cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18739/a2sq8qk2v
title: Observations from Autonomous Ocean Flux Buoy 46 deployed at site CO during the MOSAiC transpolar drift, Arctic Basin, 2019-2020

description: During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, four Autonomous Ocean Flux Buoys (AOFB) were set up on ice floes on the Siberian side of the Arctic Basin during October 2019 from the Academic Federov ice breaker. AOFB46 was located adjacent to the meteorology tower at the Central Observatory (CO) which was manned from the AWI (Alfred Wegener Institute) Polarstern ice breaker moored to the ice floe. AOFB43, 44, and 45 were deployed with other instrument systems at three remote sites – L1, L2 and L3, nominally 20 kilometers (km) out from the CO. The ice pack supporting the observation systems drifted across the Central Arctic toward Spitzbergen during the following 9 months.
 Data in this package from AOFB 46 is contained in a netcdf file with measurements of ocean variables that have been averaged onto 2-hour-long time intervals, the sample rate used for the ocean turbulence measurements. These timeseries are
 1. The decimal yearday (yd) of 2019 of the middle of each 2 hour sample interval.
 2. The GPS (Global positioning system)-derived latitude (lat) and longitude (lon) sampled at the center of each sample interval.
 3. Eastward and Northward absolute ocean velocity profiles including ice velocity derived from the AOFB position, 5 meter (m) currents measured by the turbulence sensor package, and Acoustic Doppler Current Profiler (ADCP) time series of earth-referenced currents from 10 m to 80 m depth sampled every 2 m. Exact measurement depths are contained in the depth vector variable z.
 4. Reynolds stress components and , heat flux , and salt flux , measured by the turbulence sensor nominally at 5 m depth from 2 Hertz (Hz) sampled 35 minute ensembles of high resolution velocity, temperature and conductivity timeseries. Here ρ is the in situ seawater density and cp is the specific heat of seawater.
DOI: 10.5281/zenodo.10611666
title: Reduced Gravity Experiment Data for Scour Hole Formation Including Raw Videos

description: Experiment videos taken in reduced gravity flights onboard a reduced gravity aircraft plus tabular data extracted from these videos. The 250 to 330 micron (coarse) sand experiments were from flights in April 2008. The JSC-Mars-1A mars soil simulant experiments were from flights in October 2008. The experiments are describe in:

Metzger, Philip T., Robert C. Latta III, Jason M. Schuler, and Christopher D. Immer. "Craters formed in granular beds by impinging jets of gas." In AIP Conference Proceedings, vol. 1145, no. 1, pp. 767-770. American Institute of Physics, 2009. (preprint: https://arxiv.org/pdf/0905.4851.pdf)
DOI: 10.5281/zenodo.10611667
title: Reduced Gravity Experiment Data for Scour Hole Formation Including Raw Videos

description: Experiment videos taken in reduced gravity flights onboard a reduced gravity aircraft plus tabular data extracted from these videos. The 250 to 330 micron (coarse) sand experiments were from flights in April 2008. The JSC-Mars-1A mars soil simulant experiments were from flights in October 2008. The experiments are describe in:

Metzger, Philip T., Robert C. Latta III, Jason M. Schuler, and Christopher D. Immer. "Craters formed in granular beds by impinging jets of gas." In AIP Conference Proceedings, vol. 1145, no. 1, pp. 767-770. American Institute of Physics, 2009. (preprint: https://arxiv.org/pdf/0905.4851.pdf)
DOI: 10.5517/ccdc.csd.cc2ftkf8
title: CCDC 2258890: Experimental Crystal Structure Determination

description: Related Article: Haley M. Carlson, Sydney R. Smith, R. Adam Mosey|2024|J.Org.Chem.|89|1160|doi:10.1021/acs.joc.3c02334
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fw8tf
title: CCDC 2260545: Experimental Crystal Structure Determination

description: Related Article: Jiajia Chen, Minghui Wang, Xindi Yang, Mengyao Yuan, Haiwei Liu, Pengfei Cheng, Kuoxi Xu|2024|Sens.Actuators,B|405|135313|doi:10.1016/j.snb.2024.135313
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fw8vg
title: CCDC 2260546: Experimental Crystal Structure Determination

description: Related Article: Jiajia Chen, Minghui Wang, Xindi Yang, Mengyao Yuan, Haiwei Liu, Pengfei Cheng, Kuoxi Xu|2024|Sens.Actuators,B|405|135313|doi:10.1016/j.snb.2024.135313
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fpvww
title: CCDC 2255339: Experimental Crystal Structure Determination

description: Related Article: Jiajia Chen, Minghui Wang, Xindi Yang, Mengyao Yuan, Haiwei Liu, Pengfei Cheng, Kuoxi Xu|2024|Sens.Actuators,B|405|135313|doi:10.1016/j.snb.2024.135313
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g2681
title: CCDC 2266232: Experimental Crystal Structure Determination

