cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5930p58q
title: MassIVE MSV000093876 - Ho_BLM_TOP3A_RMI_BioID_P130_Velos_Elite
title: This dataset consists of 25 raw MS files and associated peak lists and results files, acquired on a Thermo LTQ Velos Pro or Thermo Orbitrap Elite mass spectrometer operated in Data Dependent Acquisition mode. Samples were generated by Edith Cheng. Affinity purifications and mass spectrometry acquisition was performed by Wade Dunham. Analysis was performed by Jennifer Ho, Wade Dunham, Edith Cheng, Cassandra Wong, Anne-Claude Gingras, and Grant Brown. The files are associated with a manuscript submitted for publication by Jung Jennifer Ho et al. The main goal of this paper was to profile the proximity interactome of BLM-TOP3A-RMI1-RMI2 (BTRR) complex to identify proteins that suppress recombination in DNA repair. This dataset identifies the importance of RAD54L2 for the recruitment of Bloom (BLM) helicase to promote non-crossover recombination. Grant W. Brown is the corresponding author of the manuscript (grant.brown@utoronto.ca); Anne-Claude Gingras should be contacted for questions on this dataset (gingras@lunenfeld.ca)
DOI: 10.18120/6ky4-k298
title: Subproject
DOI: 10.18120/qn5b-0s17
title: Test
DOI: 10.5281/zenodo.7996499
title: The datasets for "The Cosmos in its Infancy: JADES Galaxy Candidates at z > 8 in GOODS-S and GOODS-N"

description: The JADES dataset of z > 8 galaxies and galaxy candidates accompanying the paper "The Cosmos in its Infancy: JADES Galaxy Candidates at z > 8 in GOODS-S and GOODS-N" (Hainline et al.). This paper was accepted in ApJ, January 2024. We also include the EAZY template set used for these fits. Please see the README files for descriptions of the datasets.
DOI: 10.6084/m9.figshare.24756095
title: Copula Graphical Models for Heterogeneous Mixed Data

description: This article proposes a graphical model that handles mixed-type, multi-group data. The motivation for such a model originates from real-world observational data, which often contain groups of samples obtained under heterogeneous conditions in space and time, potentially resulting in differences in network structure among groups. Therefore, the iid assumption is unrealistic, and fitting a single graphical model on all data results in a network that does not accurately represent the between group differences. In addition, real-world observational data is typically of mixed discrete-and-continuous type, violating the Gaussian assumption that is typical of graphical models, which leads to the model being unable to adequately recover the underlying graph structure. Both these problems are solved by fitting a different graph for each group, applying the fused group penalty to fuse similar graphs together and by treating the observed data as transformed latent Gaussian data, respectively. The proposed model outperforms related models on learning partial correlations in a simulation study. Finally, the proposed model is applied on real on-farm maize yield data, showcasing the added value of the proposed method in generating new production-ecological hypotheses. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=heteromixgm. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.25008248
title: Ben Sira Ms B

description: Mansucript B of Ben Sira
DOI: 10.6084/m9.figshare.24756095.v1
title: Copula Graphical Models for Heterogeneous Mixed Data

description: This article proposes a graphical model that handles mixed-type, multi-group data. The motivation for such a model originates from real-world observational data, which often contain groups of samples obtained under heterogeneous conditions in space and time, potentially resulting in differences in network structure among groups. Therefore, the i.i.d. assumption is unrealistic, and fitting a single graphical model on all data results in a network that does not accurately represent the between group differences. In addition, real-world observational data is typically of mixed discrete-and-continuous type, violating the Gaussian assumption that is typical of graphical models, which leads to the model being unable to adequately recover the underlying graph structure. Both these problems are solved by fitting a different graph for each group, applying the fused group penalty to fuse similar graphs together and by treating the observed data as transformed latent Gaussian data, respectively. The proposed model outperforms related models on learning partial correlations in a simulation study. Finally, the proposed model is applied on real on-farm maize yield data, showcasing the added value of the proposed method in generating new production-ecological hypotheses. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=heteromixgm. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.24756095.v2
title: Copula Graphical Models for Heterogeneous Mixed Data

