XRD Raw data collected. This dataset is associated with the following publication: Nadagouda , M., C. Han , D. Dionysiou, and L. Wang. An innovative zinc oxide-coated zeolite adsorbent for removal of humic acid. JOURNAL OF HAZARDOUS MATERIALS. Elsevier Science Ltd, New York, NY, USA, 313: 283-290, (2016).

Raw data required to generate the example data associated with the NIfTI-MRS data standard.

The data standard can be found on Zenodo (https://doi.org/10.5281/zenodo.5084788).

The generated example data is also on Zenodo (https://doi.org/10.5281/zenodo.5085448).

Example data generation code is available on Github (https://github.com/wexeee/mrs_nifti_standard/tree/master/example_data)

All tracks of crickets in graphic form and raw data (x, y coordinates and other parameters). Examples of source video recordings.

This imaging mass cytometry (IMC) dataset serves as an example to demonstrate raw data processing and downstream analysis tools. The data was generated as part of the Integrated iMMUnoprofiling of large adaptive CANcer patient cohorts (IMMUcan) project (immucan.eu) using the Hyperion imaging system (www.fluidigm.com/products-services/instruments/hyperion). To get an overview on the technology and available analysis strategies, please visit bodenmillergroup.github.io/IMCWorkflow. The individual data files are described below:

• Patient1.zip, Patient2.zip, Patient3.zip, Patient4.zip: raw data files of 4 patient samples. Each .zip archive contains a folder in which one .mcd file (IMC raw data) and multiple .txt files (one per acquisition) can be found.
• compensation.zip: This .zip archive holds a folder which contains one .mcd file and multiple .txt files. Multiple spots of a "spillover slide" were acquired and each .txt file is named based on the spotted metal. This data is used for channel spillover correction. For more information, please refer to the original publication: Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry
• panel.csv: This file contains metadata for each antibody/channel used in the experiment. The full column indicates which channel should be analysed. The ilastik column specifies which channels were used for ilastik pixel classification and the deepcell column indicates the channels used for deepcell segmentation.
• sample_metadata.csv: This file links each patient to their cancer type (SCCHN - head and neck cancer; BCC - breast cancer; NSCLC - lung cancer; CRC - colorectal cancer).

{# General information# The script runs with R (Version 3.1.1; 2014-07-10) and packages plyr (Version 1.8.1), XLConnect (Version 0.2-9), utilsMPIO (Version 0.0.25), sp (Version 1.0-15), rgdal (Version 0.8-16), tools (Version 3.1.1) and lattice (Version 0.20-29)# --------------------------------------------------------------------------------------------------------# Questions can be directed to: Martin Bulla (bulla.mar@gmail.com)# -------------------------------------------------------------------------------------------------------- # Data collection and how the individual variables were derived is described in: #Steiger, S.S., et al., When the sun never sets: diverse activity rhythms under continuous daylight in free-living arctic-breeding birds. Proceedings of the Royal Society B: Biological Sciences, 2013. 280(1764): p. 20131016-20131016. # Dale, J., et al., The effects of life history and sexual selection on male and female plumage colouration. Nature, 2015. # Data are available as Rdata file # Missing values are NA. # --------------------------------------------------------------------------------------------------------# For better readability the subsections of the script can be collapsed # --------------------------------------------------------------------------------------------------------}{# Description of the method # 1 - data are visualized in an interactive actogram with time of day on x-axis and one panel for each day of data # 2 - red rectangle indicates the active field, clicking with the mouse in that field on the depicted light signal generates a data point that is automatically (via custom made function) saved in the csv file. For this data extraction I recommend, to click always on the bottom line of the red rectangle, as there is always data available due to a dummy variable ("lin") that creates continuous data at the bottom of the active panel. The data are captured only if greenish vertical bar appears and if new line of data appears in R console). # 3 - to extract incubation bouts, first click in the new plot has to be start of incubation, then next click depict end of incubation and the click on the same stop start of the incubation for the other sex. If the end and start of incubation are at different times, the data will be still extracted, but the sex, logger and bird_ID will be wrong. These need to be changed manually in the csv file. Similarly, the first bout for a given plot will be always assigned to male (if no data are present in the csv file) or based on previous data. Hence, whenever a data from a new plot are extracted, at a first mouse click it is worth checking whether the sex, logger and bird_ID information is correct and if not adjust it manually. # 4 - if all information from one day (panel) is extracted, right-click on the plot and choose "stop". This will activate the following day (panel) for extraction. # 5 - If you wish to end extraction before going through all the rectangles, just press "escape". }{# Annotations of data-files from turnstone_2009_Barrow_nest-t401_transmitter.RData dfr-- contains raw data on signal strength from radio tag attached to the rump of female and male, and information about when the birds where captured and incubation stage of the nest1. who: identifies whether the recording refers to female, male, capture or start of hatching2. datetime_: date and time of each recording3. logger: unique identity of the radio tag 4. signal_: signal strength of the radio tag5. sex: sex of the bird (f = female, m = male)6. nest: unique identity of the nest7. day: datetime_ variable truncated to year-month-day format8. time: time of day in hours9. datetime_utc: date and time of each recording, but in UTC time10. cols: colors assigned to "who"--------------------------------------------------------------------------------------------------------m-- contains metadata for a given nest1. sp: identifies species (RUTU = Ruddy turnstone)2. nest: unique identity of the nest3. year_: year of observation4. IDfemale: unique identity of the female5. IDmale: unique identity of the male6. lat: latitude coordinate of the nest7. lon: longitude coordinate of the nest8. hatch_start: date and time when the hatching of the eggs started 9. scinam: scientific name of the species10. breeding_site: unique identity of the breeding site (barr = Barrow, Alaska)11. logger: type of device used to record incubation (IT - radio tag)12. sampling: mean incubation sampling interval in seconds--------------------------------------------------------------------------------------------------------s-- contains metadata for the incubating parents1. year_: year of capture2. species: identifies species (RUTU = Ruddy turnstone)3. author: identifies the author who measured the bird4. nest: unique identity of the nest5. caught_date_time: date and time when the bird was captured6. recapture: was the bird capture before? (0 - no, 1 - yes)7. sex: sex of the bird (f = female, m = male)8. bird_ID: unique identity of the bird9. logger: unique identity of the radio tag --------------------------------------------------------------------------------------------------------}

