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1. SES Grants, 2000-2015

   https://doi.org/10.7910/DVN/QCA35O  

   Kaiser, Kendra; Braswell, Anna; Fork, Megan; Fork, Megan (January 2022, Harvard Dataverse)

   Information about grants funded by NSF to support SES research from 2000-2015. The grants included in this dataset are a subset that we identified as having an SES research focus from a set of grants accessed using the Dimensions platform (https://dimensions.ai). CSV file with 35 columns and names in header row: "Grant Searched" lists the granting NSF program (text); "Grant Searched 2" lists a secondary granting NSF program, if applicable (text); "Grant ID" is the ID from the Dimensions platform (string); "Grant Number" is the NSF Award number (integer); "Number of Papers (NSF)" is the count of publications listed under "PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH" in the NSF Award Search page for the grant (integer); "Number of Pubs Tracked" is the count of publications from "Number of Papers (NSF)" included in our analysis (integer); "Publication notes" are our notes about the publication information. We used "subset" to denote when a grant was associated with >10 publications and we used a random sample of 10 publications in our analysis (text); "Unique ID" is our unique identifier for each grant in the dataset (integer); "Collaborative/Cross Program" denotes whether the grant was submitted as part of a set of collaborative or cross-program proposals. In this case, all linked proposals are given the same unique identifier and treated together in the analysis. (text); "Title" is the title of the grant (text); "Title translated" is the title of the grant translated to English, where applicable (text); "Abstract" is the abstract of the grant (text); "Abstract translated" is the abstract of the grant translated to English, where applicable (text); "Funding Amount" is the numeric value of funding awarded to the grant (integer); "Currency" is the currency associated with the field "Funding Amount" (text); "Funding Amount in USD" is the numeric value of funding awarded to the grant expressed in US Dollars (integer); "Start Date" is the start date of the grant (mm/dd/yyyy); "Start Year" is the year in which grant funding began (year); "End Date" is the end date of the grant (mm/dd/yyyy); "End Year" is the year in which the term of the grant expired (year); "Researchers" lists the Principal Investigators on the grant in First Name Last Name format, separated by semi-colons (text); "Research Organization - original" gives the affiliation of the lead PI as listed in the grant (text); "Research Organization - standardized" gives the affiliation of each PI in the list, separated by semi-colons (text); "GRID ID" is a list of the unique identifier for each the Research Organization in the Global Research Identifier Database [https://grid.ac/?_ga=2.26738100.847204331.1643218575-1999717347.1643218575], separated by semi-colons (string); "Country of Research organization" is a list of the countries in which each Research Organization is located, separated by semi-colons (text); "Funder" gives the NSF Directorate that funded the grant (text); "Source Linkout" is a link to the NSF Award Search page with information about the grant (URL); "Dimensions URL" is a link to information about the grant in Dimensions (URL); "FOR (ANZSRC) Categories" is a list of Field of Research categories [from the Australian and New Zealand Standard Research Classification (ANZSRC) system] associated with each grant, separated by semi-colons (string); "FOR [1-5]" give the FOR categories separated. "NOTES" provide any other notes added by the authors of this dataset during our processing of these data. 

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2. Publications associated with SES grants, 2000-2015

   https://doi.org/10.7910/DVN/OPSR5I  

   Kaiser, Kendra; Braswell, Anna; Fork, Megan; Fork, Megan (January 2022, Harvard Dataverse)

   Information about individual publications associated with grants funded by NSF to support SES research from 2000-2015 (see "SES grants, 2000-2015"). For grants with ten or fewer publications, we included information about all available publications in this dataset. For grants with more than ten publications, we randomly selected ten to include in this dataset. CSV file with 13 columns and names in header row: "Grant ID" is the ID from the Dimensions platform (string); "Grant Number" is the NSF Award number (integer); "Publication Title" is the title of the paper (text); "Publication Year" is the year in which the paper was published (year); "Authors" is a list or abbreviated list of the authors of the paper (text); "Journal" is the name of the scientific journal or outlet in which the paper is published (text); "Interdis Rubric 1" is a metric representing the dataset authors' assessment for the level of interdisciplinarity represented by the paper (integer: “1” indicated social and natural science interdisciplinarity where both social and environmental conditions are measured or explored and/or author affiliations included departments across these disciplines; “2” indicated general interdisciplinarity between two or more different fields (that may both be within natural or social science); and “3” indicated single-disciplinarity) "Citations" is the count of citations the paper had received as of the date listed in "date for cite count", as reported in Google Scholar (integer); "date for cite count" is the date on which citation count for the paper was obtained (ddBBByy); "Abstract" is the text of the abstract of the paper, where available (text); "Notes" are any notes added by the authors of the dataset (text). 

