that are published in:</p> <p>Sahni, S., Wang, B., Wu, D. <em>et al.</em> A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade. <em>Nat Commun</em> <strong>15</strong>, 8867 (2024). https://doi.org/10.1038/s41467-024-52555-4</p>
Dataset related to article "Tumor Treating Fields enhance chemotherapy efficacy by increasing cellular drug uptake and retention in mesothelioma cells"
<p>This record contains data related to article “Tumor Treating Fields enhance chemotherapy efficacy by increasing cellular drug uptake and retention in mesothelioma cells”</p> <p><span>Tumor Treating Fields (TTFields) applied with standard chemotherapy have been approved for the first-line treatment of unresectable pleural mesothelioma (PM), an aggressive malignancy with limited effective therapy options.</span></p> <p><span>In this study, we demonstrated that the simultaneous exposure to TTFields and doxorubicin or vinorelbine enhanced treatment efficacy in patient-derived PM cells by increasing intracellular drug concentrations. This was achieved by modulating several genes that encode transport proteins, such as the downregulation of P-glycoprotein.</span></p> <p><span>Using specific, sensitive and quantitative analytical techniques, we observed a more than 70% increase in intracellular concentrations of doxorubicin and vinorelbine in samples treated with TTFields, and a greater than 50% increase in drug uptake in cells exposed to TTFields and pemetrexed. This result indicates that the increased drug concentration observed in TTFields treated cells is significant not only for drugs that are P-gp substrates but also suggests that TTFields could potentially affect other efflux pumps. However, the co-exposure to the drug and TTFields was critical to increasing intracellular drug levels, highlighting the necessity of concurrent use with drugs to enhance the antiproliferative effects of treatment.</span></p> <p><span>The <em>in vitro</em> findings were further corroborated by <em>in vivo</em> pharmacokinetic experiments in mice subcutaneously injected with epithelioid PM tumors. Indeed, a 30% increase in intratumor concentrations was observed when vinorelbine was administered with TTFields.</span></p> <p><span>Our findings suggest that TTFields could be a well-tolerated approach for enhancing intratumoral drug levels and potentially achieving a more significant therapeutic impact on PM treatment.</span></p>
Enhancement of green ghosts due to recurrence of sprite element
<p><span>These files contain the dataset supporting the manuscript titled "Enhancement of green ghosts due to recurrence of sprite element". The dataset includes the original images and videos of events.</span></p>
Dataset for article: Potent and reversible open-channel blocker of NMDA receptor derived from dizocilpine with enhanced membrane-to-channel inhibition
<div>This version shows the correct number of n for Figure 6.</div> <div> </div>
Enhancing Collaborative Care in Schizophrenia: A Comparative Analysis of ICF Core Sets Assessments by Occupational Therapy and Mental Health Social Work Students
<p>This repository contains the R code, dataset, and README file for the network analysis of ICF (International Classification of Functioning, Disability, and Health) assessments. The study compares the assessments conducted by occupational therapy students and mental health social work students, analyzing centrality measures and Bridge Expected Influence within their respective networks.</p>
Analysis of single-cell CRISPR perturbations indicates that enhancers predominantly act multiplicatively
