Find research datasets worth reusing

opencc-by-4.0Feb 2023View details →

raw data of figures 3, 4 and 5

Raw data plotted in figures 3, 4 and 5 of the article with DOI: https://doi.org/10.1103/PhysRevApplied.18.064048

opencc-by-4.0Feb 2022View details →

Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries

Summary: There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively. The code, source data, and the simultaneously farm-size- and crop-specific harvested area datasets, including the GAEZv4 crop map based dataset and SPAM2010 crop map based dataset, are open-access, free, and available, which can be found below. The resulting dataset is available in *.csv and *.nc (netCDF) for each crop and farming system. For each crop, farming system, and farm size, we provide the gridded harvested area in the coordinate Systems of EPSG:4326 - WGS 84. Gridded summaries over crops and farming systems are also available. ----------------------------------------------------------------------------------------------------------------------- How to cite this dataset: Su, H., Willaarts, B., Luna-Gonzalez, D., Krol, M.S. and Hogeboom, R.J., 2022. Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries. Earth System Science Data, 14(9), pp.4397-4418. ----------------------------------------------------------------------------------------------------------------------- Update history: I am happy to receive any questions, comments, or potential collaboration on further dataset development. Please drop your email to Han Su (h.su@utwente.nl, han_su20@163.com) Version 1.03.1: Fix bugs in data format; Netcdf didn't show properly before in QGIS. Data underlying the three versions are the same. Version 1.02: New data summary, add Netcdf data format Version 1: Initial dataset for peer-review, CSV format only ----------------------------------------------------------------------------------------------------------------------- Note: please cite the original publications/sources if any data source based on which this dataset was developed is reused for your own study. SPAM2010:  Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth System Science Data, 12, 3545-3572, 10.5194/essd-12-3545-2020, 2020. GAEZv4: FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN, Rome, Italy, 2021 The dataset of Ricciardi et al.'s: Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.: How much of the world's food do smallholders produce?, Global Food Security, 17, 64-72, 2018. The global dominant field size dataset: Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.: Estimating the global distribution of field size using crowdsourcing, Glob Chang Biol, 25, 174-186, 10.1111/gcb.14492, 2019. GLC-Share: Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share (GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, 2014. CAAS-IFPRI cropland extent map: Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth System Science Data, 12, 1913-1928, 10.5194/essd-12-1913-2020, 2020.

opencc-by-4.0Mar 2023View details →

Gulf of Mexico planktonic foraminifera and stable isotope data from core EN-032-18PC spanning MIS 9 to MIS 5

Database of the accepted manuscript: Arellano-Torres et al., 2023. The Loop Current circulation over the MIS 9 to MIS 5 based on planktonic foraminifera assemblages from the Gulf of Mexico. Paleoceanography and Paleoclimatology, DOI: 10.1029/2022PA004568 In the sediment Core EN-032-18PC collected below the influence of the Loop Current in the eastern Gulf of Mexico, we studied mixed layer conditions and the intensity of the surface and subsurface waters flowing from the Caribbean to the gulf. This database includes analyses of 136 samples in three Supplementary Tables. (Table S1) Bulk sediment and sand fraction (>62 μm) weight (g), the absolute abundance (tests per sample) of 33 species of planktonic foraminifera. (Table S2) Relative abundance (%) of planktonic foraminifera and factor loadings of two factors (Q-mode factor analysis). (Table S3) Stable isotopes (δ18O-PDB and δ13C-PDB) (&permil;) of Globigerinoides ruber (white) and the loess-smoothing of the series with polynomial regression.

opencc-by-4.0Mar 2023View details →

Graph 5: Publication Types for Heiner Müller's Writings 1989-1998.

Graph 5 shows the publication types that Heiner Müller used to publish his writing in the years from 1989-1998.

Rereferenced Chemical Shift files used to select dihedrals for the 5 secondary structure conformations used to calculate Conformational Variability (ConVa).