The dataset contains data collected as part of the Ancient Adhesives project under the European Union’s Horizon 2020 research and innovation programme Grant Agreement No. 678 804151 (Grant holder G.H.J.L.).

It is being made public to act as supplementary data for a publication and for other researchers to use this data in their own work.

The data in this dataset were collected at TUDelft, University of Cantabria, and Museum of Prehistory and Archaeology of Cantabria in 2023.

This dataset contains:

1. Raw data for SEM-EDX of 10 archaeological objects named: MOR2; MOR4; MOR9; MOR12; MOR13; MOR17; MOR22; MOR35; MOR42; MOR50. The file format is .pdf
2. Raw data for micro-Raman of 5 archaeological objects named MOR2; MOR4; MOR9; MOR13; MOR22. The file format is .csv
3. Raw data for ATR-FTIR of 7 archaeological objects and 1 sediment sample named: MOR11; MOR38; MOR40a; MOR43; MOR44; MOR46; MOR47; MORSediment. The file format is .csv
4. Raw data for GC-MS of 23 archaeological objects (including sub-samples) and 2 sediment samples named: 230404 MOR 1.1.sirslt; 230404 MOR 5.1.sirslt; 230404 MOR 14.1.sirslt; 230404 MOR 24.1.sirslt; 230404 MOR 26.1.sirslt; 230404 MOR 28.1.sirslt; 230404 MOR 32.1.sirslt; 230404 MOR 43.1.sirslt; 230404 MOR 49.1.sirslt; 230404 MOR 57.1.sirslt; 230404 MOR 58.1.sirslt; 230421 MOR 5a.s.sirslt; 230421 MOR 11.1.sirslt; 230421 MOR 40a.3.sirslt; 230421 MOR 45.1.sirslt; 230421 MOR 47.2.sirslt; 230421 MOR 61.1.sirslt; 230421 MOR62.2.sirslt; 20220718 MOR25_1.sirslt; 20220718 MOR25_2.sirslt; 20220718 MOR38_1.sirslt; 20220718 MOR38_2.sirslt; 20220718 MOR40a_1.sirslt; 20220718 MOR40a_2.sirslt; 20220718 MOR47_1.sirslt; 20230316 MOR_41.1.sirslt; 20230316 MOR_44.1.sirslt; 20230316 MOR_46.1.sirslt; 20230316 MOR_60.1.sirslt; 20231024_MOR_SED 10.sirslt. Each.sirslt file contains the files necessary to open and manipulate the data using the original software Agilent OpenLab 2.5. Within each file there is a .DX file (for opening with Agilent OpenLab 2.5) and accompanying .ACAML, .DX, .MFX, .BIN, .RX, .PMX, and .AMX files. In addition, a .xlsx file (Morin GC-MS results.xlsx) is provided. Each sheet contains the complete GC-MS data exported for samples analyzed as well as the MS data and automated molecular data against the National Institute of Standards and Technology (NIST) library.

