Find research datasets worth reusing

opencc-by-4.0Oct 2021View details →

MUA PLidar for seeder-feeder process study

Polarization lidar observations are conducted on the campus of Wuhan University in Wuhan (30.5N, 114.4E), China. The polarization lidar data were saved to <a href="https://support.hdfgroup.org/HDF5/whatishdf5.html">HDF5</a> format with file naming as yyyymmdd_plidar.h5. Each of the HDF5 file contains the measurements at the fully day. And empty profiles were filled with NaN. The data consist of Analog data (AD) and Photon count rate data (PC) with dimension of height (2048 bins) * time (1024 profiles). The spatial and temporal resolution is 30 m and 1 min, respectively.

opencc-by-4.0Jul 2024View details →

OnSSET processed GIS input data for 20 Sub Sahara Africa countries

This repository contains the processed geospatial data files needed to run the Open-Source Spatial Electrification Tool (OnSSET), for 20 countries in Sub Sahara Africa. These data files were created by the KTH team for the Global Electrification Platform (GEP) model (https://electrifynow.energydata.info/). To access result files and geospatial population clusters, please go to https://energydata.info/dataset/?q=gep.

opencc-by-nc-nd-4.0Dec 2018View details →

Wastewater Treatment Unit Processes Datasets: Pollutant removal efficiencies, evaluation criteria and cost estimations

This dataset provides data on typical pollutant removal efficiencies, evaluation critera and cost estimation for 37 common wastewater treatment unit processes. The data is based on literature research, expert workshops and estimations. The following files are available: <ul> <li>Pollutant removal efficiencies, evaluation criteria and cost estimations for wastewater treatment unit processes (dataset) - Microsoft Excel: Database of pollutant removal efficiencies for 11 paramameters, cost estimation and evaluation criteria.</li> <li>Pollutant Removal Efficiencies for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Evaluation Criteria for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Cost Estimation for Wastewater Treatment Unit Processes (Dataset) - pdf</li> </ul>

opencc-by-4.0Dec 2018View details →

Raw and processed SPT data for "Guided nuclear exploration increases CTCF target search efficiency"

