Find research datasets worth reusing

opencc-by-4.0Feb 2022View details →

ROV cm-scale seafloor surveys at 1840 m depth in Monterey Canyon

This dataset contains 5-cm lateral resolution multibeam sonar bathymetry and 1-cm lateral resolution lidar bathymetry from ~1840 m water depth in Monterey Canyon, offshore Moss Landing, California. The data were acquired by a remotely operated vehicle (ROV) aboard the R/V Western Flyer operated by the Monterey Bay Aquarium Research Institute (MBARI). The multibeam sonar and lidar data were collected using a Reson SeaBat 7125 400-kHz multibeam sonar and a 3D at Depth SL1 subsea lidar, respectively. These surveys were all conducted between November 2015 and March 2017 as one component of the Coordinated Canyon Experiment (CCE) led by MBARI geologist Charlie Paull, in collaboration with researchers from the United States Geological Survey, the Ocean University of China, the National Oceanography Centre in Southampton, England, and the University of Hull, England. For a detailed description of the methodology used to collect, process, and grid these data, please refer to the associated journal article: <This info will be updated once the paper is published> The primary results from the CCE were described in: Paull, C.K., Talling, P.J., Maier, K.L., Parsons, D., Xu, J., Caress, D.W., Gwiazda, R., Lundsten, E.M., Anderson, K., Barry, J.P., Chaffey, M., O'Reilly, T., Rosenberger, K.J., Gales, J.A., Kieft, B., McGann, M., Simmons, S.M., McCann, M., Sumner, E.J., Clare, M.A., Cartigny, J., (2018). Powerful turbidity currents driven by dense basal layers. Nature Communications, 9: 1-9.  <a href="https://doi.org/10.1038/s41467-018-06254-6">https://doi.org/10.1038/s41467-018-06254-6</a> A general description of the CCE can be found in an article from the 2017 MBARI annual report: <a href="https://annualreport.mbari.org/2017/story/coordinated-canyon-experiment">https://annualreport.mbari.org/2017/story/coordinated-canyon-experiment</a> This dataset contains the following gridded bathymetry data in both GeoTIFF and ESRI ASCII grid format (.asc) in WG84 Geographic (EPSG: 4326): 5-cm lateral resolution multibeam sonar bathymetry grids: <ol> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_November2015 <ul> <li>Multibeam bathymetry data collected between November 18 - 20, 2015</li> </ul> </li> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_May2016 <ul> <li>Multibeam bathymetry data collected between May 10 - 12, 2016</li> </ul> </li> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_October2016 <ul> <li>Multibeam bathymetry data collected between October 26 - 28, 2016</li> </ul> </li> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_March2017 <ul> <li>Multibeam bathymetry data collected between March 7 - 12th, 2016</li> </ul> </li> </ol> 1-cm lateral resolution lidar bathymetry grids: <ol> <li>CCE_LASS_1cm_lidar_MontereyCanyon_May2016 <ul> <li>a portion of the survey area mapped by lidar between May 10 - 12, 2016</li> </ul> </li> </ol>

Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda

Datasets underlying the publication "Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda": Figure 6: Number of children enrolled in the Patient Surveillance System (grey bars), and percentage of these children being administered rectal artesunate (RAS), by country. Figure 8: Overall case fatality ratio (CFR) in patients with danger signs and a positive malaria test at enrolment across the entire study period, by enrolment location and country. Data for Uganda excludes enrolments at PHCs (N=34).

