Find research datasets worth reusing

opencc-by-4.0May 2015View details →

The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty. in Fowler's Toad (Anaxyrus fowleri) occupancy in the southern mid-Atlantic, USA

The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty.

opencc-by-4.0May 2024View details →

Dataset containing spectra of degree of linear polarization of human skin

Dataset contains spectra of degree of linear polarization (DOLP) of human finger skin. Experimental studies involved 32 conditionally healthy volunteers (9 males and 23 females, aged 22-76 years).

Electron Backscatter Diffraction Patterns from Titanium-added Interstitial-free Steel Containing Subgrains

<h3>Associated Publications</h3> <ol> <li>Bennett IV, T.J. and Taleff, E.M. Dynamic Grain Growth Driven by Subgrain Boundaries in an Interstitial-Free Steel During Deformation at 850 °C. Metall Mater Trans A 55, 429–446 (2024). <a href="https://doi.org/10.1007/s11661-023-07256-w">https://doi.org/10.1007/s11661-023-07256-w</a>.</li> <li>Bennett IV, T.J. and Taleff, E.M. Imaging and Segmenting Grains and Subgrains using Backscattered Electron Techniques. Under review (2024).</li> </ol> <h3>Data Description</h3> These data were collected by Thomas J. Bennett IV on July 28, 2022. The electron backscatter diffraction (EBSD) data and associated electron backscatter diffraction patterns (EBSPs) contained herein were acquired from a titanium-added interstitial-free (Ti-IF) steel sheet material containing numerous subgrains.  The Ti-IF steel specimen that provided these data was ramped to 850 degrees Celsius over 30 minutes, held at this temperature for one hour, and then deformed at a constant true-strain rate of 10^-4 s^-1. Upon reaching a final true strain of 0.225, the specimen was air quenched while maintaining a constant stress to preserve subgrains formed during high-temperature deformation. The tensile specimen was cut from a Ti-IF steel sheet received in a hard as-rolled condition with the tensile axis parallel to the sheet rolling direction. EBSPs were acquired from a section cut from the center of the deformed gage region using a JEOL JSM-IT300HR SEM equipped with an EDAX Velocity EBSD camera at the Center for Integrated Nanotechnologies. The following conditions were used for EBSD data acquisition: <table> <tbody> <tr> <td>Accelerating Voltage:</td> <td>20 kV</td> </tr> <tr> <td>Beam Current:</td> <td>80%</td> </tr> <tr> <td>Working Distance:</td> <td>20.0 mm</td> </tr> <tr> <td>Magnification:</td> <td>200×</td> </tr> <tr> <td>Dynamic Focus:</td> <td>44 (out of 255, arbitrary units)</td> </tr> <tr> <td>Specimen Tilt:</td> <td>70 degrees</td> </tr> <tr> <td>Scanning Grid Type:</td> <td>Square</td> </tr> <tr> <td>Step Size (x and y):</td> <td>0.5 μm</td> </tr> <tr> <td>Scan Size:</td> <td>520 (across) × 340 (down) pixels</td> </tr> <tr> <td>EBSD Camera Resolution:</td> <td>446 × 446 pixels</td> </tr> <tr> <td>EBSD Camera Binning:</td> <td>1 × 1</td> </tr> <tr> <td>EBSD Camera Exposure Time:</td> <td>10 ms</td> </tr> <tr> <td>Frame Averaging:</td> <td>None</td> </tr> <tr> <td>Specimen Tensile Direction:</td> <td>Horizontal</td> </tr> <tr> <td>Specimen Rolling Direction:</td> <td>Horizontal</td> </tr> <tr> <td>Specimen Long Transverse Direction:</td> <td>Vertical</td> </tr> <tr> <td>Specimen Short Transverse Direction:</td> <td>Normal to plane</td> </tr> <tr> <td>Pattern Center (EMSphInx Convention):</td> <td>(x_pc, y_pc, L) = (-0.2 pixels, 112.76 pixels, 21736.4 μm)</td> </tr> <tr> <td>EBSD Camera Elevation Angle:</td> <td>3 degrees</td> </tr> <tr> <td>EBSD Camera Screen Width:</td> <td>32 mm</td> </tr> <tr> <td>Pixel size on EBSD Camera Screen:</td> <td>71.749 μm/pixel ( = 32000 μm / 446 pixels)</td> </tr> </tbody> </table>   Note: Conversions between different pattern center conventions may be found in the journal article below or at the following link: <a href="https://github.com/EMsoft-org/EMsoft/wiki/DItutorial">https://github.com/EMsoft-org/EMsoft/wiki/DItutorial</a>. <ul> <li>Jackson, M.A., Pascal, E., and De Graef, M. Dictionary Indexing of Electron Back-Scatter Diffraction Patterns: a Hands-On Tutorial. Integr Mater Manuf Innov 8, 226–246 (2019). <a href="https://doi.org/10.1007/s40192-019-00137-4">https://doi.org/10.1007/s40192-019-00137-4</a>.</li> </ul> <h3>File Descriptions</h3> <ul> <li>Specimen_orientation.pdf - A schematic showing specimen reference directions and the orientation used for EBSD data acquisition.</li> <li>Patterns.zip - A compressed archive containing Patterns.up2. This file contains 16-bit EBSPs and is 70,336,697,616 bytes (70.3 GB) uncompressed.</li> <li>SHT_Indexed.ang - A file containing orientation data produced by indexing Patterns.up2 using EMSphInx. Orientations are represented by Euler angles (Bunge convention) and are to be interpreted using the EDAX Setting 2 convention (see MTEX documentation at <a href="https://mtex-toolbox.github.io/EBSDReferenceFrame.html">https://mtex-toolbox.github.io/EBSDReferenceFrame.html</a>).</li> <li>SHT_Indexed.h5 - A file in HDF5 format containing orientation data and other relevant information produced by indexing Patterns.up2 using EMSphInx.</li> <li>SHT_Indexed_IPFmap.png - An image of an inverse pole figure map colored with respect to the short transverse direction showing the data from SHT_Indexed.ang.</li> </ul> Note: The basic format of "up2" files is the following. The first 4 bytes provide the version number. The second 4 bytes are the width of the patterns. The third 4 bytes are the height of the patterns. The fourth 4 bytes are the starting position of the pattern image data. <h3>Acknowledgments</h3> The authors gratefully acknowledge support from the National Science Foundation under Grant DMR-2003312 and instrumentation under Grant DMR-9974476.  The authors also gratefully acknowledge support from the U.S. Department of Energy, Office of High Energy Physics under Grant DE-SC0009960.  This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Los Alamos National Laboratory (Contract 89233218CNA000001) and Sandia National Laboratories (Contract DE-NA-0003525).  The authors thank Mr. Thomas Cayia (Arcelor Mittal) for providing the interstitial-free steel material used for this study.