<p>This repository contains simulated data generated with GLiMMIRS-sim that was analyzed and presented in Zhou & Guyuvayurappan et al, as well as data pertaining to the <em>NMU</em> RT-qPCR experiment described in the manuscript. </p> <p> </p> <ul> <li><span>sim_base_data.tar.gz: directory containing simulated data for baseline model </span></li> <li> <p><span>sim_data_interactions_pos.tar.gz: directory containing simulated data for interactions model with positive interaction effects </span></p> </li> <li> <p><span>sim_data_interactions_neg.tar.gz: directory containing simulated data for interactions model with negative interaction effects </span></p> </li> <li><span>NMU.xlsx: spreadsheet containing data from the <em>NMU</em> RT-qPCR experiment </span></li> </ul>
Data for 'Evaluating the role of enhanced weathering in marine carbon dioxide removal under high emission pathway'
<p><span>1. ALK.mat </span></p> <p><a name="OLE_LINK86"></a><a name="OLE_LINK87"></a><span>Description: This file includes three structs, ALK_CTL, ALK_OWE, ALK_NUT, representing alkalinity data under control run, OWE simulation, NUT simulation. Each struct includes 5 parameters, lat: latitude, lon: longitude, mean_sur: variation of average alkalinity for the upper 100 m, mean_total: variation of average alkalinity for the whole water column, para_a10: 10-years average alkalinity for the upper 100 m. </span></p> <p><span>Units: meq/m<sup>3</sup></span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 1a, b and Fig.2 </span></p> <p><span> </span></p> <p><span>2. pH.mat</span></p> <p><span>Description: This file includes three structs, pH_CTL, pH_OWE, pH_NUT, representing surface pH data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pH, para_a10: 10-years average of surface pH. </span></p> <p><span>Units: unitless</span></p> <p><a name="OLE_LINK92"></a><a name="OLE_LINK93"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 1c and Fig.3 </span></p> <p><span> </span></p> <p><span>3. total_DIC_ALK.mat</span></p> <p><a name="OLE_LINK88"></a><a name="OLE_LINK89"></a><span>Description: This file includes six structs, CTLALKt, CTLDICt, OWEALKt, OWEDICt, NUTALKt, NUTDICt, representing integrated alkalinity and DIC data in control run, OWE simulation, NUT simulation. Each struct includes two parameters, para_a: decadal average of DIC inventory, para_int: the sum of global DIC inventory.</span></p> <p><span>Units: mmol (DIC)/ meq (ALK) (we plot the figure with the unit Tmol in Fig. 4 and Pmol in Fig. 1d)</span></p> <p><span>Data in this file is used in Fig. 1d and Fig. 4.</span></p> <p><span> </span></p> <p><span>4. remapped_DIC_CTL.nc, remapped_DIC_OWE.nc and DIC_remapped_NUT.nc</span></p> <p><span>Description: The three .nc files include remapped standard-grid (360*180) DIC inventory under control run, OWE simulation and NUT simulation. Each .nc file has 4 variables, lon: longitude, lat: latitude, z_t: depth, DIC: remapped DIC. </span></p> <p><span>Unit: mmol/m<sup>3</sup></span></p> <p><span>Coordinates: DIC: longitude * latitude* depth* time</span></p> <p><span>Data in this file is used in Fig. 5</span></p> <p><span> </span></p> <p><span>5. pCO2.mat</span></p> <p><span>Description: This file includes three structs, pCO2_CTL, pCO2_OWE, pCO2_NUT, representing surface pCO2 data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pCO2, para_a10: 10-years average of surface pCO2.</span></p> <p><span>Unit: ppmv</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig.6</span></p> <p><span> </span></p> <p><span>6. FG_CO2.mat</span></p> <p><a name="OLE_LINK94"></a><a name="OLE_LINK95"></a><span>Description: This file includes three structs, </span><a name="OLE_LINK90"></a><a name="OLE_LINK91"></a><span>FG_</span><span>CO2_CTL, FG_CO2_OWE, FG_CO2_NUT, representing DIC surface gas flux data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of FG_CO2, para_a10: 10-years average of FG_CO2.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><a name="OLE_LINK96"></a><a name="OLE_LINK97"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 7</span></p> <p><span> </span></p> <p><span>7. NPP.mat</span></p> <p><a name="OLE_LINK98"></a><a name="OLE_LINK99"></a><span>Description: This file includes three structs, NPP_CTL, NPP_OWE, NPP_NUT, representing total C fixation vertical integral data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of NPP, para_a10: 10-years average of NPP.