MCPNet : A parallel maximum capacity-based genome-scale gene network construction framework

This deposit contains the gene expression profile datasets used for the paper titled "MCPNet : A parallel maximum capacity-based genome-scale gene network construction framework".  There are three sets of data: <ul> <li>Simulated yeast data from NetBenchmark, with random noise injected, as well as the ground truth network matrix.  In "SimulatedYeast.zip".</li> <li>Real Yeast dataset and the ground truth network as an adjacency list file.  in "yeast_data.exp" and "yeast_gs1_list_filtered.tsv".  Data acquired from "Castro DM, de Veaux NR, Miraldi ER, Bonneau R (2019) Multi-study inference of regulatory networks for more accurate models of gene regulation. PLoS Comput Biol 15(1): e1006591. https://doi.org/10.1371/journal.pcbi.1006591", <a href="https://github.com/simonsfoundation/multitask_inferelator/tree/AMuSR">https://github.com/simonsfoundation/multitask_inferelator/tree/AMuSR</a>.</li> <li>Real Arabidopsis athaliana datasets for 5 tissues and 1 environmental challenge.  <ul> <li>athaliana_gs_probes.tsv : ground truth as an adjacency list</li> <li>microarray gene expression profiles for "development", "leaf", "seed", "flower", "seedling1week", "hormone-aba-iaa-ga-br".</li> <li>Aathaliana.Datasets-CEL-File-URLs.xlsx: list of SRA accession numbers for the A. athaliana datasets</li> </ul> </li> </ul>

opencc-by-nc-4.0Jan 2022View details →

EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems

This is the dataset created for the paper, "EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems" (https://arxiv.org/abs/2109.04919). EmoWOZ is based on MultiWOZ, a multi-domain task-oriented dialogue dataset (https://github.com/budzianowski/multiwoz). It contains more than 11K task-oriented dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. There are 7 emotion labels, which are adapted from the OCC emotion models. For data format and label definition, please refer to README.md.

Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data Revision

Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.

opencc-zeroMay 2022View details →

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Data from: Looking for compensation at multiple scales in a wetland bird community

Compensatory dynamics, during which community composition shifts despite a near-constant total community size, are usually rare: synchronous dynamics prevail in natural communities. This is a puzzle for ecologists, because of the key role of compensation in explaining the relation between biodiversity and ecosystem functioning. However, most studies so far have considered compensation in either plants or planktonic organisms, so that evidence for the generality of such synchrony is limited. Here, we extend analyses of community-level synchrony to wetland birds. We analyse a 35-year monthly survey of a community where we suspected that compensation might occur due to potential competition and changes in water levels, favouring birds with different habitat preferences. We perform both year-to-year analyses by season, using a compensation/synchrony index, as well as multiscale analyses using a wavelet-based measure, which allows for both scale- and time-dependence. We analyse synchrony both within and between guilds, with guilds defined either as tightknit phylogenetic groups or larger functional groups. We find that abundance and biomass compensation are rare, likely due to the synchronizing influence of climate (and other drivers) on birds, even after considering several temporal scales of covariation (during either cold or warm seasons, above or below the annual scale). Negative covariation in abundance at the guild or community level did only appear at the scale of a few months or several years. We also found that synchrony varies with taxonomic and functional scale: the rare cases where compensation appeared consistently in year-to-year analyses were between rather than within functional groups. Our results suggest that abundance compensation may have more potential to emerge between broad functional groups rather than between species, as well as at relatively long temporal scales (multiple years for vertebrates), above that of the dominant synchronizing driver.

opencc-by-4.0Jan 2022View details →

Data supporting "Large-scale citizen science programs can support ecological and climate change assessments"

Text file of phenology observations pulled from the USA National Phenology Network's database (www.usanpn.org) and used in this analysis.