description: Related Article: Jiajia Chen, Minghui Wang, Xindi Yang, Mengyao Yuan, Haiwei Liu, Pengfei Cheng, Kuoxi Xu|2024|Sens.Actuators,B|405|135313|doi:10.1016/j.snb.2024.135313
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25345/c59g5gq9k
title: MassIVE MSV000093992 - XLMS Analysis of the DSSO-crosslinked proteins affinity-purified from HEK293T cells stably expressing Halo-FAM60A
title: Protein crosslinking | Briefly, cell lysates expressing Halo-Fam60A were mixed with Magne HaloTag Beads, using 100 ul of bead slurry, and incubated overnight at 4C. The beads were then separated with a magnetic concentrator and washed four times using a buffer composed of 10 mM HEPES (pH 7.5), 1.5 mM MgCl2, 0.3 M NaCl, 10 mM KCl, and 0.2% Triton X-100. To elute bound proteins, the beads were treated with 200 ul of a buffer containing 50 mM HEPES (pH 7.5), 0.5 mM EDTA, 1 mM DTT, and 30 units of AcTEV, and incubated for a minimum of 2 h at 4C. The eluate was collected, and proteins were cross-linked using 5 mM disuccinimidylsulfoxide (DSSO; Sigma-Aldrich) for 40 minutes at room temperature. The cross-linking process was quenched by adding 50 mM NH4HCO3 and incubating for 15 minutes at room temperature. LC-MS3 XLMS data acquisition | Crosslinked peptides were analyzed on an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific) coupled to a Dionex UltiMate 3000 RSCLnano System. Peptides were loaded on an Acclaim PepMap 100 C18 0.3-mm-i.d., 5-mm-long trap cartridge (Thermo Fisher Scientific), then eluted to an analytical column (50 um i.d., 150 mm long) packed in-house with ReproSil-Pur C18-AQ 1.9-um resin (Dr. Masch GmbH). Full MS scans were performed at 60,000-m/z resolution in the orbitrap with a 1.6-m/z isolation window and a scan range from 375 to 1,500 m/z. The top three peptides with charge states from +4 to +8 were selected for MS2 fragmentation with 20% CID energy. MS2 scans were detected in the Orbitrap with 30,000-m/z resolution and a dynamic exclusion time of 40 s. Among MS2 fragments, if two peptides with an exact mass difference of 31.9720 with 20 ppm mass tolerance were present, both were selected for MS3 fragmentation at CID energy of 35%. MS3 scans were performed in the ion trap at rapid scan with an isolation window of 3 m/z and a maximum ion injection time of 200 ms. Each MS2 scan was followed by four MS3 scans. XLMS data analysis | The XlinkX node of Proteome Discoverer 3.0 (Thermo Fisher Scientific) was used to search for crosslinked peptides in the XLMS dataset by identifying MS2 scans with reporter ions characteristic of DSSO-modified lysine residues, with mass shifts of 158.00376 Da (monoisotopic mass), 54.01056 Da (alkene fragment), and 85.98264 Da (thiol fragment). In addition, the Sequest HT node searched the dataset with the following settings: 10 ppm precursor ion mass tolerance; 0.6 Da fragment ion mass tolerance; fixed modification: Cys carbamidomethylation; variable modifications: Met oxidation, N-terminal acetylation, Lys with amidated DSSO (279.07766 Da), and Lys with hydrolyzed DSSO (176.01433 Da); 3 maximum number of dynamic modifications. The data was searched against a non-redundant protein sequence database assembled from NCBI RefSeq GRCh38.p14 and T2T-CHM13v2.0 (2022-04-12 release), complemented with contaminants. The protein false discovery rate was set at 0.01.
DOI: 10.25390/caryinstitute.11801052.v62
title: Cary Institute Environmental Monitoring Program Current Year Data 2023

description: The Cary Institute of Ecosystem Studies Environmental Monitoring Program is a long-term data collection program designed to understand how the environment changes over time. The program includes monitoring of climate including temperature and precipitation, as well as variables related to air pollution, such as acid deposition and ozone, and water pollution and other streamwater chemistry. Our solar radiation monitoring includes diffuse and global photosynthetically active radiation (PAR), diffuse and global shortwave radiation, net radiation and UV. Long-term monitoring of solar radiation provides us with an understanding of atmospheric energy dynamics, which can affect natural and human systems. The Cary Institute of Ecosystem Studies, Environmental Monitoring Program furnishes data under the following conditions: The data have received quality assurance scrutiny by our program, and, although we are confident of the accuracy of these data, Cary Institute will not be held liable for errors in these data. Data are subject to change resulting from updates in data screening or models used. Data citation: please click on the Cite button in the Figshare repository.<br>
Those wishing to publish data from Cary Institute of Ecosystem Studies, Environmental Monitoring Program are encouraged to contact data manager Vicky Kelly, kellyv@caryinstitute.org.<br>
For complete data for previous years and for complete metadata, please see appropriate items for the Cary Institute Environmental Monitoring Program at https://caryinstitute.figshare.com/
DOI: 10.34740/kaggle/dsv/7541783
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.6084/m9.figshare.25134665.v1
title: DeBonville_ConPhys2024.csv