Raw and processed SPT data for “Guided nuclear exploration increases CTCF target search efficiency” Anders S. Hansen1,2,3,4,*, Assaf Amitai5,*, Claudia Cattoglio1,2,3,4, Robert Tjian1,2,3,4, Xavier Darzacq1,2,3   1: Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, USA; 2: Li Ka Shing Center for Biomedical and Health Sciences 3: CIRM Center of Excellence, University of California, Berkeley, Berkeley, USA; 4: Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, USA; 5: Department of Chemical Engineering, MIT, Cambridge 02139, Massachusetts, USA *: ASH and AA contributed equally.   Overview This repository contains all the raw and processed spaSPT data associated with “Guided nuclear exploration increases CTCF target search efficiency”. In total, this dataset contains data for 1,669 single-cell movies and trajectories with 85,612,029 unique displacements. In this ReadMe file we provide the following information: <ul> <li>The cell lines used in this study.</li> <li>How the data was collected.</li> <li>How to read the raw single-cell SPT spaSPT data.</li> <li>How to read the processed, quality-controlled and HMM-classified data</li> </ul> The document does not contain information about how the data was analyzed. For details on how the data was analyzed and for raw code to reproduce our figures, please go to <a href="https://gitlab.com/anders.sejr.hansen/anisotropy">https://gitlab.com/anders.sejr.hansen/anisotropy</a>   Cell lines and transient transfection constructs In total, 20 different cell lines were studied here. The mouse embryonic stem cells (mESCs) are in the JM8.N4 background (Pettitt et al., 2009) and the human cells were U2OS osteosarcoma cells. Cell lines expressing Halo-tagged proteins were either homozygous knock-in cell lines, wild-type cell lines transiently transfected with a plasmid over-expressing the protein of interest (Lipofectamine 3000), or wild-type cell lines stably over-expressing a transgene. The wild-type and C59 mESC cell lines were pathogen tested using the IMPACT II test (performed by IDEXX BioResearch) and where negative for all tested pathogens (full details provided in (Hansen et al., 2017)). The wild-type and C32 U2OS cell lines were tested for mycoplasma contamination and found to be clean and further authenticated by Short Tandem Repeat Profiling (STR profiling; 100% match with U2OS; full details provided in (Hansen et al., 2017)). Further details on each cell line or transfection construct are provided in the table in the ReadMe PDF (the MAT-file name is the name of the MAT-file containing the raw data). Data collection using stroboscopic photo-activation single-particle tracking (spaSPT) For full details on how the experiments where performed, please see the Materials and Methods section of the manuscript. Here we will briefly describe the protocol. To systematically investigate how proteins explore the nucleus we need a very large dataset (i.e. hundreds of thousands of trajectories) of very high quality SPT data (i.e. with minimal bias and without many tracking errors). The two major sources of bias in experimental SPT data are 1) motion-blurring and 2) tracking errors (Hansen et al., 2018). First, motion-blurring biases against detecting fast-moving molecules since while the camera is exposed, a fast-moving molecule will spread its photons over many pixels and no longer resemble a diffraction limited spot, whereas a bound or slow-moving molecule will emit all its photons as a single diffraction limited spot. Since most localization algorithms readily detect diffraction limited spots, but not motion-blurs this introduces a clear bias. spaSPT overcomes this bias by strobing the excitation laser (Elf et al., 2007): using just a single 1 ms excitation pulse, we simultaneously achieve high signal-to-background (~10-fold), while minimizing motion-blur as previously demonstrated (for a more complete discussion, please see (Hansen et al., 2018)). Second, tracking errors are largely caused by high particle densities: for example, when trajectories overlap, tracking errors occur which clearly prevents accurately analysis of protein diffusion and which could lead to artefactual conclusions. By using the bright photo-activatable Janelia Fluor dyes PA-JF549 and PA-JF646 (Grimm et al., 2016), we can overcome this challenge (this is also known as sptPALM (Manley et al., 2008)). During the camera integration time between frame (~447 microseconds), we pulse the 405 nm photo-activation laser at an intensity such that the mean localization density is around 1 molecule per nucleus per frame. At this density, tracking errors are greatly reduced, yet we are able to collect tens of thousands of frames at this density per cell and thus obtain large amounts of data despite imaging at a very low density. For a more complete discussion of spaSPT please see (Hansen et al., 2017, 2018). To generate a large dataset at multiple time-scales, we collected spaSPT data (generally around 8 cells per replicate and four biological replicates; occasionally a cell would be removed from subsequent analysis if the localization density was too high and thus prone to tracking errors) at 3 