The acronym MOR stands for Morín Cave, a cave in Cantabria (Spain) where the objects were found.

The data included in this dataset has been organized per method. For each specimen, more than one point was measured as indicated in the file name. Only the measurements with interpretable results are made available.

The file name includes the unique ID of the object + the analytical technique + the number of the scan. For example: MOR11_ATR_loc1

Overview The University of Notre Dame (ND) scanning lidar dataset used for the WFIP2 Campaign is provided. The raw dataset contains the radial velocity and backscatter measurements along with the beam location and other lidar parameters in the header. Data Details 1) A Halo photonics scanning lidar, owned by ND, was deployed and operated from 12/17/2015 to 02/09/2016. On 02/09/2016, this lidar was replaced by a Halo photonics scanning lidar owned by the Army Research Lab (ARL). 2) For information on the scanning patterns, refer to attached "ReadMe" file. 3) Data Period from 12/15/2015 to 02/09/2016: One data file per day (24 hours). File name of each daily data file has {boardman} as {optionalfields}. For example: lidar.z07.00.20150414.143000.boardman.csm. 4) Data Period after 02/09/2016: One scan file every 15 minutes, one stare file, and one background file every hour. File names have the following {optionalfields}: {background_boardman} for background files; {scan_boardman} for scan files; and {stare_boardman} for stare files. For example: - lidar.z07.00.20150414.143000.background_boardman - lidar.z07.00.20150414.143000.scan_boardman - lidar.z07.00.20150414.143000.stare_boardman 5) Site information: - Site: Boardman, OR - Latitude: 45.816185° N - Longitude: 119.811766° W - Elevation (meters): 112.0 Data Quality Raw data: no quality control (QC) is applied. Uncertainty The lidar measurements' uncertainty varies with the range of the measurements. Please refer to Pearson et al. (2009) for more details. Constraints 1) Because of the change of lidars, the data were downloaded in different formats. Hence, the raw data (unfiltered) primarily are in two formats: .csm and .hpl. 2) The data were downloaded every one hour or 15 minutes. Hence, the datasets are not concatenated for continuous scans. 3) A lidar offset of +195 deg (to True North) was added to the azimuthal angles from the ND scanning lidars, spanning 12/17/2015 until 02/09/2016. Later, this was corrected for the data from 02/09/2016 as the lidar aligned to True North.

Raw data from HDMSe and SWATH MS analyses of 309 prostate cancer serum samples. Prostate cancer cohort:
309 patients were divided into control (n=112), prostate cancer (PCa) (n=175), and benign prostate hyperplasia (BPH) (n=22). PCa patients were then subdivided into active surveillance (AS) (n=51) or treatment group. Treatments were radiotherapy (pre: n=26, post: n=14), hormone therapy (pre: n=7, post: n=8), prostatectomy (pre: n=21, post: n=8), and radiotherapy (pre: n=23, post: n=17)

Data presents drone survey raw data (from different flight altitudes) which has been gathered during the TalTech Study Building reconstruction (Mäepealse 3, 12618, Tallinn, Estonia) during November 2019 - November 2020.

This table contains the data on identity, pathology, and dental wear used in the study. All dental wear measurements and the individuals they are associated with are included. (XLSX)

The dataset contains data collected as part of the Ancient Adhesives project under the European Union’s Horizon 2020 research and innovation programme Grant Agreement No. 678 804151 (Grant holder G.H.J.L.).

It is being made public to act as supplementary data for a publication and for other researchers to use this data in their own work.

The data in this dataset were collected at TUDelft in 2022 and 2023.

This dataset contains:

-Raw data of XRD of 11 archaeological objects named: SBF4; SBF5; SBF9; SBF10; SBF14; SBF15; SBF17; SBF20; SBF21; SBF23; SBF24. The filfe format is .raw

-Raw data of FTIR of 6 archaeological objects named SBF4; SBF9; SBF10; SBF20; SBF21; SBF24. The file format is .csv

-Raw data of ATR-FTIR of 1 archaeological object named SBF14. The file format is .csv

The acronym SBF stands for Steenbokfontein, a cave in the Western Cape province (South Africa) where the objects were found.

The data included in this dataset has been organized per specimen. For each specimen, more than one point was measured as indicated in the file name.