description: This project contains data and metadata for the publication "Thermal tolerance and survival are modulated by a natural gradient of infection in differentially acclimated hosts" submitted in <i>Conservation Physiology</i> (CONPHYS-2023-135).Fish were collected in 2020 (fall) and 2021 (summer and fall), acclimated to a range of ecologically relevant temperatures (), and submitted to thermal tolerance trials (CTmax + CTmin). Survival was assessed for a week for the summer 2021 fish. We then related all measured metrics to the number of parasite species found in the fish to assess the influence of infection on thermal tolerance of their hosts. The dataset also includes measures of fish mass (corrected for parasites) and mass of multiple internal organs.
DOI: 10.6084/m9.figshare.21514116.v3
title: Pulmonary micrometastasis dataset

description: An ensemble of PhysiCell simulations of a multiscale model for pulmonary micrometastasis. Computational model and data analysis are available at https://github.com/heberlr/melanoma.
DOI: 10.34740/kaggle/dsv/7541792
title: Top Spotify Songs in 73 Countries (Daily Updated)
DOI: 10.6084/m9.figshare.21861948.v2
title: Data for "Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models"

description: Datasets for the paper entitled "Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models" by Maciej P. Polak, Shrey Modi, Anna Latosinska, Jinming Zhang, Jasmine Wang, Shanonan Wang, Ayan Deep Hazra, and Dane Morgan<br>

<br>
MPPolak_BulkModulus_ValidationData.xlsx - a dataset of bulk modulus sentences, positive - containing bulk modulus data, and negative - not contaning data, used for model assessment.
<br>
MPPolak_BulkModulus_AllTrainData.xlsx - a dataset of bulk modulus sentences, positive - containing bulk modulus data, and negative - not contaning data, used for fine tuning of the model and model assessment.
<br>
MPPolak_CritCoolRate_Dataset.xlsx - a dataset of critical cooling rates for metallic glasses developed in this paper with the ,ethod presented in the paper, consisting of names of materials, values of critical cooling rates, their units, and DOIs of the source documents.
DOI: 10.6073/pasta/eff37c93bc889bbfc3fd609748c48369
title: Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Snow and Frost

description: This dataset contains snow depth and frost depth measurements from the Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest. Samples are collected weekly throughout the winter months. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
DOI: 10.5281/zenodo.10055763
title: Dataset for Insect Detection Remote Sensing

description: # MSU Horticulture farm beehive dataset

 

Dataset associated with journal submission entitled "Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data" by authors T. C. Vannoy, N. B. Sweeney, J. A. Shaw, and B. M. Whitaker.

The associated software is archived at https://zenodo.org/doi/10.5281/zenodo.10055809.

 

 

The data  were collected in June and July of 2022 at the horticulture farm at Montana State University - Bozeman.  The data consists of 9977 images taken in front of the beehives.

 

For the data collection process, the lidar was mounted in the back of a U-haul van and pointed in front of the beehives. The lidar was then run at a variety of pan and tilt angles. This created a diverse set of images with varying levels of activity depending on how far the beam was from the beehives, along with some of the sets of images containing stationary targets where the beam was hitting a beehive or plant in the distance.

 

## Organization

At the top-level, data are split by the collection date. The next level down, the folders correspond to individual data collection runs; the timestamp at the end of the folder names indicates when the data collection started.

 

Each top-level date folder contains a README file that describes the individual data collection runs.

 

Each data collection folder contains the following files:

- `adjusted_data_junecal_volts.mat`: The main data file, which contains all the data and metadata.

- `<image-number>_P<pan-angle>_T<tilt-angle>.mat`: The individual images; these files contain the raw data. These are all combined into `adjusted_data_junecal_volts.mat`, so these files are rarely used.

- `labels.csv`: The class labels for all data in the folder

- `labels.mat`: The class labels converted into label vectors, which are used for machine learning.

 

 

## Class labels

After collecting the data, we manually labelled the bounding box of each insect in each image, then converted the bounding boxes into binary labels that indicate whether a row contains an insect. Each potential insect was labeled with a confidence rating because some bees were more obvious than others. During the labeling process, we found 4671 probable bees. Since we were not able to collect ground-truth data in the field, it is possible that our labels are imperfect: some insects might have been missed, and some non-insects might have been labeled as insects.