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3. Data from: Inheritance of DNA methylation differences in the mangrove Rhizophora mangle

   https://doi.org/10.5281/zenodo.5102718  

   Mounger, Jeannie; Boquete, M. Teresa; Schmid, Marc W.; Granado, Renan; Robertson, Marta H.; Voors, Sandy A.; Langanke, Kristen L.; Alvarez, Mariano; Wagemaker, Cornelis A.M.; Schrey, Aaron W.; et al (January 2021, Zenodo)

   {"Abstract":["This record contains supplementary information for the article "Inheritance of DNA methylation differences in the mangrove Rhizophora mangle" published in Evolution&Development. It contains the barcodes (barcodes.txt), the reference contigs (contigs.fasta.gz), the annotation of the reference contigs (mergedAnnot.csv.gz), the SNPs (snps.vcf.gz), the methylation data (methylation.txt.gz), and the experimental design (design.txt). All data are unfiltered. Short reads are available on SRA (PRJNA746695). Note that demultiplexing of the pooled reads (SRX11452376) will fail because the barcodes are already removed and the header information is lost during SRA submission. Instead, use the pre-demultiplexed reads that are as well linked to PRJNA746695.<\/p>\n\n\n <\/p>\n\nTable S13 (TableS13_DSSwithGeneAnnotation.offspringFams.csv.gz): <\/strong><\/p>\n\nDifferential cytosine methylation between families using the mother data set. The first three columns fragment number ("chr"), the position within the fragment ("pos"), and the sequence context ("context"). Columns with the pattern FDR_<X>_vs_<Y> contain false discovery rates of a test comparing population X with population Y. Average DNA methylation levels for each population are given in the columns "AC", "FD", "HI", "UTB", "WB", and "WI". The remaining columns contain the annotation of the fragment, for example whether it matches to a gene and if yes, the gene name ID and description are provided.<\/p>"]} 

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4. A biodiversity dataset graph: DataONE

   https://doi.org/10.5281/zenodo.1486279  

   Poelen, Jorrit H. (January 2018, Zenodo)