These Chemical Shifts in this repository were processed with ShiftCrypt (1) and then processed as described in the manuscript.    1- Gabriele Orlando, Daniele Raimondi, Luciano Porto Kagami, Wim F Vranken, ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values, Nucleic Acids Research, Volume 48, Issue W1, 02 July 2020, Pages W36–W40, <a href="https://doi.org/10.1093/nar/gkaa391">https://doi.org/10.1093/nar/gkaa391</a>

replicAnt - Plum2023 - 3D Models - Unreal Engine 5

This dataset contains the 3D models used to generate all synthetic data presented in the replicAnt - generating annotated images of animals in complex environments using Unreal Engine manuscript. The models have been generated with the open-source photogrammetry platform scAnt <a href="https://peerj.com/articles/11155/">peerj.com/articles/11155</a>/ and have been pre-processed and converted into Unreal Engine 5 compatible .uasset files, to be used with the associated replicAnt project available from <a href="https://github.com/evo-biomech/replicAnt">https://github.com/evo-biomech/replicAnt</a>. Abstract: Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To overcome these limitations, we created replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware instead. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation; and that it increases the subject-specificity and domain-invariance of the trained networks, so conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field. Funding This study received funding from Imperial College’s President’s PhD Scholarship (to Fabian Plum), and is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 851705, to David Labonte). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Quantum-Chemical Bonding Database (Unprocessed data : Part 5)

This dataset is published as part of our publication: <a href="https://www.nature.com/articles/s41597-023-02477-5">A Quantum-Chemical Bonding Database for Solid-State Materials</a>Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID)

Corona Immunitas Phases 5 and 6 - Central Database

Corona Immunitas was a Switzerland-wide, population-base SARS-CoV-2 study, assessing the seroprevalence and other study outcomes over six phases, starting in 2020 and ending in 2023. This entry describes the central database of study phases 5 and 6. Study phases 1 to 4 have been described <a href="https://doi.org/10.5281/zenodo.7520050">elsewhere</a>. This entry consists of the documentation of the database, the guide how to get access to the data and the description of available variables.

opencc-by-4.0May 2023View details →

Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)

Classify video clips with natural scenes of actions performed by people visible in the videos. See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a> This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: <a href="https://doi.org/10.5281/zenodo.7882861">https://doi.org/10.5281/zenodo.7882861</a> . Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>). Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a> For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example). I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date.  Cite this dataset as: Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a> To download the dataset via the command line, please use: <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>

opencc-by-4.0Apr 2022View details →

Supporting Data Ferguson, Camenzind, et al., "Measurement-induced induced population switching", Phys. Rev. Research 5, 023028 (2023)

This repository contains data for the publication "Measurement-induced population switching", Phys. Rev. Research 5, 023028 (2023) by Ferguson, Camenzind, et al. Abstract Quantum information processing is a key technology in the ongoing second quantum revolution, with a wide variety of hardware platforms competing toward its realization. An indispensable component of such hardware is a measurement device, i.e., a quantum detector that is used to determine the outcome of a computation. The act of measurement in quantum mechanics, however, is naturally invasive as the measurement apparatus becomes entangled with the system that it observes. This always leads to a disturbance in the observed system, a phenomenon called quantum measurement backaction, which should solely lead to the collapse of the quantum wave function and the physical realization of the measurement postulate of quantum mechanics. Here we demonstrate that backaction can fundamentally change the quantum system through the detection process. For quantum information processing, this means that the readout alters the system in such a way that a faulty measurement outcome is obtained. Specifically, we report a backaction-induced population switching, where the bare presence of weak, nonprojective measurements by an adjacent charge sensor inverts the electronic charge configuration of a semiconductor double quantum dot system. The transition region grows with measurement strength and is suppressed by temperature, in excellent agreement with our coherent quantum backaction model. Our result exposes backaction channels that appear at the interplay between the detector and the system environments, and opens new avenues for controlling and mitigating backaction effects in future quantum technologies.

