cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25004084
title: 3300028269_6.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004084.v1
title: 3300028269_6.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004096
title: 3300024278_10.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004096.v1
title: 3300024278_10.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004126
title: 3300024311_10.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004126.v1
title: 3300024311_10.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004372
title: 3300027157_10.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004402
title: Erken-D6-bin-0908.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004447.v1
title: 3300027224_8.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004195
title: 3300014801_6.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004195.v1
title: 3300014801_6.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004216
title: 3300027205_5.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004216.v1
title: 3300027205_5.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004372.v1
title: 3300027157_10.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004402.v1
title: Erken-D6-bin-0908.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004447
title: 3300027224_8.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004057.v1
title: LBMAG.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004249
title: ErkenSummer-bin-3811.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004249.v1
title: ErkenSummer-bin-3811.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004033
title: LH-05nov19-151.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004033.v1
title: LH-05nov19-151.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004045
title: ZH-13nov19-169.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004045.v1
title: ZH-13nov19-169.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004048
title: ZE-LR-56.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004048.v1
title: ZE-LR-56.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004057
title: LBMAG.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003844
title: MaH-09apr19-262.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003844.v1
title: MaH-09apr19-262.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003847
title: MsH-01oct19-328.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003847.v1
title: MsH-01oct19-328.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003850
title: MsH-30apr19-148.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003874
title: TrE-25oct19-138.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003850.v1
title: MsH-30apr19-148.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003856
title: MoE-23oct19-149.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003856.v1
title: MoE-23oct19-149.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003874.v1
title: TrE-25oct19-138.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003859
title: MsH-30jul19-138.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003859.v1
title: MsH-30jul19-138.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003862
title: ZE-LR-8 blastp results.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003862.v1
title: ZE-LR-8 blastp results.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003880
title: TH-11nov19-25.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003880.v1
title: TH-11nov19-25.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003889
title: AH-24oct19-286.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003889.v1
title: AH-24oct19-286.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003913
title: AE-03may19-91.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003913.v1
title: AE-03may19-91.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003937
title: TH-08apr19-142.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003937.v1
title: TH-08apr19-142.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003964
title: LH-02apr19-149.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003964.v1
title: LH-02apr19-149.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003994
title: LE-02apr19-86.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25003994.v1
title: LE-02apr19-86.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004009
title: MaE-09apr19-183.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004009.v1
title: MaE-09apr19-183.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004024
title: ZH-03apr19-259.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004024.v1
title: ZH-03apr19-259.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004714
title: 20240117_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.25004714.v1
title: 20240117_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.25004708
title: Motivation Research Using Labeling Functions

description: Motivation is an important factor in software development.However, it is a subjective concept that is hard to quantify and study empirically.Therefore, it seems that the wealth of data available about real software development projects in repositories such as GitHub cannot be used to study motivation.We present a new methodology to overcome this difficulty, based on the use of labeling functions.A labeling function is a validated heuristic that need only be better than a guess, computable on a dataset.We define four labeling functions for motivation, for example working in diverse hours of the day, and show that they indeed correlate with motivation.We then apply them to more than 150 thousand developers working on GitHub projects.This enables us to characterize and compare the behaviors of developers who are motivated or less so.The results indicate that the effect of motivation is indeed large, and allow us to build a model to predict developer retention in a project.<br>
DOI: 10.6084/m9.figshare.25004708.v1
title: Motivation Research Using Labeling Functions

description: Motivation is an important factor in software development.However, it is a subjective concept that is hard to quantify and study empirically.Therefore, it seems that the wealth of data available about real software development projects in repositories such as GitHub cannot be used to study motivation.We present a new methodology to overcome this difficulty, based on the use of labeling functions.A labeling function is a validated heuristic that need only be better than a guess, computable on a dataset.We define four labeling functions for motivation, for example working in diverse hours of the day, and show that they indeed correlate with motivation.We then apply them to more than 150 thousand developers working on GitHub projects.This enables us to characterize and compare the behaviors of developers who are motivated or less so.The results indicate that the effect of motivation is indeed large, and allow us to build a model to predict developer retention in a project.<br>
DOI: 10.6084/m9.figshare.25006274
title: BLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25006274.v1
title: BLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.18743/data.00000329
title: Supplementary Data for G Driver JGR Planets paper "Large area Glacier-Like Forms on Mars, Insights from Crater Morphologies and Crater Retention Ages"

description: Dataset contains a number of spreadsheets of statistical data for the paper along with files applicable to the CraterStats program for surface age dating.
description: Data collected via mapping of Glacier-Like Forms using ArcGIS Pro 3.0. Crater retention ages created from data collected in Arc and processed via CraterStats 2.0
DOI: 10.21227/crsb-ka71
title: Aeneas' cryptographic disc (4th c. B.C.)