Spatial Scaling Challenge - Additional Data. COST Action CA17134 SENSECO. Working Group 1

This dataset contains the data additional data provided for the SPATIAL SCALING CHALLENGE organized in the framework of the SENSECO COST Action CA17143 “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (<a href="https://www.senseco.eu/">https://www.senseco.eu/</a>), by the Working Group 1. “Closing the scaling gap: from leaf measurements to satellite images” (<a href="https://www.senseco.eu/working-groups/wg1-scaling-gap/">https://www.senseco.eu/working-groups/wg1-scaling-gap/</a>).   The main dataset, documentation and scripts of the SPATIAL SCALING CHALLENGE must be downloaded from: <a href="https://doi.org/10.5281/zenodo.6451335">https://doi.org/10.5281/zenodo.6451335</a> This additional dataset includes half-hourly time series of down-welling incoming spectral irradiance (W m-2 µm-1) in the visible and near-infrared domains as measured by a field spectroradiometer operating in a nearby ecosystem station. The inclusion of these data in the Spatial Scaling Challenge is at the discretion of the participants.    The SPATIAL SCALING CHALLENGE aims at gathering the community’s expertise and knowledge to tackle the scaling problems posed by variables of different nature. These experiences will be summarized in a journal article where all the participants are invited to contribute. The exercise is internationally open. Ph.D. students, early career and senior researchers, spin-offs, and companies working in the field of remote sensing of vegetation ecophysiology are welcome to participate.   STILL OPEN FOR PARTICIPATION! New deadline 31st of October 2022.   Follow all the communications and updates of the SPATIAL SCALING CHALLENGE in the RG site: <a href="https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1">https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1</a>.

Lightning Assimilation in the Weather Research and Forecasting (WRF) Model: Technique Updates and Assessment of the Applications from Regional to Hemispheric Scales

Figure 1. The data is proprietary, but it can be purchased from Vaisala Inc. (https:// <a href="http://www.vaisala.com/en/products/systems/lightning-detection">www.vaisala.com/en/products/systems/lightning-detection</a>), and the WWLLN raw data are also available for purchase at <a href="http://wwlln.net">http://wwlln.net</a>. Figure 2. Maps, data is not applicable. Figure 3. Data file: NLDN_WWLLN_Prism_Rainfall_Analysis.xlsx Figure 4. Data file: NLDN_WWLLN_METVARS_T2_Jul_2016.xlsx Figure 5. Data file: CONUSall_METOBS_q_Jul_2016.xlsx Figure 6. Data file: CONUSall_METOBS_ws_Jul_2016.xlsx Figure 7. Created using the R script: Hemi_Rain_ModelOnlyWGPM.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds, AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds, and GPM_WRF_Paired_rain2Hemispheric_July2016.rds. Figure 8. Created using the R script: Hemi_Rain_Aanlysis.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds and AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds. Figure 9. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx Figure 10. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx Figure 11. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx Figure 12. Created using the R script: CreateCPCdataforUSdomain_vs_Prism.R based on the R oject files: Prism_CFC_WRF*.rds Figure 13. Data file: Hemi_lta_METOBS_T2_Jul_2016.xlsx Figure 14. Data file: Hemi_lta_METOBS_q_Jul_2016.xlsx

All-India district-scale climate projections for current (2006-2015), mid-century (2041-50), and end-century (2091-2100) periods

<ol> <li>This dataset contains monthly mean data for 10 meteorological variables for each district of India for current, mid-century, and end-century periods for all the districts of India. </li> <li>To generate this data, we first dynamically downscaled the CMIP5 CESM RCP8.5 projections over India for the Current (2006-2015), Mid-Century (2041-50), and End-Century (2091-2100) periods using the Weather Research and Forecasting (WRF) model to 10 km resolution. The 30 years of data are archived in the World Data Center for Climate (WDCC) at DKRZ (Barik et al. 2021 and 2022). Next, we processed the 10-km gridded downscaled data to calculate the monthly climatological mean by averaging over 10 years for each of the 3 periods. Finally, the monthly climatological means were processed in ArcGIS to develop the district scale datasets.</li> </ol>

The multilayer volume-of-fluid method for multiphase flows across scales: breaking waves, microfluidics, and membrane-less electrolyzers

Supplementary movies to PhD thesis <a href="https://doi.org/10.3929/ethz-b-000547518">10.3929/ethz-b-000547518</a>

opencc-zeroMay 2022View details →

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Remarkable effects of microbial factors on soil phosphorus bioavailability: a country-scale study