Timeline of government interventions and events regarding the COVID-19 pandemic in Sweden December 31, 2019, to May 5, 2023.

The Swedish approach to managing the COVID-19 pandemic has received significant attention in international scholarly work and the press. For this dataset, we have reviewed governmental and media archives to build a detailed timeline that chronicles significant policies, interventions, and events in the Swedish management of COVID-19. The dataset contains summary descriptions of what took place, when it happened, and who the principal actors involved were. Links to primary sources are provided for each entry. Because of the level of detail and saturation, the dataset offers a detailed account of Swedish pandemic governance and will benefit anyone working on Swedish pandemic management or doing comparative work between Sweden and other jurisdictions. The dataset contains details on the date an event took place (column 1), tags to facilitate navigation (column 2), details on the principal actors involved in the event (column 3), a summary description of what took place and who was involved (column 4), and links to primary materials (e.g., archival entries) (columns 4-12). Through a structured and detailed outline, the dataset provides a saturated account of policy interventions and events in Sweden during the COVID-19 pandemic for the period 2020-2023 until it was no longer considered a public health emergency of international concern by the WHO and complements existing and less detailed timelines published at earlier points in the period.

ENDGAME - Laboratory Experiment 2022-11-29 Exp. 002 - Part 5 - High Speed Camera data

Preliminary test with high speed camera and Schlieren shadow photography. Images of the rise of some coalescing air bubbles in a 2D setup obtained using 2 parallel glass sheets (3 mm thickness) separated by rubber seals and filled with distilled water. The gap between the two parallel sheets is 3 mm. Air was injected manually into the 2D setup through a straw (4 mm diam) at high speed. Frame rate of the high speed camera is 250 fps. The spherical mirror used for the Schlieren setup was 75 mm wide with a 750 mm focal length.

Deconvolved STED nanoscopy images of the nuclear phosphatidylinositol 4,5-bisphosphate and nuclear speckle marker SON together with deconvolved confocal images of DAPI stained nuclei in human formalin-fixed paraffin-embedded skin warts sections