description: This dataset contains a total of 4671 submissions, with 12% correct solutions and 24% partial solutions. Link to the original challenge: https://csacademy.com/ieeextreme-practice/task/d48ada9a7213299f1b24b22b2fb9443f/ See the included PDF document for details about the original challenge.
DOI: 10.6084/m9.figshare.24925957
title: Clustering High-Dimensional Noisy Categorical Data

description: Clustering is a widely used unsupervised learning technique that groups data into homogeneous clusters. However, when dealing with real-world data that contain categorical values, existing algorithms can be computationally costly in high dimensions and can struggle with noisy data that has missing values. Furthermore, except for one algorithm, no others provide theoretical guarantees of clustering accuracy. In this article, we propose a general categorical data encoding method and a computationally efficient spectral-based algorithm to cluster high-dimensional noisy categorical data (nominal or ordinal). Under a statistical model for data on <i>m</i> attributes from <i>n</i> subjects in <i>r</i> clusters with missing probability <i>ϵ</i>, we show that our algorithm exactly recovers the true clusters with high probability when mn(1−ϵ)≥CMr2 log 3M, with M=max(n,m) and a fixed constant <i>C</i>. In addition, we show that mn(1−ϵ)2≥rδ/2 with 0&lt;δ&lt;1 is necessary for <i>any</i> algorithm to succeed with probability at least (1+δ)/2. In cases where <i>m</i> = <i>n</i> and <i>r</i> are fixed, the sufficient condition matches with the necessary condition up to a polylog(n) factor. In numerical studies our algorithm outperforms several existing algorithms in both clustering accuracy and computational efficiency. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24925957.v1
title: Clustering High-Dimensional Noisy Categorical Data

description: Clustering is a widely used unsupervised learning technique that groups data into homogeneous clusters. However, when dealing with real-world data that contain categorical values, existing algorithms can be computationally costly in high dimensions and can struggle with noisy data that has missing values. Furthermore, except for one algorithm, no others provide theoretical guarantees of clustering accuracy. In this article, we propose a general categorical data encoding method and a computationally efficient spectral-based algorithm to cluster high-dimensional noisy categorical data (nominal or ordinal). Under a statistical model for data on <i>m</i> attributes from <i>n</i> subjects in <i>r</i> clusters with missing probability <i>ϵ</i>, we show that our algorithm exactly recovers the true clusters with high probability when mn(1−ϵ)≥CMr2 log 3M, with M=max(n,m) and a fixed constant <i>C</i>. In addition, we show that mn(1−ϵ)2≥rδ/2 with 0&lt;δ&lt;1 is necessary for <i>any</i> algorithm to succeed with probability at least (1+δ)/2. In cases where <i>m</i> = <i>n</i> and <i>r</i> are fixed, the sufficient condition matches with the necessary condition up to a polylog(n) factor. In numerical studies our algorithm outperforms several existing algorithms in both clustering accuracy and computational efficiency. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.7152656
title: Effects of Body Shadowing in LoRa Localization systems

description: The purpose of this database of measurements with and without body obstruction is to provide the scientific community with a tool to evaluate different localisation methods in these scenarios. The measurements were performed in an outdoor environment with a semi-dense and dense vegetation, located at the Universidad del Norte in Colombia. The data presents the RSSI values transmitted by a LoRa WisTrio RAK5205 node and received by four Risinghf LoRaWAN gateways composing the network, operating at 915 MHz. Additionally, the node and gateways locations in geographic coordinates are provided. Measurements were made by placing the node on a tripod for each test position (scenario without human shadowing - NHS) and then placing the node on a person's chest (scenario with human shadowing - HS) for the same positions.


The database is composed of three groups:
- Raw data:
    RSSI_NHS_raw.csv: Containing 50 packets per position in No Human Shadowing scenario.
    RSSI_HS_raw.csv: Containing 50 packets per position with reception at 3 or 4 gateways, in Human Shadowing scenario.
- Cleaned data: Only for the 16 node positions where the messages were received at the four gateways, after outlier removal (See https://www.mathworks.com/matlabcentral/fileexchange/62954-mean_removing_outliers_tukey-x-rmzerovals?s_tid=mwa_osa_a).
    NHS_16ps.csv: Containing 40 packets per position in No Human Shadowing scenario.
    HS4GWs_16ps.csv: Containing 40 packets per position in Human Shadowing scenario.


Additionally, the geographical coordinates for the gateways are provided in the PosGWs.csv file.


The methodology for data collection and the first results will be published in a research journal.
DOI: 10.5281/zenodo.7152657
title: Effects of Body Shadowing in LoRa Localization systems

description: The purpose of this database of measurements with and without body obstruction is to provide the scientific community with a tool to evaluate different localisation methods in these scenarios. The measurements were performed in an outdoor environment with a semi-dense and dense vegetation, located at the Universidad del Norte in Colombia. The data presents the RSSI values transmitted by a LoRa WisTrio RAK5205 node and received by four Risinghf LoRaWAN gateways composing the network, operating at 915 MHz. Additionally, the node and gateways locations in geographic coordinates are provided. Measurements were made by placing the node on a tripod for each test position (scenario without human shadowing - NHS) and then placing the node on a person's chest (scenario with human shadowing - HS) for the same positions.