Mass spectrometry dataset for: "Discovery of Nostatin A, an azole containing sactipeptide with prominent cytostatic activity and pro-apoptotic activity"

MSn mass spectrometry dataset used in: Discovery of nostatin A, an azole-containing sactipeptide with prominent cytostatic and pro-apoptotic activity. Kateřina Delawská*, Jan Hájek*, Kateřina Voráčová*, Marek Kuzma, Jan Mare&scaron;, Kateřina Vicková, Alan Kádek, Dominika Tučková, Filip Gallob, Petra Divoká, Martin Moos, Stanislav Opekar, Lukas Koch, Kumar Saurav, David Sedlák, Petr Novák, Petra Urajová, Jason Dean, Radek Gažák, Timo J.H. Niedermeyer, Zdeněk Kameník, Petr &Scaron;imek, Andreas Villunger and Pavel Hrouzek. Org. Biomol. Chem. (2025). doi:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4OB01395F">10.1039/D4OB01395F</a>   Description: MSn mass spectrometry elucidation of the molecular structure of nostatin A, a bioactive peptide isolated from Nostoc sp. cyanobacteria. Sample and data processing: 1) FTICR data: Experiments were performed using a 15T SolariX XR Fourier-transform ion cyclotron resonance mass spectrometer (ESI-FTICR MS; Bruker Daltonics, Billerica, MA, USA) equipped with infrared multiple photon dissociation (IRMPD). All data were acquired using 2 µl/min direct infusion of NosA dissolved at 10 µM in 60% acetonitrile acidified with 0.1% formic acid. Ion fragmentation was performed using IRMPD inside the ICR cell. For this a Diamond C-30A CO2 laser (Coherent, Santa Clara, CA, USA) resonating at 10.6 µm was custom-coupled to the SolariX FTICR MS and laser pulses were precisely timed in synchronization with the ICR pulse sequence. Further MS3 fragmentation experiments were performed using in source collisional fragmentation (isCID) followed by quadrupole isolation and subsequent collision induced fragmentation of particular fragment ions of interest. Detailed parameters (ESI and MS settings) are stored inside the metadata of the individual data files as well as described in the resulting publication. Spectral peaks were also exported in plain m/z vs intensity XY text files from Bruker Data Analysis 5.1. 2) qTOF data: HPLC-HRMS experiment was performed using a Dionex UltiMate 3000 HPLC system (Thermo Scientific, Sunnyvale, CA, USA) coupled with a diode array detector (DAD) connected to the Bruker Impact HD mass spectrometer equipped with an electrospray ionization (ESI) source (ESI-HRMS; Bruker, Billerica, MA, USA). The separations were performed on a C18 column (Phenomenex Kinetex C18, 150 × 4.6 mm, 2.6 μm) eluted with water (A)/acetonitrile (B) gradient (0 min 15%, 1 min 15%, 20 min 100%, 25 min 100%, 30 min 15%, 33min 15% of B) at a constant flow rate of 0.6 mL/min. Both solvents were acidified with 0.1% formic acid. Fragmentation energy was set to 87eV and 35eV for 1+ and 2+ Nostatin A, respectively. Detailed parameters (ESI and MS settings) are stored in the metadata of the individual data file as well as described in the resulting publication. *other variants of Nostatin bearing different acyl chains on proline residue were detected in crude extract only (C33_nosA_LCMS_2217.d) and not purified. Raw Bruker DataAnalysis .d files, which otherwise behave as folders, were compressed with the TAR algorithm implemented in the 64-bit Total Commander 11.02.