description: This article proposes a graphical model that handles mixed-type, multi-group data. The motivation for such a model originates from real-world observational data, which often contain groups of samples obtained under heterogeneous conditions in space and time, potentially resulting in differences in network structure among groups. Therefore, the iid assumption is unrealistic, and fitting a single graphical model on all data results in a network that does not accurately represent the between group differences. In addition, real-world observational data is typically of mixed discrete-and-continuous type, violating the Gaussian assumption that is typical of graphical models, which leads to the model being unable to adequately recover the underlying graph structure. Both these problems are solved by fitting a different graph for each group, applying the fused group penalty to fuse similar graphs together and by treating the observed data as transformed latent Gaussian data, respectively. The proposed model outperforms related models on learning partial correlations in a simulation study. Finally, the proposed model is applied on real on-farm maize yield data, showcasing the added value of the proposed method in generating new production-ecological hypotheses. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=heteromixgm. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.25008248.v1
title: Ben Sira Ms B

description: Mansucript B of Ben Sira
DOI: 10.6084/m9.figshare.25007294
title: Merged NCES Academic Library Survey Data 1996 - 2018

description: This data set consists of data on academic libraries in the United States from 1996-2018. Multiple data sources from the National Center for Education Statistics (NCES) were merged. File Format: csvFile Size: 26648 KBLanguage: EnglishCoverage: Time period covered: 1996-2018.<b>Codebook</b>: See the codebook for Merged NCES Academic Library Survey 1996 - 2016 Dataset, which is included as a supplemental file here: https://digitalscholarship.unlv.edu/lib_datasets/1/<b>Sources: </b>All data sources were downloaded from the National Center for Education Statistics website https://nces.ed.gov/. Individual datasets and years accessed are listed below.[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries component, Integrated Postsecondary Education Data System (IPEDS), (2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries Survey (ALS) Public Use Data File, Library Statistics Program, (2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/surveys/libraries/aca_data.asp[dataset] U.S. Department of Education, National Center for Education Statistics, Institutional Characteristics component, Integrated Postsecondary Education Data System (IPEDS), (2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Enrollment component, Integrated Postsecondary Education Data System (IPEDS), (2018, 2016, 2014, 2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Human Resources component, Integrated Postsecondary Education Data System (IPEDS), (2018, 2016, 2014, 2012, 2010, 2008, 2006), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Employees Assigned by Position component, Integrated Postsecondary Education Data System (IPEDS), (2004, 2002), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Staff component, Integrated Postsecondary Education Data System (IPEDS), (1999, 1997, 1995), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7
DOI: 10.25810/w4fz-yx96
title: Amazon Japan (amazon.jp) Secondhand Book Price Trends

description: This dataset is a summary of book prices collected from Amazon Japan (amazon.jp) in the Fall of 2022. The books in this dataset are from a donation the University of Colorado Libraries recieved. The dataset includes 329 Japanese titles across 3,282 books. Data on the books includes it's ISBN, title (in Japanese and Romanized), author, publisher, date of publication, price, shipping cost, name of the Amazon.jp seller, and a description of the item provided by the seller.

This Excel file includes various tables and charts to explore the data and review trends.
DOI: 10.3886/e194748v1
title: ECIN Replication Package for "Growth at Risk From Climate Change"

description: How will climate change affect risks to economic activity? Research on climate impacts has tended to focus on effects on the average level of economic growth. I examine whether climate change may make severe contractions in economic activity more likely using quantile regressions linking growth to temperature. The effects of temperature on downside risks to economic growth are large and robust across specifications. These results suggest the growth at risk from climate change is large—climate change may make economic contractions more likely and severe and thereby significantly impact economic and financial stability and welfare. <br><br><br>The views expressed herein are those of the author, and do not reflect those of the Federal Reserve Board or its staff. <br><br><br><br>
DOI: 10.3886/e194748v2
title: ECIN Replication Package for "Growth at Risk From Climate Change"

description: How will climate change affect
risks to economic activity? Research on
climate impacts has tended to focus on effects on the average level of economic
growth. I examine whether climate change
may make severe contractions in economic activity more likely using quantile
regressions linking growth to temperature. 
The effects of temperature on downside risks to economic growth are
large and robust across specifications. 
These results suggest the growth at risk from climate change is large—climate
change may make economic contractions more likely and severe and thereby
significantly impact economic and financial stability and welfare. 

<br><br><br>The
views expressed herein are those of the author, and do not reflect those of the
Federal Reserve Board or its staff. <br><br><br><br>
DOI: 10.3886/e194748
title: ECIN Replication Package for "Growth at Risk From Climate Change"

description: How will climate change affect
risks to economic activity? Research on
climate impacts has tended to focus on effects on the average level of economic
growth. I examine whether climate change
may make severe contractions in economic activity more likely using quantile
regressions linking growth to temperature. 
The effects of temperature on downside risks to economic growth are
large and robust across specifications. 
These results suggest the growth at risk from climate change is large—climate
change may make economic contractions more likely and severe and thereby
significantly impact economic and financial stability and welfare. 