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><a name="OLE_LINK104"></a><a name="OLE_LINK105"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 8</span></p> <p><span> </span></p> <p><span>8. spC.mat, diatC.mat, diazC.mat</span></p> <p><span>Description: The three files include phytoplankton biomass of small phytoplankton (spC_CTL, spC_OWE, spC_NUT), diatom (<a name="OLE_LINK100"></a><a name="OLE_LINK101"></a>diatC_CTL, diatC_OWE, diatC_NUT), and diazotroph (diazC_CTL, diazC_OWE, diazC_NUT). All phytoplankton biomass is measured in the unit of carbon. We store data on each phytoplankton species in the form of a struct. Each struct has five parameters, lat: latitude, lon: longitude, mean_sur: variation of <a name="OLE_LINK102"></a><a name="OLE_LINK103"></a>average of upper 100 m phytoplankton biomass; mean_total: variation of average phytoplankton biomass in the upper ocean, para_a10: 10-years average of average phytoplankton biomass in the upper ocean. </span></p> <p><span>Unit: mmol/m<sup>3</sup></span></p> <p><a name="OLE_LINK108"></a><a name="OLE_LINK109"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><a name="OLE_LINK110"></a><a name="OLE_LINK111"></a><span>Data in these files are used in Fig. 9</span></p> <p><span> </span></p> <p><span>9. <a name="OLE_LINK106"></a><a name="OLE_LINK107"></a>calcToSed.mat</span></p> <p><span>Description: This file includes three structs, calcToSed_CTL, calcToSed_OWE, calcToSed_NUT, representing CaCO3 flux to sediments flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of CaCO3 flux, para_a10: 10-years average of CaCO3 flux. </span></p> <p><span>Unit: nmol/cm<sup>2</sup>/s (need to convert unit to mol/m<sup>2</sup>/yr)</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><a name="OLE_LINK112"></a><a name="OLE_LINK113"></a><span>Data in these files are used in Fig. 10</span></p> <p><span> </span></p> <p><span>10. POC.mat</span></p> <p><span>Description: This file includes three structs, POC_CTL, POC_OWE, POC_NUT, representing 100 m POC flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of flux, para_a10: 10-years average of flux.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in these files are used in Fig. 11</span></p> <p><span> </span></p> <p><span>11. atmoCO2.mat</span></p> <p><span>Description: This file includes three structs, CTLATM, OWEATM, NUTATM. In each struct, atm_co2_trend represent the atmospheric CO<sub>2</sub> variation with the unit ppm. </span></p> <p><span>Data in this file is used in Fig.1f</span></p> <p><span> </span></p> <p><span> </span></p> <p><span> </span></p>
Dataset for "Detection and quantification of ergothioneine in human serum using surface enhanced Raman scattering (SERS)"
Open the record for dataset details and reuse information.
MyoD1 localization at the nuclear periphery is mediated by association of WFS1 with active enhancers - Image Dataset
<p>The set of raw images used in this study published on Nature Communications:</p> <p><a href="https://www.nature.com/articles/s41467-025-57758-x" target="_blank" rel="noopener">https://www.nature.com/articles/s41467-025-57758-x</a></p> <p>are available as 4 subsets below:</p> <p><a href="https://osf.io/w5n43/" target="_blank" rel="noopener">https://osf.io/w5n43/</a></p> <p><a href="https://osf.io/bfxdw/" target="_blank" rel="noopener">https://osf.io/bfxdw/</a></p> <div><a href="https://osf.io/rkguq/" target="_blank" rel="noopener">https://osf.io/rkguq/</a></div> <div> </div> <div><a href="https://osf.io/q6b4j/" target="_blank" rel="noopener">https://osf.io/q6b4j/</a> <div> </div> <div>The reason of this Zenodo entry is to combine these subsets into a single DOI link.