 

There is a `labels.csv` file in the root directory, which contains all the labels. Each of the data-collection subdirectories has a `labels.csv` and `labels.mat` file that contain the labels for that run.

 

### Class imbalance

Of the 9977 images, 3498 (35.14\%) contain one or more bees. In total, the dataset has 1775906 rows, 11492 (0.647\%) of which contain an insect measurement. Due to sampling jitter in the ADC, most insects span multiple range bins, leading to an increase in the number of rows that were labeled as containing insects.  The dataset has a large class imbalance, particularly when looking at how many rows contain insects.
DOI: 10.5281/zenodo.10055762
title: Dataset for Insect Detection Remote Sensing

description: # MSU Horticulture farm beehive dataset

 

Dataset associated with journal submission entitled "Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data" by authors T. C. Vannoy, N. B. Sweeney, J. A. Shaw, and B. M. Whitaker.

The associated software is archived at https://zenodo.org/doi/10.5281/zenodo.10055809.

 

 

The data  were collected in June and July of 2022 at the horticulture farm at Montana State University - Bozeman.  The data consists of 9977 images taken in front of the beehives.

 

For the data collection process, the lidar was mounted in the back of a U-haul van and pointed in front of the beehives. The lidar was then run at a variety of pan and tilt angles. This created a diverse set of images with varying levels of activity depending on how far the beam was from the beehives, along with some of the sets of images containing stationary targets where the beam was hitting a beehive or plant in the distance.

 

## Organization

At the top-level, data are split by the collection date. The next level down, the folders correspond to individual data collection runs; the timestamp at the end of the folder names indicates when the data collection started.

 

Each top-level date folder contains a README file that describes the individual data collection runs.

 

Each data collection folder contains the following files:

- `adjusted_data_junecal_volts.mat`: The main data file, which contains all the data and metadata.

- `<image-number>_P<pan-angle>_T<tilt-angle>.mat`: The individual images; these files contain the raw data. These are all combined into `adjusted_data_junecal_volts.mat`, so these files are rarely used.

- `labels.csv`: The class labels for all data in the folder

- `labels.mat`: The class labels converted into label vectors, which are used for machine learning.

 

 

## Class labels

After collecting the data, we manually labelled the bounding box of each insect in each image, then converted the bounding boxes into binary labels that indicate whether a row contains an insect. Each potential insect was labeled with a confidence rating because some bees were more obvious than others. During the labeling process, we found 4671 probable bees. Since we were not able to collect ground-truth data in the field, it is possible that our labels are imperfect: some insects might have been missed, and some non-insects might have been labeled as insects.

 

There is a `labels.csv` file in the root directory, which contains all the labels. Each of the data-collection subdirectories has a `labels.csv` and `labels.mat` file that contain the labels for that run.

 

### Class imbalance

Of the 9977 images, 3498 (35.14\%) contain one or more bees. In total, the dataset has 1775906 rows, 11492 (0.647\%) of which contain an insect measurement. Due to sampling jitter in the ADC, most insects span multiple range bins, leading to an increase in the number of rows that were labeled as containing insects.  The dataset has a large class imbalance, particularly when looking at how many rows contain insects.
DOI: 10.34740/kaggle/dsv/7541823
title: Full TMDB Movies Dataset 2024 (985K Movies)
DOI: 10.25345/c55q4rx6x
title: MassIVE MSV000093993 - GNPS Silybum marianum callus seed
title: Samples of analysis of callus and seeds of Silybum marianum extracts by LC-Ms/MS
DOI: 10.34740/kaggle/dsv/7541874
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.48577/jpl.ildrvn
title: Detectability of Unresolved Particles in Off-Axis Digital Holographic Microscopy

description: Off-axis digital holographic microscopy (DHM) provides both amplitude and phase images, and so may be used for label-free 3D tracking of micro- and nano-sized particles of different compositions, including biological cells, strongly absorbing particles, and strongly scattering particles. Contrast is provided by differences in either the real or imaginary parts of the refractive index (phase contrast and absorption) and/or by scattering. While numerous studies have focused on phase contrast and improving resolution in DHM, particularly axial resolution, absent have been studies quantifying the limits of detection for unresolved particles. This limit has important implications for microbial detection, including in life-detection missions for space flight. Here we examine the limits of detection of nanosized particles as a function of particle optical properties, microscope optics (including camera well depth and substrate), and data processing techniques and find that DHM provides contrast in both amplitude and phase for unresolved spheres, in rough agreement with Mie theory scattering cross-sections. Amplitude reconstructions are more useful than phase for low-index spheres and should not be neglected in DHM analysis.