cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12834896.v3
title: Data for geodiv vignette

description: Data for the vignette associated with the geodiv R package. Data may be used to analyze patterns in landscape heterogeneity over Oregon, USA.<br>Data included are as follows:<br><b>(var)_img_450m.tif:</b> texture images (gradient metric applied over moving windows), for the specified metric, applied over a small portion of southwestern Oregon state. The window size used is 450x450m.<br><b>oregon_elevation_SRTM_2000.tif:</b> Shuttle Radar Topography Mission (SRTM) elevation data covering Oregon state for the year 2000. Data were prepared and downloaded in Google Earth Engine in Fall 2019, and are at 30m resolution.<b><br></b><b>oregon_max_growing_season_EVI_2000_2019.tif:</b> Mean maximum growing season MODIS Enhanced Vegetation Index (EVI) data from 2000-2019. Data were prepared and exported from Google Earth Engine in Fall 2019, and are at 250m resolution.<b><br></b><b>elevation_variables.csv:</b> All gradient surface metrics included in the geodiv R package, calculated for 30km moving windows over Oregon state, using a raster of SRTM elevation (above) aggregated to ~2km resolution.<br><b>evi_variables.csv:</b> All gradient surface metrics included in the geodiv R package, calculated for 30km moving windows over Oregon state, using a raster of MODIS EVI (above) aggregated to 2km resolution.<br><br>
DOI: 10.6084/m9.figshare.14312201
title: molecular matrices (cytb and nd4)

description: These two matrices were used to build a concatenated dataset to analyze the phylogenetic relationship within the Bothrops neuwiedi complex.
DOI: 10.6084/m9.figshare.14198984.v10
title: Salmonella Infantis data

description: Population genomics - Salmonella Infantis
DOI: 10.5519/qd.851ywcmn
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (162 records)
DOI: 10.5281/zenodo.4637815
title: Raman spectroscopy of lipid halo and dense-core amyloid plaques: ageing process characterization in the Alzheimer's Disease APPswePS1ΔE9 mouse model

description: The experimental raw data related to work entitled "Raman spectroscopy of lipid halo and dense-core amyloid plaques: ageing process characterization in the Alzheimer’s Disease APPswePS1ΔE9 mouse model"
DOI: 10.5281/zenodo.4637814
title: Raman spectroscopy of lipid halo and dense-core amyloid plaques: ageing process characterization in the Alzheimer's Disease APPswePS1ΔE9 mouse model

description: The experimental raw data related to work entitled "Raman spectroscopy of lipid halo and dense-core amyloid plaques: ageing process characterization in the Alzheimer’s Disease APPswePS1ΔE9 mouse model"
DOI: 10.7910/dvn/1agpvw
title: Locating the Right Sober Living Home after Treatment

description: So, you've finally completed an inpatient or outpatient drug and alcohol program. Congrats! That is the first step in the right direction when looking to change your life for the better after battling addiction. But this is just the first hurdle you have to overcome since addiction recovery is a lifelong process. Without finding a way to avoid and manage triggers of relapse, chances are you will get back to your old habits. Fortunately, this is something you can prevent hassle-free by taking advantage of a transitional housing program to continue your journey to full recovery. While there are many different sober living homes out there, it may prove difficult to separate the good eggs from the bad ones. Either way, this should not deter your quest to turn your transition out of rehab into a rewarding and beneficial experience. Today we will take you through three tips to employ when locating the right sober living home after treatment. Keep reading to find out more Financial Obligation Associated Some people tend to think that sober living communities double up as homes for the homeless. However, that is not the truth since there is a fee you have to pay, just as is the case with renting a house. Before signing on the dotted line and joining a sober living home, be sure to factor in the financial obligation associated with moving into the home. Whereas it may seem overwhelming to think about the finances while focusing on your journey to reaching long-term sobriety, it is well worth it in the end. Use this as the perfect opportunity to compare the fees charged by different sober living homes near you. It's then that you can make an informed decision. Support Services Offered Having a supportive and conducive environment to stay in during your addiction recovery is a critical part of changing your life for the better. However, there is no essence of having all that without the right support services to help you maintain your sobriety. Ensure you enquire about the recovery support services offered by a prospective sober living San Diego home before concluding. Location When looking for the right sober living home after treatment, keep in mind that the location plays a vital role in your final decision. The best way to go about this is by paying a visit to the area and carry out a background check of the home. Ensure the sober living home of choice is in a safe and convenient location. That way, you'll find a home that makes it possible for you to stay close to your support system. Final Thoughts Many factors come into play when looking for the right sober living home in San Diego after treatment. Remember, the best home should always meet your needs no matter the situation. Hopefully, these tips can come in handy when looking to move into a sober living home and continue on your path to sobriety.
DOI: 10.5281/zenodo.4637883
title: Data for Pilot Study: The Impact of Emotional Signals on Percieved Need and Propensity to Help