Low soil phosphorus (P) bioavailability causes the widespread occurrence of P-limited terrestrial ecosystems around the globe. Exploring the factors influencing soil P bioavailability at large spatial scales is critical for managing these ecosystems. However, previous studies have mostly focused on abiotic factors. In this study, we explored the effects of microbial factors on soil P bioavailability of terrestrial ecosystems using a countrycontinental-scale sampling effort. Our results showed that soil microbial biomass carbon (MBC) and acid phosphatase were important predictors of soil P bioavailability of agro- and natural ecosystems across China although they appeared less important than total soil P. The two microbial factors had a positive effect on soil P bioavailability of both ecosystem types and were able to mediate the effects of several abiotic factors (e.g., mean annual temperature). Meanwhile, we revealed that soil phytase could affect soil P bioavailability at the countrycontinental scale via ways similar to those of soil MBC and acid phosphatase, a pattern being more pronounced in agroecosystems than in natural ecosystems. Moreover, we obtained evidence for the positive effects of microbial genes encoding these enzymes on soil P bioavailability at the countrycontinental scale although their effect sizes varied between the two ecosystem types. Taken together, this study demonstrated the remarkable effects of microbial factors on soil P bioavailability at a large spatial scale, highlighting the importance to consider microbial factors in managing the widespread P-limited terrestrial ecosystems.

opencc-by-4.0Mar 2022View details →

Dataset of Jupyter Notebooks from the paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts"

<pre>This archive contains the dataset of properly-licensed Jupyter notebooks from the MSR'22 paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts". The dataset contains 847,881 notebooks stored in the PostgreSQL dump file. You can find the details about the database in the README file. To transform the notebooks into this convenient format and to calcuate the structural metrics, we used our library called Matroskin, which can be found here: <a href="https://github.com/JetBrains-Research/Matroskin">https://github.com/JetBrains-Research/Matroskin</a>. </pre>

opencc-by-4.0Jan 2022View details →

EvaNIL: silver standard dataset for large-scale NIL entity linking evaluation

The EvaNIL dataset can be used to train or evaluate approaches developed for NIL entity linking. It was built from several Biomedical and Life Sciences corpora: <ul> <li>PubMed DS</li> <li>CRAFT corpus</li> <li>MedMentions</li> </ul> These corpora contain entities associated with knowledge base concepts. To build the EvaNIL dataset, we assumed that those knowledge base concepts did not exist in the respective knowledge bases, so each entity is associated instead with the direct ancestors of those original concepts. The EvaNIL dataset is divided into 6 partitions including annotations from several knowledge bases: <ul> <li>"medic" (CTD-MEDIC)</li> <li>"ctd_anatomy" (CTD-Anatomy)</li> <li>"ctd_chemicals" (CTD-Chemicals)</li> <li>"chebi" (ChEBI)</li> <li>"go_bp" (GO-Biological Process)</li> <li>"hp" (HPO)</li> </ul>

Dataset for Rasch analyses of the brief Critical Thinking Scale (CTh) and assessment of change

Dataset for Rasch analyses of the brief Critical Thinking Scale (CTh) and assessment of change Data from the study reported in ” Exploring first semester changes in domain-specific critical thinking”. Data are from Danish Psychology students, and consists of three data sets containing the variables described below. Baseline data set (n = 336) Gender: 1 = female, 2 = male Agegroup (median split): 1 = 21 years and younger, 2 = 22 years and older Math (perception of own mathematical knowledge as adequate): 1 = inadequate, 2 = adequate Statfuture (expectation to need statistics in future employment): 1 = yes, 2 = maybe, 3 = no CTh1, CTh2, CTh5 are items of the CTh scale with response scale (item statements included in the article): 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = always RescaledWML (rescaled person parameter estimates at baseline) Matched longitunial data set dataset (n = 165) Gender, Agegroup, Math and Statfuture were collected at baseline Gender: 1 = female, 2 = male Agegroup (median split): 1 = 21 years and younger, 2 = 22 years and older Math (perception of own mathematical knowledge as adequate): 1 = inadequate, 2 = adequate Statfuture (expectation to need statistics in future employment): 1 = yes, 2 = maybe, 3 = no CTh1b, CTh2b, CTh5b are items of the CTh scale at baseline with response scale (item statements included in the article): 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = always CTh1f, CTh2f, CTh5f are items of the CTh scale at follow-up with response scale (item statements included in the article): 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = always RescaledWMLf (rescaled person parameter estimates at follow-up)