Figure 6 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 6. Regression Graph (linear model) for the variable coefficient: Apparent digestibility of crude protein for tambaqui fed diets with different particle size of corn (Ŷ = 72.2 – 1.21.X; (R2 = 52.0%; p= 0.0014)).

Figure 5 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 5. Regression Graph (Quadratic Model) for the variable specific growth rate (TCE) after 68 days of experiment (Ŷ = 6.15 – 0.00279.X + 0.00000191.X2; (R2 = 53.7%; p= 0.0006)).

Figure 2 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 2. Regression Graph (Model Quadratic) for variable weight gain in the 68 days of experiment (Ŷ = 61.3 – 0.0807.X + 0.0000574X2 (R2 = 58.5%; p= 0.0002)).

Figure 4 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 4. Regression Graph (Quadratic Model) for variable Total feed consumption in the 68 days of experiment (Ŷ = 556.6 – 0.513.X + 0.000321.X2; (R2 = 65.6%; p<0.0001)).

Figure 3 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 3. Regression Graph (Cubic Model) for apparent feed conversion variable after 68 days of experiment (Ŷ = 1.27 – 0.00284X + 0.00000783X2 – 0.00000000570X3; (R2 = 58.,1%; p= 0.0007)).

Figure 1 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 1. Regression Graph (Model Quadratic) for variable weight final after 68 days of experiment (Ŷ = 72.3 – 0.809.X + 0.0000576X2; (R2 = 58.5%; p= 0.0002)).

IR data of the compounds published in "Synthesis and Reactivity of Molybdenum and Tungsten Alkyne Complexes Containing 6-Methylpyridine-2-thiolate Ligands"

Here, the uploaded data are associated with the manuscript "Synthesis and Reactivity of Molybdenum and Tungsten Alkyne Complexes Containing 6-Methylpyridine-2-thiolate Ligands," published in Helvetica Chimica Acta under the following DOI: https://doi.org/10.1002/hlca.202100137 The .dpt files represent IR spectra of the compounds published in the manuscript. The labeling used consists of two parts, e.g., 1b – ME162, where 1b represents the label of the compound as presented in the manuscript, and ME162 represents the crystallographic label found in the supplementary information (SI).

opencc-by-4.0Dec 2023View details →

Figure 1 in Evaluation of Plastic Shipping Bags for Hypothenemus hampei (Ferrari) (Coleoptera: Curculionidae) Containment

Figure 1. Diagram of Hypothenemus hampei (Ferrari) shipping bag observation arenas used in this experiment. Arenas were constructed of lidded 3.78 L (1-gal) glass jars with drying treated coffee in one of nine bag treatments suspended from the top by a hot-glued string tether to allow escaping (left) or not escaping (right) H. hampei observation in the bottom of the jar.

Figure 7 in From Eradication to Containment: Invasion of French Polynesia by Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) and Releases of Two Natural Enemies: A 17-Year Case Study

Figure 7. Annual percentage of individual guavas infested with fruit flies on Tahiti. Number of fruits incubated individually each year were: 172 in 2002, 348 in 2003, 539 in 2004, 607 in 2005, 98 in 2006, 4 in 2007, 237 in 2008, and 807 in 2009.