The database is composed of three groups:
- Raw data:
    RSSI_NHS_raw.csv: Containing 50 packets per position in No Human Shadowing scenario.
    RSSI_HS_raw.csv: Containing 50 packets per position with reception at 3 or 4 gateways, in Human Shadowing scenario.
- Cleaned data: Only for the 16 node positions where the messages were received at the four gateways, after outlier removal (See https://www.mathworks.com/matlabcentral/fileexchange/62954-mean_removing_outliers_tukey-x-rmzerovals?s_tid=mwa_osa_a).
    NHS_16ps.csv: Containing 40 packets per position in No Human Shadowing scenario.
    HS4GWs_16ps.csv: Containing 40 packets per position in Human Shadowing scenario.


Additionally, the geographical coordinates for the gateways are provided in the PosGWs.csv file.


The methodology for data collection and the first results will be published in a research journal.
DOI: 10.6073/pasta/7cb52c298cc15c4c19e751baedb02422
title: Meteorological data from El Verde Field Station: NADP Tower

description: Meteorological sensors are located at the top of a 20 m tower, the NADP Tower, behind the main buildings of El Verde Field Station, 350 masl. No large trees are present near the tower. Sensors are connected to a Campbell 1000X data logger, with a storage module, and downloaded every two weeks using a wireless radio connection from the laboratory to the tower. Data are compiled at the station before it is made available. The station was initiated in 2000. Complementary manual measurements of rainfall and maximum and minimum air temperatures are taken on a daily basis at a separate physical location.
 
 
 Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
DOI: 10.17632/tv5xr9s7xs.1
title: Supplemental Material - Matching-adjusted indirect comparison of guselkumab versus risankizumab in patients with moderate-to-severe plaque psoriasis: Change in baseline PASI from week 4-40

description: This is supplemental material for the manuscript: Matching-adjusted indirect comparison of guselkumab versus risankizumab in patients with moderate-to-severe plaque psoriasis: Change in baseline PASI from week 4-40
DOI: 10.5281/zenodo.10475718
title: Seagrass Carbon Assimilation and Desiccation Rates and Chlorophyll Fluorescence Parameters
DOI: 10.5281/zenodo.10475717
title: Seagrass Carbon Assimilation and Desiccation Rates and Chlorophyll Fluorescence Parameters

description: This dataset accompanies the publication "Infrared gas analysis as a method of measuring seagrass photosynthetic rate in the face of desiccation stress" by Capistrant-Fossa & Dunton (2024). Contained in here are both raw from instruments (LI-6400 XT and WALZ Diving PAM) and data reformatted for analysis. 

 

Experiment 1: Carbon Assimilation Rates of Desiccating Seagrass Blades in LI-6400 Chambers



Modified has data in analyzable format from the LI-6400 XT instrument. "Controls" refer to data collected from dead seagrasses (see manuscript for details). All columns are standard outputs from the LI-6400 instrument, see manual and  https://www.licor.com/env/support/LI-6400/topics/guided-tour-2-new-measurements.html for standard defintions of columns. 


Experiment 2: Fv/Fm of Desicacting Seagrass Blades



Data in analyzable format from a WALZ Diving PAM. Maximum potential quantum efficiency of Photosystem II (Fv/Fm) collected every 15 min for desiccating seagrass blades (see manuscript for details).


Experiment 3: Rapid Light Curves of Desiccating Seagrass Blades



Modified has data in analyzable format from both a WALZ Diving PAM and LI-6400 XT. The LI-6400 XT shown an example light curve using the instrument and Thalassia testudinum, while the light curves generated by the WALZ diving PAM were before and after an hour of drying (see manuscript for details). All columns of the PAM files are standard photosynethic parameters (Species Tested, Trial #, Before or After Desiccation Stress, Flouresence, Maximum Flouresence, Photosynthetically Active Radiation, Effective Quantum Yield of Photosystem II, Apparent Electron Transport Rate, and Maximum Potential Quantum Efficiency of Photosystem II). 


Experiment 4: Alleviation of Desiccation Stress (Not Shown in Manuscript)

airport_data.csv: Environmental data from the local Mustang Beach airport (KRAS) used in a water evaporation model. All values are recorded in customary units.

con_mod.rda: General additive model describing the loss of water over time for control "dead" seagrass blades.

Experiment1.csv: Mass (g), area (cm2), and quality assurance data LI-6400 measurements from Experiment 1.

Experiment2.csv: Mass (g) data from Experiment 2.

Experiment3.csv: Area data (cm2) from Experiment 2.

samp_wl.rda: General additive model describing the loss of water over time for live "experimental" seagrass blades.

tidal_data.csv: Predicted tides for Port Aransas used in a water evaporation model. All values are referenced to the MLLW datum (m).
DOI: 10.5281/zenodo.10493826
title: Seagrass Carbon Assimilation and Desiccation Rates and Chlorophyll Fluorescence Parameters