Data from: Chromosome-scale assembly with a phased sex-determining region resolves features of early Z and W chromosome differentiation in a wild octoploid strawberry

Abstract: When sex chromosomes stop recombining, they start to accumulate differences. The sex-limited chromosome (Y or W) especially is expected to degenerate via the loss of nucleotide sequence and the accumulation of repetitive sequences. However, how early signs of degeneration can be detected in a new sex chromosome is still unclear. The sex determining region (SDR) of the octoploid strawberries is young, small, and dynamic. Using PacBio HiFi reads, we obtained a chromosome scale assembly of a female (ZW) Fragaria chiloensis plant carrying the youngest and largest of the known SDR on the W in strawberries. We fully characterized the previously incomplete SDR, confirming its gene content, genomic location and evolutionary history. Resolution of gaps in the previous characterization of the SDR added 10 kbp of sequence including a non-canonical LTR-retrotransposon; whereas the Z sequence revealed a Harbinger transposable element adjoining the SDR insertion site. Limited genetic differentiation of the sex chromosomes coupled with structural variation may indicate an early stage of W degeneration. The sex chromosomes have a similar percentage of repeats but differ in their repeat distribution. Differences in the pattern of repeats (transposable element polymorphism) apparently precede sex chromosome differentiation, thus potentially contributing to recombination cessation as opposed to being a consequence of it. Repository content: data (sequence alignments, phylogenetic trees, genome assembly, and vcf files) and scripts associated with the manuscript "Chromosome-scale assembly with a phased sex-determining region resolves features of early Z and W chromosome differentiation in a wild octoploid strawberry"

opencc-zeroMay 2022View details →

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Why bears hibernate? Redefining the scaling energetics of hibernation

Hibernation is a natural state of suspended animation that many mammals experience and has been interpreted as an adaptive strategy for saving energy. However, the actual amount of savings that hibernation represents, and particularly its dependence on body mass (the "scaling") has not been calculated properly. Here we estimated the scaling of daily energy expenditure of hibernation (DEEH), covering a range of five orders of magnitude in mass. We found that DEEH scales isometrically with mass, which means that a gram of hibernating bat has a similar metabolism to that of a gram of bear, 20,000 times larger. Given that the metabolic rate of active animals scales allometrically, the point where these scaling curves intersect with DEEH represents the mass where energy savings by hibernation are zero. For BMR, these zero savings are attained for a relatively small bear (~100 kg). Calculated on a per-cell basis, the cellular metabolic power of hibernation was estimated to be 1.3x10-12 ± 2.6x10-13 W/cell, which is lower than the minimum metabolism of isolated mammalian cells. This supports the idea of the existence of a minimum metabolism that permits cells to survive under a combination of cold and hypoxia.