</div> <div>The total size adds up to 200GB and it is not possible to host such a large dataset on Zenodo, and it is only possible as maximum of 50GB pieces on Open Science Framework.</div> <div> </div> <div>Summary of the study:</div> <div>Spatial organization of the mammalian genome influences gene expression and cell identity. While association of genes with the nuclear periphery is commonly linked to transcriptional repression, also active, expressed genes can localize at the nuclear periphery. The transcriptionally active MyoD1 gene, a master regulator of myogenesis, exhibits peripheral localization in proliferating myoblasts, yet the underlying mechanisms remain elusive. Using a newly generated reporter cell line, we demonstrate here that peripheral association of the MyoD1 locus is independent of mechanisms involved in heterochromatin anchoring. We identify a set of nuclear envelope transmembrane proteins, particularly WFS1, that actively tether MyoD1 to the nuclear periphery. WFS1 primarily associates with active distal enhancer elements upstream of MyoD1, and with a subset of enhancers enriched in active histone marks genome-wide, which are linked to expressed myogenic genes. Overall, our data identify a novel mechanism involved in tethering active genes to the nuclear periphery.</div> <div> </div> <div>This research was funded in whole or in part by the Austrian Science Fund (FWF) [P29713-B28, P32512-B and P36503-B] to Roland Foisner and a doctorate program funded by the Austrian Science Fund (FWF) [W1261-B28].</div> <div> </div> </div>
Supproting Materials for "Cross-Linked Selenoctanoic Acid Nanoplatform Enables Bidirectional Regulation of Intra- and Extracellular ROS: A New Avenue for Enhanced Cancer Immunotherapy" - section A
<p><strong>Table of Content</strong></p> <h1>1. Synthesis and characterization</h1> <h2> 1.1. SeLA</h2> <h3> 1.1.0. Lab book pages</h3> <h3> 1.1.1. NMR</h3> <h3> 1.1.2. HR-MS</h3> <h3> 1.1.3. UV</h3> <h3> 1.1.4. IR</h3> <h2> 1.2. cSeLAN</h2> <h3> 1.2.0. Lab book pages</h3> <h3> 1.2.1. NMR</h3> <h3> 1.2.2. UV</h3> <h3> 1.2.3. IR</h3> <h3> 1.2.4. GPC</h3> <h3> 1.2.5. MALDI-TOF-MS</h3> <h3> 1.2.6. DLS</h3> <h3> 1.2.7. Critical melle cncentration</h3> <h3> 1.2.8. ICP-MS</h3> <h3> 1.2.9. TEM</h3> <h2> 1.3. cLAN</h2> <h3> 1.3.0. Lab book pages</h3> <h3> 1.3.1. NMR</h3> <h3> 1.3.2. UV</h3> <h3> 1.3.3. DLS</h3> <h3> 1.3.4. GPC</h3> <h1>2. <em>In vitro</em> intra- and extracellular ROS regulation</h1> <h2> 2.0. Lab book pages</h2> <h2> 2.1. Raman scattering spectra</h2> <h2> 2.2. HPLC</h2> <h3> 2.2.1. Standard peaking</h3> <h3> 2.2.2. HPLC analysis</h3> <h2> 2.3. ICP-MS</h2> <h2> 2.4. MTT</h2> <h2> 2.5. Intracellular ROS generation</h2> <h3> 2.5.1. Fluorescence images</h3> <h3> 2.5.2. Flow fluorescence quantification</h3> <h2> 2.6. Intracellular pro-oxidation mechanism</h2> <h3> 2.6.1. Quantification of intracellular ATP</h3> <h3> 2.6.2. Quantification of intracellular NADH</h3> <h3> 2.6.3. Quantification of intracellular <strong>·</strong>OH</h3> <h3> 2.6.4. Quantification of intracellular ROS</h3> <h2> 2.7. Quantification of extracellular ROS</h2> <h1>3. <em>In vitro</em> ICD-inducing ability</h1> <h2> 3.0. Lab book pages</h2> <h2> 3.1. ATP release</h2> <h2> 3.2. HMGB1</h2> <h2> 3.3. CRT</h2> <h1>4. T cell proliferation and activation</h1> <h2> 4.0. Lab book pages</h2> <h2> 4.1. Flow cytometry analysis of the T cell proliferation</h2> <h2> 4.2. Quantification of IFN-γ secretions</h2> <h1>5. Biosafety evaluation of cSeLAN</h1> <h2> 5.0. Lab book pages</h2> <h2> 5.1. Hemolytic and hemagglutination assay</h2> <h3> 5.1.1. Hemolytic assay</h3> <h3> 5.1.2. Hemagglutination assay</h3> <h2> 5.2. Acute toxicity test</h2> <h3> 5.2.1. The amounts of dead mice</h3> <h3> 5.2.2. Body weight of mice</h3> <h3> 5.2.3. Hematological assay</h3> <h3> 5.2.4. Blood biochemistry</h3> <h3> 5.2.5. H&E staining of main organs</h3> <h1>6. Pharmacokinetic