The file name includes the unique ID of the object + the analytical technique + the number of the scan (at least 2 per object). For example: SBF14_ATR_scan01

Raw data outputs 1-18

Raw data output 1. Differentially expressed genes in AML CSCs compared with GTCs as well as in TCGA AML cancer samples compared with normal ones. This data was generated based on the results of AML microarray and TCGA data analysis.

Raw data output 2. Commonly and uniquely differentially expressed genes in AML CSC/GTC microarray and TCGA bulk RNA-seq datasets. This data was generated based on the results of AML microarray and TCGA data analysis.

Raw data output 3. Common differentially expressed genes between training and test set samples the microarray dataset. This data was generated based on the results of AML microarray data analysis.

Raw data output 4. Detailed information on the samples of the breast cancer microarray dataset (GSE52327) used in this study.

Raw data output 5. Differentially expressed genes in breast CSCs compared with GTCs as well as in TCGA BRCA cancer samples compared with normal ones.

Raw data output 6. Commonly and uniquely differentially expressed genes in breast cancer CSC/GTC microarray and TCGA BRCA bulk RNA-seq datasets. This data was generated based on the results of breast cancer microarray and TCGA BRCA data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively.

Raw data output 7. Differential and common co-expression and protein-protein interaction of genes between CSC and GTC samples. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively.

Raw data output 8. Differentially expressed genes between AML dormant and active CSCs. This data was generated based on the results of AML scRNA-seq data analysis.

Raw data output 9. Uniquely expressed genes in dormant or active AML CSCs. This data was generated based on the results of AML scRNA-seq data analysis.

Raw data output 10. Intersections between the targeting transcription factors of AML key CSC genes and differentially expressed genes between AML CSCs vs GTCs and between dormant and active AML CSCs or the uniquely expressed genes in either class of CSCs.

Raw data output 11. Targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section.

Raw data output 12. CSC-specific targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section.

Raw data output 13. The protein-protein interactions between AML key CSC genes with themselves and their targeting transcription factors. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis.

Raw data output 14. The previously confirmed associations of genes having the highest targeting desirableness and CSC-specific targeting desirableness scores with AML or other cancers’ (stem) cells as well as hematopoietic stem cells. These data were generated based on a PubMed database-based literature mining.

Raw data output 15. Drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section.

Raw data output 16. CSC-specific drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section.

Raw data output 17. Candidate drugs for experimental validation. These drugs were selected based on their respective (CSC-specific) drug scores. CSC is the abbreviation of cancer stem cell.

Raw data output 18. Detailed information on the samples of the AML microarray dataset GSE30375 used in this study.

This document mainly includes Coding sequences of PME-domain and pro-region of Type-1 PME in representative plants,Raw data from fusion gene analysis by LIR inference,Raw data from repeated sequence studies within four Cruciferae representative species and Graphical Abstract.

Bulk RNA-seq data (smartseq2; raw freature counts) of naive murine CD4+ T cells co-cultured with murine HSPCs (THSPC), or with murine DCs (TDC), or murine LSKs as control condition, in the presence or absence of antigen (ova,ctrl)

This entry contains raw data files from experiments performed on the Vulcan beamline at the Spallation Neutron Source at Oak Ridge National Laboratory using a pressure cell. Cylindrical granite and marble samples were subjected to confining pressures of either 0 psi or approximately 2500 psi and internal pressures of either 0 psi, 1500 psi or 2500 psi through a blind axial hole at the center of one end of the sample. The sample diameters were 1.5" and the sample lengths were 6". The blind hole was 0.25" in diameter and 3" deep. One set of experiments measured strains at points located circumferentially around the center of the sample with identical radii to determine if there was strain variability (this would not be expected for a homogeneous material based on the symmetry of loading). Another set of experiments measured load variation across the radius of the sample at a fixed axial and circumferential location. Raw neutron diffraction intensity files and experimental parameter descriptions are included.