Microspines in tropical climbing plants: a small-scale fix for life in an obstacle course

This dataset is associated to the original research article "Microspines in tropical climbing plants: a small-scale fix for life in an obstacle course" published in the Journal of Experimental Botany.  <table> <tbody> <tr> <td>Variable</td> <td>Description</td> <td>Type of variable</td> <td>Units</td> </tr> <tr> <td>sample</td> <td>Sample identifiier</td> <td>Nominal Variable</td> <td> </td> </tr> <tr> <td>sp_code</td> <td>Species identifier</td> <td>Nominal Variable</td> <td> </td> </tr> <tr> <td>sp</td> <td>Species latin name</td> <td>Nominal Variable</td> <td> </td> </tr> <tr> <td>direction</td> <td>Friction test direction</td> <td>Categorical variable</td> <td> </td> </tr> <tr> <td>static_force</td> <td>Force needed to induce motion of the stem segment</td> <td>Continuous variable</td> <td>mN</td> </tr> <tr> <td>sliding_force</td> <td>Force needed to maintain the stem segment in motion</td> <td>Continuous variable</td> <td>mN</td> </tr> <tr> <td>sliding_dist</td> <td>Distance spanned by the stem segment during test</td> <td>Continuous variable</td> <td>mm</td> </tr> <tr> <td>nPeaks</td> <td>Number of force peaks detected during test</td> <td>Discrete variable</td> <td>mN</td> </tr> <tr> <td>peak_frequ</td> <td>Peak frequency during sliding (NPeaks/sliding_dist)</td> <td>Continuous variable</td> <td>peaks.mm-1</td> </tr> <tr> <td>diam</td> <td>Diameter of the tested segment</td> <td>Continuous variable</td> <td>mm</td> </tr> </tbody> </table>

Accompanying dataset for: A Multi-scale, Multiomic Atlas of Human Normal and Follicular Lymphoma Lymph Nodes