evaluation of cSeLAN</h1> <h2> 6.0. Lab book pages</h2> <h2> 6.1 Pharmacokinetic evaluation of cSeLAN</h2> <h1>7. <em>In vivo</em> anti-tumor immune response</h1> <h2> 7.0. Lab book pages</h2> <h2> 7.1. Chemiluminescence images</h2> <h3> 7.1.1. Chemiluminescence images of isoluminol</h3> <h3> 7.1.2. Chemiluminescence images of luminol</h3> <h3> 7.1.3. Related quantifications upon various treatments</h3> <h2> 7.2. Immunofluorescence images</h2> <h2> 7.3. T cell proliferation</h2> <h2> 7.4. DC proliferation</h2> <h1>8. <em>In vivo</em> anti-tumor efficacy</h1> <h2> 8.0. Lab book pages</h2> <h2> 8.1. Tumor growth</h2> <h3> 8.1.1. Tumor volume</h3> <h3> 8.1.2. Tumor weight</h3> <h3> 8.1.3. Tumor images</h3> <h2> 8.2. Body weight</h2> <h2> 8.3. Lung tissues with metastatic nodules</h2> <h3> 8.3.1. Lung tissues images</h3> <h3> 8.3.2. Number of pulmonary metastatic nodules</h3> <h3> 8.3.3. H&E</h3> <h2> 8.4. H&E of tumor and main organs</h2> <h2> 8.5. Cytokine secretions</h2> <h2> 8.6. Kaplan−Meier survival curves</h2> <h1>9. <em>In vitro</em> NK cytotoxicity and activity</h1> <h2> 9.0. Lab book pages</h2> <h2> 9.1. NK cytotoxicity</h2> <h2> 9.2. NK activity</h2>
Reviving diversity: cryoprotectants and culturing methods enhance recovery of mammalian gut microbes from field samples
<p>Welcome!</p> <p>Here you will find the codes used in the analysis we ran for our manuscript titled "Reviving diversity: cryoprotectants and culturing methods enhance recovery of mammalian gut microbes from field samples". We would be happy to help in way we can, so please do not hesitate to reach out if you have questions or suggestions.</p> <p>In summary, we developed this study in response to challenges encountered in our own field research. Recent advancements in culturomics and microbiology are enabling us to further explore the realm of microbiome science. We aspire for this study to serve as a foundational step toward identifying preservation methods that can help protect the microbial communities of wild animals.</p> <p>Our study aims are:</p> <ol> <li>The primary objective of this study was to evaluate the effectiveness of various preservation solutions in maintaining microbial integrity of gut samples during collection and for a short duration, simulating transportation conditions, prior to long-term storage at -80°C. We cultivated the samples in three distinct culture media to maximize the recovery of microbial diversity.</li> <li>The second objective was to compare the inventory of the preserved and cultured microbial community to that of the original uncultured samples, aiming to determine whether the preservation solutions retained unique taxa absent in the frozen original samples.</li> </ol> <p>Notes to keep in mind:</p> <ol> <li>We chose preservation solutions that have already been described for microbial culturing.</li> <li>We chose culture media that have already been described for microbial culturing, especially gut microbiome samples.</li> </ol> <p><em>As you go through the Rmd document included here, please make sure all documents have been properly downloaded. Additionally, please make sure to change the names of the alpha diversity metric files in the Rmd to that of the files here as they were modified for proper uploading methods here. </em></p>
Enhancing Women's Experiences in Software Engineering