Dataset comprising raw paired RNA-seq data in fastq.gz format for 7 samples of rosette forming brain tumors

README

WARNING

Below is a template to write a README file for this BIDS dataset. If this message is still present, it means that the person exporting the file has decided not to update the template.If you are the researcher editing this README file, please remove this warning section. The README is usually the starting point for researchers using your dataand serves as a guidepost for users of your data. A clear and informativeREADME makes your data much more usable. In general you can include information in the README that is not captured by some otherfiles in the BIDS dataset (dataset_description.json, events.tsv, ...). It can also be useful to also include information that might already bepresent in another file of the dataset but might be important for users to be aware ofbefore preprocessing or analysing the data. If the README gets too long you have the possibility to create a /doc folderand add it to the .bidsignore file to make sure it is ignored by the BIDS validator. More info here: https://neurostars.org/t/where-in-a-bids-dataset-should-i-put-notes-about-individual-mri-acqusitions/17315/3

Details related to access to the data

• [ ] Data user agreement If the dataset requires a data user agreement, link to the relevant information.
• [ ] Contact person Indicate the name and contact details (email and ORCID) of the person responsible for additional information.
• [ ] Practical information to access the data If there is any special information related to access rights orhow to download the data make sure to include it.For example, if the dataset was curated using datalad,make sure to include the relevant section from the datalad handbook:http://handbook.datalad.org/en/latest/basics/101-180-FAQ.html#how-can-i-help-others-get-started-with-a-shared-dataset ## Overview
• [ ] Project name (if relevant)
• [ ] Year(s) that the project ran If no scans.tsv is included, this could at least cover when the data acquisitionstarter and ended. Local time of day is particularly relevant to subject state.
• [ ] Brief overview of the tasks in the experiment A paragraph giving an overview of the experiment. This should include thegoals or purpose and a discussion about how the experiment tries to achievethese goals.
• [ ] Description of the contents of the dataset An easy thing to add is the output of the bids-validator that describes what type ofdata and the number of subject one can expect to find in the dataset.
• [ ] Independent variables A brief discussion of condition variables (sometimes called contrastsor independent variables) that were varied across the experiment.
• [ ] Dependent variables A brief discussion of the response variables (sometimes called thedependent variables) that were measured and or calculated to assessthe effects of varying the condition variables. This might also includequestionnaires administered to assess behavioral aspects of the experiment.
• [ ] Control variables A brief discussion of the control variables --- that is what aspectswere explicitly controlled in this experiment. The control variables mightinclude subject pool, environmental conditions, set up, or other thingsthat were explicitly controlled.
• [ ] Quality assessment of the data Provide a short summary of the quality of the data ideally with descriptive statistics if relevantand with a link to more comprehensive description (like with MRIQC) if possible. ## Methods ### Subjects A brief sentence about the subject pool in this experiment. Remember that Control or Patient status should be defined in the participants.tsvusing a group column.
• [ ] Information about the recruitment procedure- [ ] Subject inclusion criteria (if relevant)- [ ] Subject exclusion criteria (if relevant) ### Apparatus A summary of the equipment and environment setup for theexperiment. For example, was the experiment performed in a shielded roomwith the subject seated in a fixed position. ### Initial setup A summary of what setup was performed when a subject arrived. ### Task organization How the tasks were organized for a session.This is particularly important because BIDS datasets usually have task dataseparated into different files.)
• [ ] Was task order counter-balanced?- [ ] What other activities were interspersed between tasks?
• [ ] In what order were the tasks and other activities performed? ### Task details As much detail as possible about the task and the events that were recorded. ### Additional data acquired A brief indication of data other than theimaging data that was acquired as part of this experiment. In additionto data from other modalities and behavioral data, this might includequestionnaires and surveys, swabs, and clinical information. Indicatethe availability of this data. This is especially relevant if the data are not included in a phenotype folder.https://bids-specification.readthedocs.io/en/stable/03-modality-agnostic-files.html#phenotypic-and-assessment-data ### Experimental location This should include any additional information regarding thethe geographical location and facility that cannot be includedin the relevant json files. ### Missing data Mention something if some participants are missing some aspects of the data.This can take the form of a processing log and/or abnormalities about the dataset. Some examples:
• A brain lesion or defect only present in one participant- Some experimental conditions missing on a given run for a participant because of some technical issue.- Any noticeable feature of the data for certain participants- Differences (even slight) in protocol for certain participants. ### Notes Any additional information or pointers to information thatmight be helpful to users of the dataset. Include qualitative informationrelated to how the data acquisition went.

This data set includes the raw data and the posterior samples from the Bayesian models referring to the article, Seeing Both the Forest and the Trees Distinct Resolution of Hierarchical Representations in Visual Working Memory

This repository contains all original code for the system control, image reconstruction and image registration for the MLS-SIM system. The example raw data was generated by the MLS-SIM imaging system on awake head-fixed mice. No further processing was involved. For detailed information, please refer to the published literature related to this data.

This dataset is about: (Figure 4) Raw data of XRD measurements of Ganzi_loess_section samples, MGCK2G. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.836298 for more information.