This dataset accompanies the manuscript titled “A Multi-scale, Multiomic Atlas of Human Normal and Follicular Lymphoma Lymph Nodes”, A. Radtke et al., bioRxiv, 2022. [<a href="https://doi.org/10.1101/2022.06.03.494716">doi: 10.1101/2022.06.03.494716</a>] The dataset contains the processed scRNA-seq information from human lymph nodes, both normal and from Follicular Lymphoma (FL) patients analyzed in this work as a Seurat object. The scRNA-seq information was saved in the rds format for viewing and analysis using the R programming language (to load it in R: scrna_seq_data <- readRDS("scRNA_seq_data_object.rds")). Additionally, the dataset contains comma-separated-value tables describing human lymph nodes, both normal and from Follicular Lymphoma (FL) patients. The files are formatted using the anatomical structures (AS), cell types (CT), and biomarkers (B), ASCT+B format defined by the Human BioMolecular Atlas Program (HuBMAP) for use with the <a href="http://hubmapconsortium.github.io/ccf-asct-reporter/">Reporter visualization tool</a>. Details on the structure of ASCT+B tables and the Reporter tool can be found in the <a href="https://doi.org/10.5281/zenodo.5944386">standard operating procedure</a> authored by the ASCT+B working group.  ASCT+B Table Details In support of a human reference atlas (Regev et al., 2017; Snyder et al., 2019), the Human BioMolecular Atlas Program (HuBMAP) is creating machine readable tables that catalog the anatomical structures (AS), cell types (CT), and biomarkers (B) found in human organs (Börner et al., 2021). ASCT+B tables facilitate data integration across multimodal assays and support comparisons between normal and diseased tissues. In addition, they are readily visualized with the <a href="http://hubmapconsortium.github.io/ccf-asct-reporter/">ASCT+B Reporter</a>, a web based tool. For these reasons, we created 10 ASCT+B tables from the datasets included in our study. To construct these tables, we used the <a href="https://doi.org/10.48539/HBM573.SHCQ.259">Lymph Node v1.1 ASCT+B table</a> as a starting point. The presence or absence of anatomical structures was determined by visual inspection of images and quantitative image analysis of cellular communities. Certain anatomical structures were absent from the excisional biopsies of FL patients e.g., capsule, medulla, hilum, etc. In contrast, the lack of primary follicles, mantle zones, polarized germinal centers (GC), and negligible interfollicular cortex and paracortex in FL LNs reflects changes arising from malignancy. Cell types were defined based on gene biomarkers from bulk and single cell RNA sequencing (RNA-seq) and protein biomarkers from the highly multiplexed imaging method, IBEX (Radtke et al., 2022; Radtke et al., 2020). Whenever possible, cell types captured across assays were defined by both gene and protein biomarkers. However, several cell types were only profiled by bulk RNA-seq, scRNA-seq, or IBEX imaging. In these instances, only assay-specific biomarkers are included in the ASCT+B tables. Whenever possible, we used agreed upon ontology terms to define cell types; however, our study identified several unique cell types not included in ontology databases such as DC-SIGN+ follicular dendritic cells (FDCs). Furthermore, the Reporter does not allow visualization of similar cell types (DC-SIGN- FDCs versus DC-SIGN+ FDCs) in the same anatomical structure if a shared Cell Ontology (CL) identifier is used (FDC: CL:0000442). In these instances, we removed the CL term to allow the Reporter to display the various subpopulations discovered in this study. Cell types were placed in their respective anatomical structures using domain knowledge, visual inspection of images, and quantitative image analysis. Reporter Usage Instructions <ul> <li>Visualizing an individual ASCT+B table: <ol> <li>Go to <a href="https://hubmapconsortium.github.io/ccf-asct-reporter/">Reporter</a></li> <li>Launch Playground</li> <li>Click on Upload tab</li> <li>Attach CSV final of ASCT+B table</li> <li>Use the toolbars on the left to adjust display. Typical parameters include: Tree Height (1400),Tree width (1000), Bimodal Distance X (500), and Bimodal Distance Y (50). </li> <li>Toggle between gene and protein biomarkers by clicking drop down menu under Biomarkers tab on left-side of screen.</li> </ol> </li> <li>Comparing non-FL and FL tables to the Lymph Node v1.1 ASCT+B table: <ol> <li>Go to <a href="https://hubmapconsortium.github.io/ccf-asct-reporter/">Reporter</a></li> <li>Select “go to visualization” to compare new tables to a master table for lymph node</li> <li>Click check box next to lymph node and select version of published master table v1.1</li> <li>Click submit</li> <li>Click compare button at top right tool bar</li> <li>Attach CSV file of non-FL and FL ASCT+B tables </li> <li>Pick colors </li> <li>Go to bottom of panel and click add</li> <li>Click compare</li> <li>Adjust settings for tree height, tree width, bimodal distance x, bimodal distance y, ontology ID on or off, biomarker type (gene or protein), etc.</li> </ol> </li> </ul> References <ul> <li>Börner, K., Teichmann, S.A., Quardokus, E.M., Gee, J.C., Browne, K., Osumi-Sutherland, D., Herr, B.W., Bueckle, A., Paul, H., Haniffa, M., et al. (2021). Anatomical structures, cell types and biomarkers of the Human Reference Atlas. Nature Cell Biology 23, 1117-1128.</li> <li>Radtke, A.J., Chu, C.J., Yaniv, Z., Yao, L., Marr, J., Beuschel, R.T., Ichise, H., Gola, A., Kabat, J., Lowekamp, B., et al. (2022). IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues. Nature Protocols.</li> <li>Radtke, A.J., Kandov, E., Lowekamp, B., Speranza, E., Chu, C.J., Gola, A., Thakur, N., Shih, R., Yao, L., Yaniv, Z.R., et al. (2020). IBEX: A versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues. Proc Natl Acad Sci U S A 117, 33455-33465.</li> <li>Regev, A., Teichmann, S.A., Lander, E.S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017). The Human Cell Atlas. Elife 6.</li> <li>Snyder, M.P., Lin, S., Posgai, A., Atkinson, M., Regev, A., Rood, J., Rozenblatt-Rosen, O., Gaffney, L., Hupalowska, A., Satija, R., et al. (2019). The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574, 187-192.</li> </ul>