<br><br><br>The
views expressed herein are those of the author, and do not reflect those of the
Federal Reserve Board or its staff. <br><br><br><br>
DOI: 10.3886/e194748v3
title: ECIN Replication Package for "Growth at Risk From Climate Change"

description: How will climate change affect
risks to economic activity? Research on
climate impacts has tended to focus on effects on the average level of economic
growth. I examine whether climate change
may make severe contractions in economic activity more likely using quantile
regressions linking growth to temperature. 
The effects of temperature on downside risks to economic growth are
large and robust across specifications. 
These results suggest the growth at risk from climate change is large—climate
change may make economic contractions more likely and severe and thereby
significantly impact economic and financial stability and welfare. 

<br><br><br>The
views expressed herein are those of the author, and do not reflect those of the
Federal Reserve Board or its staff. <br><br><br><br>
DOI: 10.5066/p9r5pqfn
title: Screening geochemistry, gas chromatography, and solid bitumen reflectance data in the Bakken petroleum system, Williston Basin, USA

description: Thirty-two organic-rich samples from the lower and upper shale members of the Devonian–Mississippian Bakken Formation were collected from eight cores across the Williston Basin, USA, at depths (~7,575–11,330 ft) representing immature through post peak oil/early condensate thermal maturity conditions. Reflectance results were correlated to programmed temperature pyrolysis parameters [hydrogen index (HI), production index (PI), Tmax], normal hydrocarbon and isoprenoid analysis of extractable organic matter (pristane/n-C17, phytane/n-C18) from GC analysis, and peak ratios from FTIR spectroscopy (branching ratio, A-factor).
DOI: 10.6084/m9.figshare.25007105
title: MGB Model v2.0 for São Francisco River Basin

description: Input files for the MGB model, adjusted for simulation of the São Francisco river basin, Brazil, from 1980 to 2000.
DOI: 10.6084/m9.figshare.25007105.v1
title: MGB Model v2.0 for São Francisco River Basin

description: Input files for the MGB model, adjusted for simulation of the São Francisco river basin, Brazil, from 1980 to 2000.
DOI: 10.18739/a2599z35k
title: Multispectral Orthomosaic Image for Upper Nome River (MP31) Beaver Ponds, Seward Peninsula, Alaska, 04 August 2021

description: Emergence of beavers as ecosystem engineers in the New Arctic project focuses on establishing field sites at tundra beaver ponds to study the implications of beaver engineering on hydrology and permafrost. Drones are being used to collect baseline data and track beaver dam building and pond evolution over time. This dataset consists of a multispectral orthomosaic derived from drone surveys on 04 August 2021 at the Upper Nome River, MP31, site on the Seward Peninsula, Alaska. 1500 digital images from five spectral bands were acquired from a DJI Phantom 4 Real-Time Kinematic (DJI P4RTK) quadcopter with a DJI D-RTK 2 Mobile Base Station. The mapped area was around 25 hectares (ha). The drone system was flown at 120 meters (m) above ground level (agl) and images were captured using the hover and capture at point mode. The orientation of the camera was set to 90 degrees (i.e. looking straight down). The along-track overlap and across-track overlap of the mission were set at 80% and 70%, respectively. All images were processed in the software Pix4D Mapper (v. 4.6.4) using the standard 3D Maps workflow and the accurate geolocation and orientation calibration method to produce the multispectral orthophoto mosaic at a spatial resolution of 10 centimeters (cm). Images of a MicaSense calibrated reflectance panel were used for radiometric processing and calibration of each spectral band in the Index Calculator in Pix4D. A Leica Viva differential global positioning system (GPS) provided ground control for the mission and the data were post-processed to WGS84 UTM Zone 3 North.
DOI: 10.5517/ccdc.csd.cc2ddg2c
title: CCDC 2216502: Experimental Crystal Structure Determination

description: Related Article: Zhuo Yang, Jinshan Wang, Aimin Li, Chao Wang, Wei Ji, Elísabet Pires, Wenzhong Yang, Su Jing|2024|RSC Advances|14|1488|doi:10.1039/D3RA07390D
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.cc2fb925
title: CCDC 2244216: Experimental Crystal Structure Determination