description: No description provided.
DOI: 10.5281/zenodo.4637881
title: Data for Pilot Study: The Impact of Emotional Signals on Percieved Need and Propensity to Help

description: No description provided.
DOI: 10.5281/zenodo.4637904
title: Data for Depression and suicidality as costly signals of need

description: No description provided.
DOI: 10.5281/zenodo.4637903
title: Data for Depression and suicidality as costly signals of need

description: No description provided.
DOI: 10.5517/ccdc.csd.cc25zjg5
title: CCDC 2025337: Experimental Crystal Structure Determination

description: Related Article: Jiefeng Hu, Xianyu Yang, Shasha Shi, Bo Cheng, Xiaoling Luo, Yu Lan, Teck-Peng Loh|2021|Chemical Science|12|4034|doi:10.1039/D0SC06603F
DOI: 10.5517/ccdc.csd.cc260t9c
title: CCDC 2026572: Experimental Crystal Structure Determination

description: Related Article: Ce Qing Foo, Abdul Sadeer, Yongxin Li, Sumod A. Pullarkat, Pak-Hing Leung|2021|Organometallics|40|682|doi:10.1021/acs.organomet.0c00783
DOI: 10.5517/ccdc.csd.cc26ch8c
title: CCDC 2036832: Experimental Crystal Structure Determination

description: Related Article: Felix Otte, Johannes Kleinheider, Wolf Hiller, Ruimin Wang, Ulli Englert, Carsten Strohmann|2021|J.Am.Chem.Soc.|143|4133|doi:10.1021/jacs.1c00239
DOI: 10.5517/ccdc.csd.cc26ch9d
title: CCDC 2036833: Experimental Crystal Structure Determination

description: Related Article: Felix Otte, Johannes Kleinheider, Wolf Hiller, Ruimin Wang, Ulli Englert, Carsten Strohmann|2021|J.Am.Chem.Soc.|143|4133|doi:10.1021/jacs.1c00239
DOI: 10.5517/ccdc.csd.cc26chbf
title: CCDC 2036834: Experimental Crystal Structure Determination

description: Related Article: Felix Otte, Johannes Kleinheider, Wolf Hiller, Ruimin Wang, Ulli Englert, Carsten Strohmann|2021|J.Am.Chem.Soc.|143|4133|doi:10.1021/jacs.1c00239
DOI: 10.5517/ccdc.csd.cc26chcg
title: CCDC 2036835: Experimental Crystal Structure Determination

description: Related Article: Felix Otte, Johannes Kleinheider, Wolf Hiller, Ruimin Wang, Ulli Englert, Carsten Strohmann|2021|J.Am.Chem.Soc.|143|4133|doi:10.1021/jacs.1c00239
DOI: 10.6084/m9.figshare.14270267.v2
title: SplitFrictionModel Worksheet

description: Worksheet from The Split Friction Model - Introducing Mixed Lubrication to Curling by Martin Ziegler<br>
DOI: 10.25345/c5wn6d
title: MassIVE MSV000087097 - A CANCER UBIQUITOME LANDSCAPE IDENTIFIES METABOLIC REPROGRAMMING AS TARGET OF PARKIN TUMOR SUPPRESSION
title: SILAC-based Global Proteome and diGly antibody enrichment of PC3 Cells Expressing Parkin or Vector
DOI: 10.25919/76yc-xe83
title: Parkes observations for project P1080 semester 2020OCTS_06