Figure 6a–d in From Eradication to Containment: Invasion of French Polynesia by Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) and Releases of Two Natural Enemies: A 17-Year Case Study

Figure 6a–d. Quarterly emergences on Tahiti of B. dorsalis and F. arisanus per kg fruit for guava (a), Tahitian chestnut (b), tropical almond (c), and mango (d). See under

Figure 4 in From Eradication to Containment: Invasion of French Polynesia by Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) and Releases of Two Natural Enemies: A 17-Year Case Study

Figure 4. Annual proportion of fruit fly (B. dorsalis, B. tryoni, B. kirki) and parasitoid (F. arisanus, D. longicaudata) emergences in guava, tropical almond, Tahitian chestnut, and mango fruits for selected years.

Figure 3a, b in From Eradication to Containment: Invasion of French Polynesia by Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) and Releases of Two Natural Enemies: A 17-Year Case Study

Figure 3a, b. Coconut husk block (a) and BactroMAT-ME (b) bait stations used for eradication of B. dorsalis. (Photos: L. Leblanc).

Figure 1 in From Eradication to Containment: Invasion of French Polynesia by Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) and Releases of Two Natural Enemies: A 17-Year Case Study

Figure 1. Monthly captures of B. dorsalis in methyl eugenol traps and quarterly percent parasitism on guava, Tahitian chestnut and tropical almond on Tahiti.

Figure 5a–d in From Eradication to Containment: Invasion of French Polynesia by Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) and Releases of Two Natural Enemies: A 17-Year Case Study

Figure 5a–d. Quarterly emergences on Tahiti of B. dorsalis and F. arisanus per fruit for guava (a), Tahitian chestnut (b), tropical almond (c), and mango (d). Numbers of fruits used for each host and each year (for guava, Tahitian chestnut, tropical almond and mango, respectively) were: 1998: 1634, 16238, 5314, 67; 1999: 264, 304, 993, 404; 2000: 37, 40, 154, 64; 2001: 52, 0, 20, 74; 2002: 492, 1204, 474, 268; 2003: 1531, 1539, 2685, 977; 2004: 2252, 1324, 810, 291; 2005: 1071, 904, 4373, 436; 2006: 1927, 3343, 3140, 1044; 2007: 1537, 1525, 4200, 1814; 2008: 3255, 2648, 5045, 2052; 2009: 1515, 1972, 5475, 549.

opencc-by-4.0Dec 2020View details →

Fig. 2. SNSB-BSPG 2020 XCIII 18 containing a in A new glimpse on trophic interactions of 100-million-year old lacewing larvae

Fig. 2. SNSB-BSPG 2020 XCIII 18 containing a neuropteran larva with attached mite from Hukawng Valley, Kachin State, Myanmar; Turonian– Cenomanian, Cretaceous, 90–100 mya; in dorsal (A1) and ventral (A2) views.

Solutions for Reproducibility in Empirical Research: Virtual Machines, Containers, Environment Management Packages, and Cloud Platforms

This image provides a comprehensive overview of various technologies and platforms used to enhance the reproducibility of empirical research. It is divided into several sections: <ol> <li>Virtual Machines (VMs): the left section of the image illustrates the architecture of VMs with Type 1 and Type 2 hypervisors.     - Type 1 Hypervisor runs directly on the hardware, providing high efficiency and performance. Examples include VMware ESXi, Microsoft Hyper-v, and Xen Project.    - Type 2 Hypervisor runs on an existing operating system, offering flexibility at the cost of some performance. Examples include Oracle VirtualBox, VMware Workstation, and Parallels.</li> <li>Containers: the middle section of the image explains the containerization concept, which shares the host operating system's kernel, making containers more lightweight than VMs. Technologies like Docker and Kubernetes are shown as popular solutions for container orchestration.</li> <li>Environment Management Packages: the top right section focuses on tools for managing software dependencies and environments. renv (for R) and Conda (for Python and other languages) are highlighted as key tools for creating reproducible research environments.</li> <li>Cloud Platforms: the bottom right section features various cloud-based platforms that facilitate reproducible research by providing scalable and shareable computational environments. Platforms include Google Colab, Posit Cloud, JupyterHub, Binder, Nextjournal, OpenShift, and Code Ocean.</li> </ol> Together, these solutions provide a robust framework for ensuring that empirical research can be reliably reproduced and validated by others, addressing the challenges of dependency management, environment consistency, and computational resource availability.