camera exposure times: 4 ms (add ~447 microseconds), 7 ms (add ~447 microseconds) and 13 ms (add ~447 microseconds); this roughly corresponds to frame-rates of ~223 Hz, 133 Hz, and 74 Hz. To generate data at longer lag times (this was only done after HMM-classification; see also main paper), this data was subsampled according to the table in the ReadMe PDF. Overview of raw, unprocessed single-cell spaSPT data This repository contains all the raw, unprocessed spaSPT data for each single cell in the directory “UnprocessedSingleCellData”. In total, this directory contains 1669 MAT-files corresponding to the 1669 single-movies recorded here. The files are systematically named for their parent cell line, replicate number and cell number. For example, “mESC_C59_Halo-mCTCF_133Hz_Rep4_Cell8.mat” contains SPT data for mouse embryonic stem cell line clone 59 and Halo-CTCF was imaged at ~133 Hz; the file is for cell number 8 in biological replicate number 4. All the other single-cell experiments are similarly named. Each MAT-file contain all the trajectories recording from a single-cell movie and stored as a “structure array” object, “trackedPar”. trackedPar contains three variables for each trajectory: <ul> <li>trackedPar.xy: “xy” is a matrix with 2 columns and a number of rows corresponding to the number of frames where the molecule was located. The first column is the x-coordinate and the second column is the y-coordinate and the units are micrometers.</li> <li>trackedPar.Frame: “Frame” is a column vector where each element is an integer describing the frame number wherein the particle was localized (each trajectory contains at most 1 gap between frames).</li> <li>trackedPar.TimeStamp: “TimeStamp” is a column vector where each element is the timepoint (in units of seconds) where the molecule was localized.</li> </ul> Note that this Matlab format is directly readable by Spot-On: <a href="https://spoton.berkeley.edu/">https://spoton.berkeley.edu/</a>   Overview of HMM-classified and processed SPT data The raw spaSPT data was processed (to remove any rare tracking errors) and all the data merged. The data was the classified into either a “bound” or “free” state using a 2-state Hidden-Markov Model (HHM; vbSPT (Persson et al., 2013)) and then temporally subsampled to generate data at longer frame rates. Full details on how the data was processed are given in the Materials and Methods section as well as on GitLab. Raw code to reproduce all the figures from unprocessed single-cell spaSPT data is also available at GitLab: <a href="https://gitlab.com/anders.sejr.hansen/anisotropy">https://gitlab.com/anders.sejr.hansen/anisotropy</a> All the HMM-classified data for each cell line at each frame rate is stored in a single MAT-file. The MAT-file contains two key cell arrays: “CellTracks” and “CellTrackViterbiClass”. The two cell arrays contain the XY-coordinates and classification, respectively. More, specifically, suppose trajectory k is made up of n localizations. Then: CellTracks{k}: a matrix of n rows and 2 columns containing the x,y coordinates in units of micrometers. CellTrackViterbiClass{k}: a column vector of length n-1 where each entry is an integer: either “1” or “2”. Thus, the length of this vector is 1 less than the number of rows in the CellTracks matrix. This is because only the displacements and not the localizations are classified. For example, the CellTrackViterbiClass{k} reads [1;1;1;2;2] it means that there were 6 localizations and that the first 3 displacements (1à2, 2à3, 3à4) were classified as “bound” and the last 2 displacements (4à5, 5à6) were classified as “free”.       References Elf, J., Li, G.-W., and Xie, X.S. (2007). Probing transcription factor dynamics at the single-molecule level in a living cell. Science 316, 1191–1194. Grimm, J.B., English, B.P., Choi, H., Muthusamy, A.K., Mehl, B.P., Dong, P., Brown, T.A., Lippincott-Schwartz, J., Liu, Z., Lionnet, T., et al. (2016). Bright photoactivatable fluorophores for single-molecule imaging. Nat. Methods 66779. Hansen, A.S., Pustova, I., Cattoglio, C., Tjian, R., and Darzacq, X. (2017). CTCF and cohesin regulate chromatin loop stability with distinct dynamics. Elife 6. Hansen, A.S., Woringer, M., Grimm, J.B., Lavis, L.D., Tjian, R., and Darzacq, X. (2018). Robust model-based analysis of single-particle tracking experiments with Spot-On. Elife 7, e33125. Manley, S., Gillette, J.M., Patterson, G.H., Shroff, H., Hess, H.F., Betzig, E., and Lippincott-Schwartz, J. (2008). High-density mapping of single-molecule trajectories with photoactivated localization microscopy. Nat. Methods 5, 155–157. Persson, F., Lindén, M., Unoson, C., and Elf, J. (2013). Extracting intracellular diffusive states and transition rates from single-molecule tracking data. Nat. Methods 10, 265–269. Pettitt, S.J., Liang, Q., Rairdan, X.Y., Moran, J.L., Prosser, H.M., Beier, D.R., Lloyd, K.C., Bradley, A., and Skarnes, W.C. (2009). Agouti C57BL/6N embryonic stem cells for mouse genetic resources. Nat. Methods 6, 493–495. Teves, S.S., An, L., Hansen, A.S., Xie, L., Darzacq, X., and Tjian, R. (2016). A dynamic mode of mitotic bookmarking by transcription factors. Elife 5.