   {"Abstract":["The intended use of this archive is to facilitate meta-analysis of the Data Observation Network for Earth (DataONE, [1]). <\/p>\n\nDataONE is a distributed infrastructure that provides information about earth observation data. This dataset was derived from the DataONE network using Preston [2] between 17 October 2018 and 6 November 2018, resolving 335,213 urls at an average retrieval rate of about 5 seconds per url, or 720 files per hour, resulting in a data gzip compressed tar archive of 837.3 MB .  <\/p>\n\nThe archive associates 325,757 unique metadata urls [3] to 202,063 unique ecological metadata files [4]. Also, the DataONE search index was captured to establish provenance of how the dataset descriptors were found and acquired. During the creation of the snapshot (or crawl), 15,389 urls [5], or 4.7% of urls, did not successfully resolve. <\/p>\n\nTo facilitate discovery, the record of the Preston snapshot crawl is included in the preston-ls-* files . There files are derived from the rdf/nquad file with hash://sha256/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f . This file can also be found in the data.tar.gz at data/8c/67/e0/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f/data . For more information about concepts and format, please see [2]. <\/p>\n\nTo extract all EML files from the included Preston archive, first extract the hashes assocated with EML files using:<\/p>\n\ncat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\\t' '\\n' | grep "hash://" | sort | uniq > eml-hashes.txt<\/p>\n\nextract data.tar.gz using:<\/p>\n\n~/preston-archive$$ tar xzf data.tar.gz <\/p>\n\nthen use Preston to extract each hash using something like:<\/p>\n\n~/preston-archive$$ preston get hash://sha256/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa\n<eml:eml xmlns:eml="eml://ecoinformatics.org/eml-2.1.1" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:stmml="http://www.xml-cml.org/schema/stmml_1.1" packageId="doi:10.18739/A24P9Q" system="https://arcticdata.io" scope="system" xsi:schemaLocation="eml://ecoinformatics.org/eml-2.1.1 ~/development/eml/eml.xsd">\n  <dataset>\n    <alternateIdentifier>urn:x-wmo:md:org.aoncadis.www::d76bc3b5-7b19-11e4-8526-00c0f03d5b7c</alternateIdentifier>\n    <alternateIdentifier>d76bc3b5-7b19-11e4-8526-00c0f03d5b7c</alternateIdentifier>\n    <title>Airglow Image Data 2011 4 of 5</title>\n...<\/p>\n\nAlternatively, without using Preston, you can extract the data using the naming convention:<\/p>\n\ndata/[x]/[y]/[z]/[hash]/data<\/p>\n\nwhere x is the first 2 characters of the hash, y the second 2 characters, z the third 2 characters, and hash the full sha256 content hash of the EML file.<\/p>\n\nFor example, the hash hash://sha256/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa can be found in the file: data/00/00/2d/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa/data . For more information, see [2].<\/p>\n\nThe intended use of this archive is to facilitate meta-analysis of the DataONE dataset network. <\/p>\n\n[1] DataONE, https://www.dataone.org\n[2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 . DataONE was crawled via Preston with "preston update -u https://dataone.org".\n[3] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\\t' '\\n' | grep -v "hash://" | sort | uniq | wc -l\n[4] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\\t' '\\n' | grep "hash://" | sort | uniq | wc -l\n[5] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep  "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\\t' '\\n' | grep -v "hash://" | sort | uniq | wc -l<\/p>\n\nThis work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.<\/p>"]} 

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5. DeepCaImX: An end-to-end recurrent compressed sensing method to denoise, detect and demix calcium imaging data

   https://doi.org/10.5281/zenodo.12650420  

   Zhang, Kangning; Tang, Sean; Zhu, Vivian; Barchini, Majd; Yang, Weijian (September 2024, Zenodo)