The collection and analysis of formalin-fixed paraffin-embedded (FFPE) human skin sections was approved by the local ethics-committee at the Department of Pathology, University of Cologne, Germany. Written informed consentwas obtained from all patients in accordance with the Declaration of Helsinki. For biopsy materials from archival paraffin blocks of human skin, an informed consent was obtained from all the subjects and ethical approval obtained from the Ethics Committee at the University of Cologne. Surgically removed human FFPE skin biopsies were sectioned into 4 µm sections. Sections were dewaxed, and indirectly immunofluorescently labeled against nuclear phosphatidylinositol 4,5-bisphosphate (nPI(4,5)P2) using  5 µg/mL rabbit primary polyclonal antibody (Echelon Biosciences Inc. Z-A045, clone 2C11). The primary antibody against nPI(4,5)P2 was recognized by the goat secondary antibody conjugated with Abberrior Star 635P (Abberior 2-0002-007-5). Sections were indirectly immunofluorescently labeled against nuclear speckle marker SON using  1 µg/mL rabbit primary polyclonal antibody (Abcam ab121759). The primary antibody against SON was recognized by the goat secondary antibody conjugated with Abberrior Star 580 (Abberrior ST580-1002). Sections were co-stained by DAPI 1:1000 in PBS for 5 min. Imaging of nPI(4,5)P2-635P channel was performed on Leica TCS SP8 STED 3x inverted DMi8 microscope with pulsed white light laser 470-640 nm 1.5 mW and 775 nm pulse STED laser >1.5 W controlled by Leica Application Suite X software and equipped with HC PL APO CS2 100x/1.40 OIL objective used with Leica Type F immersion oil n=1.518. Unidirectional xyz scanning speed was 400 Hz, line accumulation 8. Pixel size was 20 nm in X and Y. Channel settings: 7% 633 nm laser; 775 Notch filter; 50% 775 nm STED laser; 30% 3D STED; HyD 639-698 nm, photon-counting mode, gain 100, gating 0.3-10 ns. Imaging of SON-580 channel was performed on Leica TCS SP8 STED 3x inverted DMi8 microscope with pulsed white light laser 470-640 nm 1.5 mW and 775 nm pulse STED laser >1.5 W controlled by Leica Application Suite X software and equipped with HC PL APO CS2 100x/1.40 OIL objective used with Leica Type F immersion oil n=1.518. Unidirectional xyz scanning speed was 400 Hz, line accumulation 8. Pixel size was 20 nm in X and Y. Channel settings: 10% 585 nm laser; 775 Notch filter; 80% 775 nm STED laser, 30% 3D STED; Hybrid detector (HyD) 589-616 nm, photon-counting mode, gain 100, gating 0.4-10 ns. Z-stacks of STED images were deconvolved using Huygens Professional 22.10 software (Scientific Imaging B.V.). Data sets were processed using Workflow Processor. The workflow consisted of selecting images, setting up the microscopy and deconvolution parameters and saving deconvolved images as 8-bit TIFF single files for individual channels (which were later used for the quantitative analyses; see below). Microscopy parameters were optimized and set as follows. Sampling intervals were ≤20 nm in X and Y and  ≤20 nm in Z. Numerical aperture was 1.4; refractive indexes of the lens immersion oil was 1.518 and of the embedding media 1.458; objective quality was good, coverslip position was 0 µm and imaging direction was downward. For nPI(4,5)P2-635P STED channel the backprojected pinhole was 216 nm; excitation (ex.) and emission (em.) wavelengths (λ) were 633 and 651 nm, resp., ex. fill factor 2. STED depletion mode was pulsed, saturation factor 25, STED λ = 775, STED immunity factor 10 and STED 3X was 30%. Classic MLE algorithm with stabilization of Z-slices was used and signal-to-noise ratio was 5.1. For SON-580 STED channel the backprojected pinhole was 195 nm; excitation (ex.) and emission (em.) wavelengths (λ) were 585 and 602 nm, resp., ex. fill factor 2. STED depletion mode was pulsed, saturation factor 20, STED λ = 775, STED immunity factor 10 and STED 3X was 30%. Classic MLE algorithm with stabilization of Z-slices was used and signal-to-noise ratio was 4.

Multiplexed Staining Dataset - OMAP 5 - Liver-Lanthanides-conjugated antibodies and C60-secondary ion mass spectrometry imaging

This dataset contains images of multiplexed antibody panel on a human pediatric liver section including the nuclear marker and antibodies conjugated with lanthanides tags. The dataset is one example of serial experiments of multiplexed antibody staining and imaging. The antibody panel targets the major cell types and tissue structures in the liver tissue. Data acquisition was performed using single multiplexing imaging by C60-secondary ion mass spectrometry.

opencc-by-4.0Aug 2023View details →

EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 5: Aspects of ENCI I.: Hybridity, private/public, passive/active forms

This document is Part 5 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship (ENCI) between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe. EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.

opencc-by-4.0Aug 2023View details →

Hyperspectral 2D fan-beam X-ray CT dataset of 5 materials

Hyperspectral X-ray CT dataset acquired at the DTU 3D imaging center. The phantom consists of 5 materials: Aluminium (10 mm) and PVC (7.8 mm) in solid blocks. Sugar, H2O2, and H2O in circular glass containers. 3D array with dimension: 128 x 370 x 258 < channel, angle, horizontal >   Detector parameters: Number of detector pixels: 258 (concatenated from 2 detector modules with 128 pixels each and 2 pixel interpolated across a gap between detectors) Pixel size: 0.077 cm Sep=0.153  Pixels' gap length (cm) det_space=(ndet)*pixel_size+Sep # physical width of detector in cm (pixels*pixel_size), including the gap   Acquisition Parameters 360 # Angular span of projections in degrees 370 # Number of projections. note: last projection taken is not a duplicate of the first projection. At angle: 360/370 degrees from first projection. 115.0 # Source-Detector distance in cm 0 # Vertical source shift from perfect placement 0 # Vertical detector shift from perfect placement 57.5 # Source-AxisOfRotation distance in cm   rot_axis_x = 0 # x-position offset of AxisOfRotation rot_axis_y = 0 # y-position offset of AxisOfRotation