REFLOW - Dataset for the report on P cycling and physico-chemical processes after fertilizer additions

This data set contains daily phosphorus (P) turnover values in soils amended with recycled and mineral P fertilisers

opencc-zeroNov 2022View details →

dryad40/100

Processed single cell data from CODEX multiplexed imaging of the human intestine

We performed CODEX (co-detection by indexing) multiplexed imaging on 64 sections of the human intestine (~16 mm2) from 8 donors (B004, B005, B006, B008, B009, B010, B011, and B012) using a panel of 57 oligonucleotide-barcoded antibodies. Subsequently, images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue. The outputs of this process were data frames of 2.6 million cells with 57 antibody fluorescence values quantified from each marker. Each cell has its cell type, cellular neighborhood, community of neighborhooods, and tissue unit defined with x, y coordinates representing pixel location in the original image. This is from a total of 25 cell types, 20 multicellular neighborhoods, 10 communities of neighborhoods, and 3 tissue segments that could be used to understand the cellular interactions, composition, and structure of the human intestine from the duodenum to the sigmoid colon and understand differences between different areas of the intestine. This data could be used as a healthy baseline to compare other single-cell datasets of the human intestine, particularly multiplexed imaging ones. The overall structure of the datasets is individual cells segmented out in each row. Columns MUC2 through CD161 are the markers used for clustering the cell types. These are the columns that are the values of the antibody staining the target protein within the tissue quantified at the single-cell level. This value is the per cell/area averaged fluorescent intensity that has subsequently been z normalized along each column as described above. OLFM4 through MUC6 were captured in the quantification but not used within the clustering of cell types. Other columns are explained in the table in the Usage Notes section below. Along with this main data table, there is also a donor metadata table that links the donor ids to clinical metadata such as: age, sex, race, BMI, history of diabetes, history of cancer, history of hypertension, and history of gastorintestinal disease. The raw imaging data can be found at (<a href="https://portal.hubmapconsortium.org/">https://portal.hubmapconsortium.org/</a>). We have created a landing page with links to all the raw dataset IDs and the HuBMAP ID for this Collection is HBM692.JRZB.356 and the DOI is:10.35079/HBM692.JRZB.356. This can be used to also pair it with the matched snRNAseq and snATACseq for each section of tissue.

Dataset Literature Review Digital Forensic and Image Processing

Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "digital forensic" dan "image processing"

Dataset Literature Review Digital Forensic AND Image Processing

Data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "digital forensic" dan "image processing"

Supplementary materials (processed data) for paper "Electrical signal transfer characteristics of mycelium-bound composites and fungal fruiting bodies."

Processed data for paper "Electrical signal transfer characteristics of mycelium-bound composites and fungal fruiting bodies."

opencc-zeroNov 2022View details →

dryad40/100

Face processing in the infant brain after pandemic lockdown

The role of visual experience in the development of face processing has long been debated. We present a new angle on this question through a serendipitous study that cannot easily be repeated. Infants viewed short blocks of faces during fMRI in a repetition suppression task. The same identity was presented multiple times in half of the blocks (Repeat condition) and different identities were presented once each in the other half (Novel condition). In adults, the fusiform face area (FFA) tends to show greater neural activity for Novel vs. Repeat blocks in such designs, suggesting that it can distinguish same vs. different face identities. As part of an ongoing study, we collected data before the COVID-19 pandemic and after an initial local lockdown was lifted. The resulting sample of 12 infants (9–24 months) was divided equally into pre-and post-lockdown groups with matching ages and data quantity/quality. The groups had strikingly different FFA responses: pre-lockdown infants showed repetition suppression (Novel > Repeat), whereas post-lockdown infants showed the opposite (Repeat > Novel), often referred to as repetition enhancement. These findings provide speculative evidence that altered visual experience during the lockdown, or other correlated environmental changes, may have affected face processing in the infant brain.

Glacial meltwater determines the balance between autotrophic and heterotrophic processes in a Greenland fjord

Raw data for the summer CTD transect, the annual CTD mooring, data from the seasonal sediment trap, data from the oxygen and 14C incubations and nutrients data presented in the paper

Fig. 2. Atherigona parviclivis Muller, 2015. a. Trifoliate process, ventral. b. Same, lateral. c. Hypopygial prominence, dorsal. d in Some Afrotropical species of Atherigona Rondani (Diptera: Muscidae) revisited and a new species described

Fig. 2. Atherigona parviclivis Muller, 2015. a. Trifoliate process, ventral. b. Same, lateral. c. Hypopygial prominence, dorsal. d. Same, lateral.