   {"Abstract":["# DeepCaImX## Introduction#### Two-photon calcium imaging provides large-scale recordings of neuronal activities at cellular resolution. A robust, automated and high-speed pipeline to simultaneously segment the spatial footprints of neurons and extract their temporal activity traces while decontaminating them from background, noise and overlapping neurons is highly desirable to analyze calcium imaging data. In this paper, we demonstrate DeepCaImX, an end-to-end deep learning method based on an iterative shrinkage-thresholding algorithm and a long-short-term-memory neural network to achieve the above goals altogether at a very high speed and without any manually tuned hyper-parameters. DeepCaImX is a multi-task, multi-class and multi-label segmentation method composed of a compressed-sensing-inspired neural network with a recurrent layer and fully connected layers. It represents the first neural network that can simultaneously generate accurate neuronal footprints and extract clean neuronal activity traces from calcium imaging data. We trained the neural network with simulated datasets and benchmarked it against existing state-of-the-art methods with in vivo experimental data. DeepCaImX outperforms existing methods in the quality of segmentation and temporal trace extraction as well as processing speed. DeepCaImX is highly scalable and will benefit the analysis of mesoscale calcium imaging. \n\n## System and Environment Requirements#### 1. Both CPU and GPU are supported to run the code of DeepCaImX. A CUDA compatible GPU is preferred. * In our demo of full-version, we use a GPU of Quadro RTX8000 48GB to accelerate the training speed.* In our demo of mini-version, at least 6 GB momory of GPU/CPU is required.#### 2. Python 3.9 and Tensorflow 2.10.0#### 3. Virtual environment: Anaconda Navigator 2.2.0#### 4. Matlab 2023a\n\n## Demo and installation#### 1 (_Optional_) GPU environment setup. We need a Nvidia parallel computing platform and programming model called _CUDA Toolkit_ and a GPU-accelerated library of primitives for deep neural networks called _CUDA Deep Neural Network library (cuDNN)_ to build up a GPU supported environment for training and testing our model. The link of CUDA installation guide is https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html and the link of cuDNN installation guide is https://docs.nvidia.com/deeplearning/cudnn/installation/overview.html. #### 2 Install Anaconda. Link of installation guide: https://docs.anaconda.com/free/anaconda/install/index.html#### 3 Launch Anaconda prompt and install Python 3.x and Tensorflow 2.9.0 as the virtual environment.#### 4 Open the virtual environment, and then  pip install mat73, opencv-python, python-time and scipy.#### 5 Download the "DeepCaImX_training_demo.ipynb" in folder "Demo (full-version)" for a full version and the simulated dataset via the google drive link. Then, create and put the training dataset in the path "./Training Dataset/". If there is a limitation on your computing resource or a quick test on our code, we highly recommand download the demo from the folder "Mini-version", which only requires around 6.3 GB momory in training. #### 6 Run: Use Anaconda to launch the virtual environment and open "DeepCaImX_training_demo.ipynb" or "DeepCaImX_testing_demo.ipynb". Then, please check and follow the guide of "DeepCaImX_training_demo.ipynb" or or "DeepCaImX_testing_demo.ipynb" for training or testing respectively.#### Note: Every package can be installed in a few minutes.\n\n## Run DeepCaImX#### 1. Mini-version demo* Download all the documents in the folder of "Demo (mini-version)".* Adding training and testing dataset in the sub-folder of "Training Dataset" and "Testing Dataset" separately.* (Optional) Put pretrained model in the the sub-folder of "Pretrained Model"* Using Anaconda Navigator to launch the virtual environment and opening "DeepCaImX_training_demo.ipynb" for training or "DeepCaImX_testing_demo.ipynb" for predicting.\n\n#### 2. Full-version demo* Download all the documents in the folder of "Demo (full-version)".* Adding training and testing dataset in the sub-folder of "Training Dataset" and "Testing Dataset" separately.* (Optional) Put pretrained model in the the sub-folder of "Pretrained Model"* Using Anaconda Navigator to launch the virtual environment and opening "DeepCaImX_training_demo.ipynb" for training or "DeepCaImX_testing_demo.ipynb" for predicting.\n\n## Data Tailor#### A data tailor developed by Matlab is provided to support a basic data tiling processing. In the folder of "Data Tailor", we can find a "tailor.m" script and an example "test.tiff". After running "tailor.m" by matlab, user is able to choose a "tiff" file from a GUI as loading the sample to be tiled. Settings include size of FOV, overlapping area, normalization option, name of output file and output data format. The output files can be found at local folder, which is at the same folder as the "tailor.m".\n\n## Simulated Dataset#### 1. Dataset generator (FISSA Version): The algorithm for generating simulated dataset is based on the paper of FISSA (_Keemink, S.W., Lowe, S.C., Pakan, J.M.P. et al. FISSA: A neuropil decontamination toolbox for calcium imaging signals. Sci Rep 8, 3493 (2018)_) and SimCalc repository (https://github.com/rochefort-lab/SimCalc/). For the code used to generate the simulated data, please download the documents in the folder "Simulated Dataset Generator". #### Training dataset: https://drive.google.com/file/d/1WZkIE_WA7Qw133t2KtqTESDmxMwsEkjJ/view?usp=share_link#### Testing Dataset: https://drive.google.com/file/d/1zsLH8OQ4kTV7LaqQfbPDuMDuWBcHGWcO/view?usp=share_link\n\n#### 2. Dataset generator (NAOMi Version): The algorithm for generating simulated dataset is based on the paper of NAOMi (_Song, A., Gauthier, J. L., Pillow, J. W., Tank, D. W. & Charles, A. S. Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods. Journal of neuroscience methods 358, 109173 (2021)_). For the code use to generate the simulated data, please go to this link: https://bitbucket.org/adamshch/naomi_sim/src/master/code/## Experimental Dataset#### We used the samples from ABO dataset:https://github.com/AllenInstitute/AllenSDK/wiki/Use-the-Allen-Brain-Observatory-%E2%80%93-Visual-Coding-on-AWS.#### The segmentation ground truth can be found in the folder "Manually Labelled ROIs". #### The segmentation ground truth of depth 175, 275, 375, 550 and 625 um are manually labeled by us. #### The code for creating ground truth of extracted traces can be found in "Prepro_Exp_Sample.ipynb" in the folder "Preprocessing of Experimental Sample"."]} 

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