description: Related Article: Andrei Andreichenko, Aurélien R. Willauer, Till Schertenleib, Antoine Curé, Rosario Scopelliti, Farzaneh Fadaei-Tirani, Wendy L. Queen, Anne-Sophie Chauvin, Marinella Mazzanti|2023|Inorg.Chem.|62|7982|doi:10.1021/acs.inorgchem.3c00881
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.cc2fb947
title: CCDC 2244218: Experimental Crystal Structure Determination

description: Related Article: Andrei Andreichenko, Aurélien R. Willauer, Till Schertenleib, Antoine Curé, Rosario Scopelliti, Farzaneh Fadaei-Tirani, Wendy L. Queen, Anne-Sophie Chauvin, Marinella Mazzanti|2023|Inorg.Chem.|62|7982|doi:10.1021/acs.inorgchem.3c00881
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.cc2frfg3
title: CCDC 2256845: Experimental Crystal Structure Determination

description: Related Article: Farshid Hajibabaei, Samaneh Sanei Movafagh, Sadegh Salehzadeh, Robert William Gable|2024|Dalton Trans.|53|1066|doi:10.1039/D3DT03281G
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.cc2frfk6
title: CCDC 2256848: Experimental Crystal Structure Determination

description: Related Article: Farshid Hajibabaei, Samaneh Sanei Movafagh, Sadegh Salehzadeh, Robert William Gable|2024|Dalton Trans.|53|1066|doi:10.1039/D3DT03281G
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.cc2ftvpt
title: CCDC 2259177: Experimental Crystal Structure Determination

description: Related Article: Priyanka Sahu, Narayan Ch. Jana, Sahadev Barik, Hemanta K. Kisan, Avtar Changotra, Anvarhusein A. Isab, Joydev Dinda|2024|Dalton Trans.|53|1099|doi:10.1039/D3DT02551A
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.cc2ftvqv
title: CCDC 2259178: Experimental Crystal Structure Determination

description: Related Article: Priyanka Sahu, Narayan Ch. Jana, Sahadev Barik, Hemanta K. Kisan, Avtar Changotra, Anvarhusein A. Isab, Joydev Dinda|2024|Dalton Trans.|53|1099|doi:10.1039/D3DT02551A
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: 10.6084/m9.figshare.25008431
title: Seasonal flow forecasts 2001-2018

description: Seasonal flow forecasts for the São Francisco river basin, based on climate forecasts from the Eta model and simulation from the MGB hydrological model.
DOI: 10.6084/m9.figshare.25008431.v1
title: Seasonal flow forecasts 2001-2018

description: Seasonal flow forecasts for the São Francisco river basin, based on climate forecasts from the Eta model and simulation from the MGB hydrological model.
DOI: 10.5281/zenodo.10519823
title: Intransitivity in plant-soil feedbacks is rare but is associated with multispecies coexistence

description: R and Mathematica scripts for "Intransitivity in plant-soil feedbacks is rare but is associated with multispecies coexistence"

This respository contains code accompanying the manuscript "Intransitivity in plant-soil feedbacks is rare but is associated with multispecies coexistence" by Pajares-Murgó et al.  The file *data.csv* contains the data obtained in the experiment of PSF. The dataset is divided by columns: Tray indicates each 15-cell plastic tray, ISO (individual soil sources) indicates the soil beneath 6 adult individuals of each species, ISE (individual seed sources) indicates the seeds collected from 5 adult individual of each species, CANOPY_soil and RECRUIT indicate the plant species, and success is the binomial variable that indicates the the proportion of experimental cells with soil of species j containing a seedling of species i at the end of the experiment.  The file *recruitment_density.csv* contains the data on the recruitment of plant species observed in the field. The dataset is divided by columns: CANOPY and RECRUIT indicate the plant species, Fij indicates the frequency of recruitment between pairs of species, Eij indicates the efficiency of recruitment between pairs of species (estimated as the number of saplings of species i recruiting under species j divided by the cover of the canopy and recruit species), cover_can indicates the cover of canopy species and cover_rec indicates the cover of recruit species.  The files *1. Testing for species-specific PSFs*, *2. Field crossvalidation*, *Pairwise-level feedback* and *Community-level feedback* contain an implementation of the models described in the Main Text, suitable for numerical integration, along with other useful functions for generating figures and analysis in the Main Text and figures and tables in the Supplementary Information.   Please email mpajares@ujaen.es or jmalcan@ujaen.es with any questions.