openCC (other)Mar 2024View details →

Macrosystems EDDIE Module 5 version 2: Introduction to Ecological Forecasting (Instructor Materials)

Ecological forecasting is a tool that can be used for understanding and predicting changes in populations, communities, and ecosystems. Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Ecological forecasters develop and update forecasts using the iterative forecasting cycle, in which they make a hypothesis of how an ecological system works; embed their hypothesis in a model; and use the model to make a forecast of future conditions. When observations become available, they can assess the accuracy of their forecast, which indicates if their hypothesis is supported or needs to be updated before the next forecast is generated. In this Macrosystems EDDIE (Environmental Data-Driven Inquiry & Exploration) module, students will apply the iterative forecasting cycle to develop an ecological forecast for a National Ecological Observation Network (NEON) site. Students will use NEON data to build an ecological model that predicts primary productivity. Using their calibrated model, they will learn about the different components of a forecast with uncertainty and compare productivity forecasts among NEON sites. The overarching goal of this module is for students to learn fundamental concepts about ecological forecasting and build a forecast for a NEON site. Students will work with an R Shiny interface to visualize data, build a model, generate a forecast with uncertainty, and then compare the forecast with observations. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This EDI data package contains instructional materials necessary to teach the module. Intructional materials (instructor manual, introductory presentation for the module, and a presentation to introduce students and instructors to R Shiny) are provided in both pdf and editable formats with

openCC (other)Apr 2023View details →

Metabolism and decomposition rates from 5 Lake Superior Tributaries, 2018-2019

Ecosystem respiration (ER), and decomposition are fundamental processes driving carbon cycling in streams. Most studies examine rates of autotrophic respiration (AR) and heterotrophic respiration (HR) together as ecosystem respiration (ER), even though these two processes are carried out by different groups of organisms, and these processes, alongside decomposition, may respond differently to ongoing changes in environmental factors. We measured metabolism (gross primary production and ER) and decomposition at eight sites in four streams in the Upper Peninsula of Michigan across gradients of canopy cover and DOC concentrations. We estimated AR and HR using quantile regression and used predictive modeling to determine the environmental drivers that predicted variation in these processes across the study streams. This data archive includes (1) continuous dissolved oxygen and temperature data used to model metabolism, (2) modeled rates of gross primary production, ecosystem respiration, autotrophic respiration and heterotrophic respiration, (3) estimates of decomposition rates determined using cotton strip assays, and (4) environmental model used for predictive modeling

openCC (other)Feb 2016View details →

Baltimore Ecosystem Study: Riparian vegetation data - 5 of 11 - 1999 seedling data

This is one of 11 datasets generated in a study of riparian vegetation in the Baltimore Ecosystem Study from 1999-2004. Comparisons of vegetation between the rural/suburban (upper) and urban (lower) sections of the watershed show distinct patterns across an urban to rural gradient. In the lower, more urban section of the watershed, wetland tree species are either absent or occur as small stems while upland species are abundant, in mixed sizes. A comparison of the number of wetland and upland species in the mostly urbanized Gwynns Falls riparian zone with non-urbanized Piedmont floodplains throughout Maryland shows approximately twice as many upland species in the urban floodplain than in non-urbanized floodplains. The majority of shrubs in riparian zones through the Gwynns Falls are upland species. For herbaceous species, frequencies of upland and wetland species are about equal in the upper and middle regions of the watershed, but upland species are more common in the more urban lower floodplains by a factor of greater than two.