<p><strong>Context: </strong>Women face many challenges in their lives, including harassment, lack of acceptance, and excessive workloads. These challenges affect their daily experiences and influence major life decisions, such as pursuing a career in IT. These issues further diminish the already low participation of women in the industry, making the field less appealing to potential newcomers. The challenges often begin before women even enroll in bachelor's programs, setting a difficult path for those aspiring to enter the software development industry.<br><strong>Goal: </strong>To investigate challenges women face in IT at different stages of their lives—specifically, during their last year of high school, while they are at University pursuing their bachelor's degree, and as practitioners. Furthermore, we aim to explore solutions to address these identified challenges.<br><strong>Research Method: </strong>To achieve this goal, we conducted a literature review using the snowballing technique to identify challenges and solutions already reported in the literature. The literature review results were used as input for workshops, in which we sought to understand the perspectives of high school women, undergraduates, and practitioners regarding the same set of challenges and solutions identified in the literature.<br><strong>Results:</strong> Our studies revealed that, regardless of the life stage, women feel discouraged in a toxic environment often characterized by a lack of inclusion, harassment, and the exhausting need to prove themselves. We also discovered that some challenges are specific to certain life stages; for example, issues related to maternity were mentioned only by practitioners.<br><strong>Conclusions: </strong>Women encounter gender-related challenges even before deciding to enter the software development field when the proportion of men and women is still similar. While issues such as feeling the need to prove themselves are mentioned at all three stages, high school women's challenges are more often directed toward convincing their parents that they are mature enough to handle their own responsibilities. As women move to the other stages, the challenge shifts to proving their competence in managing responsibilities for which they have received training. Increasing the inclusion of women in the field should, therefore, start earlier, and profound societal changes may be necessary to boost women's participation in the field. </p>
Enhanced Latin Lemma Dataset
<h2><strong>Overview</strong></h2> <p>The Latin Lexicon Dataset contains information about Latin words collected through webscraping from Wiktionary. The dataset includes various linguistic features such as part of speech, lemma, aspect, tense, verb form, voice, mood, number, person, case, and gender. Additionally, it provides source URLs and links to the Wiktionary pages for further reference. The dataset aims to contribute to linguistic research and analysis of Latin language elements.</p> <h3><strong>Versions of the Dataset</strong></h3> <p>This dataset is available in three versions, each offering varying levels of refinement:</p> <ul> <li><strong>wiki_latin_data_v1.csv(v1):</strong> The initial raw version, containing all webscraped data without extensive cleaning or filtering.</li> <li><strong>wiki_latin_data_v2.csv(v2):</strong> A more processed version, where some inconsistencies and duplicates were removed, and linguistic features were better aligned.</li> <li><strong><strong>wiki_latin_data_v3.csv (v3)</strong>:</strong> The most refined version, offering a clean, well-organized dataset with comprehensive linguistic features and translation equivalents with minimal errors. This version is recommended for most use cases.</li> </ul> <h3><strong>Data Source:</strong></h3> <ul> <li>Webscraped from Wiktionary</li> </ul> <h3><strong>Produced by:</strong></h3> <ul> <li>Python-based web scraping algorithms</li> </ul>
Spleen STARmap PLUS dataset of Transient hepatic reconstitution of thymic factors enhances aged immunity
<p>Here is the STARmap PLUS dataset of Spleen section included in "<strong>Transient hepatic reconstitution of thymic factors enhances aged immunity</strong>" from Friedrich et al. </p>
Acidic Microenvironment Enhances Cisplatin Resistance in Bladder Cancer via Bcl-2 and XIAP
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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.