Sicily Sentinel-1 dataset processed with MiaplPy

Displacement on Sicily, Italy Sensor: Sentinel-1 Descending track 124 Time: 2017.10 - 2019.09, 63 acquisitions Processor: ISCE/topsStack This is the outputs from time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.

Bristol Dry Lake Sentinel-1 dataset processed with MiaplPy and Mintpy

Displacement data on Bristol Dry Lake, USA Sensor: Sentinel-1 Descending track 173 Time: 2017.02 - 2021.01, 157 acquisitions Processor: ISCE/topsStack This is the outputs from time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a> and <a href="https://github.com/insarlab/MintPy">MintPy</a>.

Mud Creek landslide Sentinel-1 dataset processed with MiaplPy

Displacement timeseries on Mud Creek landslide, USA Sensor: Sentinel-1 Descending track 42 Time: 2015.03.01 - 2017.05.13, 63 acquisitions Processor: ISCE/topsStack This is the outputs from time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.

Miami Sentinel-1 dataset processed with MiaplPy

A stack of Coregistered SLCs and displacement timeseries on Miami, USA Sensor: Sentinel-1 Ascending track 48 Time: 2015.09.21 - 2021.11.12, 147 acquisitions Processor: ISCE/topsStack This is the outputs from time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.

opencc-zeroDec 2022View details →

dryad40/100

Rapid resource depletion on coral reefs disrupts competitor recognition processes among butterflyfish species

Avoiding costly fights can help conserve energy needed to survive rapid environmental change. Competitor recognition processes help resolve contests without escalating to attack, yet we have limited understanding of how they are affected by resource depletion and potential effects on species coexistence. Using a mass coral mortality event as a natural experiment and 3,770 field observations of butterflyfish encounters, we test how rapid resource depletion could disrupt recognition processes in butterflyfishes. Following resource loss, heterospecifics approached each other more closely before initiating aggression, fewer contests were resolved by signalling, and the energy invested in attacks was greater. In contrast, behaviour towards conspecifics did not change. As predicted by theory, conspecifics approached one another more closely and were more consistent in attack intensity yet, contrary to expectations, resolution of contests via signalling was more common among heterospecifics. Phylogenetic relatedness or body size did not predict these outcomes. Our results suggest that competitor recognition processes for heterospecifics became less accurate after mass coral mortality, which we hypothesise is due to altered resource overlaps following dietary shifts. Our work implies that competitor recognition is common among heterospecifics, and disruption of this system could lead to suboptimal decision-making, exacerbating sublethal impacts of food scarcity.

opencc-by-nc-sa-4.0Dec 2022View details →

Dataset of the survey on the digital transformation of business processes at universities in Germany

In spring 2022, a broad survey was conducted among all 423 German universities on the status and the future of the digitalisation of administrative business processes. During the research survey, participants gave or rejected their consent for the publication of their contribution as open data. Respecting the roughly 30% of participants who declined, we published all remaining data here on Zenodo. The raw version of the dataset contains filtered, but pseudonymised data. The second version of the dataset is modified by the quality control process as described in section 3.3. of the corresponding Data Paper. The published data also includes the catalogue of questions indicating the respective category of time and competency pattern in which the question was used. This catalogue also contains the match with the PDF printout of the web-based questionnaire and the corresponding variables (V1-V1073) of the data records. Finally, the general introduction as well as the proposed letter to the target groups are included in the data files for reference.

opencc-by-4.0Apr 2022View details →

Replication Package for: Streaming vs. Functions: A Cost Perspective on Cloud Event Processing

In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data volumes. Despite their architectural differences, both can be used to model and implement loosely-coupled job graphs. In this paper, we consider the selection of FaaS and DSP from a cost perspective. We implement stateless and stateful workflows from the Theodolite benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we show how application type, cloud service provider, and runtime environment can influence the cost of application deployments and derive decision guidelines for cloud engineers.