description: We propose timing observations of a new pulsar discovered with the MWA. Thanks to the interferometric advantages of the MWA and the unique design of our survey we were able to determine the pulsar's position to &lt;1 arcminute precision, thereby providing an opportunity for accelerated convergence of its timing solution and determine its spin and astrometric parameters. With an estimated luminosity ~0.1 mJy kpc^2 at 1.4 GHz, this may turn out to be a low-luminosity pulsar, of which only a handful are currently known. Observations with the ultra-wide band low-frequency receiver will help us further investigate this in detail through measurements flux densities and spectral index. We will also undertake polarimetric analysis of the new pulsar to better constrain the pulsar's geometry and emission properties.
DOI: 10.3886/e119446v73
title: COVID-19 US State Policy Database

description: For questions or comments about the database please contact:<br>Alexandra Skinner <br>skinnera@bu.edu <br>Research Fellow & Database Manager<br>Department of Health Law, Policy & Management <br>Boston University School of Public Health<br><br>Database of state policies on closures, shelter-in-place orders, housing protections, changes to Medicaid and SNAP, physical distancing closures, reopening, and more created by researchers at the Boston University School of Public Health. Policies included are state-wide directives or mandates, not guidance or recommendations. In order for a policy to be included, it must apply to the entire state. We are working quickly to go through state government websites to make the policy database as complete and accurate as possible in a rapidly changing policy context. If you use data on a given policy, we encourage you to triangulate based on additional sources of policy data and to review the source documentation to consider the coding decisions that are right for your work. Of course, please email us if you note a discrepancy so we can improve the database for everyone. State policy source documentation can be found at: tinyurl.com/statepolicysources.
description: State policies. Smallest Geographic Unit: State
description: other; Contributors: Megan Cole Brahim, Noa Krawczyk, Carolyn Hoffman, Claire Corkish, Elizabeth Ferrara, Elizabeth Long, Emily Baroni, Faith Contador, Hannah Simon, Morgan Simko, Rachel Scheckman, Sarah Brewer, Sue Kulkarni, Felicia Heykoop, Manish Patel, Aishwarya Vidyasagaran, Andrew Chiao, Cara Safon, Samantha Burkhart, Harry Jung, Victor Lee, Marina MacKinnon, Aarohi Rede, Marie Vearling, Sophie Li, Christina Wang, Tim Levengood, María Jordán P. Emma Chistolini
DOI: 10.25345/c5rz2r
title: MassIVE MSV000087098 - Divergent and self-reactive immune responses in the CNS of COVID-19 patients with neurological symptoms
title: COVID-19 patients frequently develop neurological symptoms, but the biological underpinnings of these phenomena are unknown. Through single cell RNAseq and cytokine analyses of CSF and blood from COVID-19 patients with neurological symptoms, we find compartmentalized, CNS specific T cell activation and B cell responses. All COVID-19 cases had CSF anti-SARS-CoV-2 antibodies whose target epitopes diverged from serum antibodies. In an animal model, we find that intrathecal SARS-CoV-2 antibodies are found only during brain infection, and are not elicited by pulmonary infection. We produced CSF-derived monoclonal antibodies from a COVID-19 patient, and find that these mAbs target both anti-viral and anti-neural antigens including one mAb that reacted to both spike protein and neural tissue. Overall, CSF IgG from 4/6 patients contains anti-neural reactivity. This immune survey reveals evidence of a compartmentalized immune response in the CNS of COVID-19 patients and suggests a role for autoimmunity in neurologic sequelae of COVID-19.
DOI: 10.6084/m9.figshare.12611384
title: Probabilistic Flow Covering Problem: Data and Results

description: <b>ohio.csv</b><br>Contains tabular network:a) column 1 = origin node id (integer)<br>b) column 2 = destination node id (integer)<br>c) column 3 = minimum number steps between od pair (a 1.0 indicates a direct connection, the network arcs) (integer)<br>d) column 4 = minimum distance between node pair (double)<br>e) column 5 = truck flow in car equivalents (integer)<br><br><b>shpfiles.7z</b><br>Contains arcs/nodes in .shp gis format:a) interstatenodes.shp - network nodesb) interstatearcs.shp - network arcsc) msabndry - boundary for metropolitan statistical areasd) ohiobndy.shp - boundary for the state of Ohio, USA<br>Results.7zContains:a) optimization input files in .lp format, named beta, lambda, alphab) gurobi log files in .txt format, named beta, lambda, alphac) the selected arcs for optimal solutions, named beta, lambda, alphad) WorkspaceGDB.gdb an arcgis geodatabase containing geographic files for the identified solutions.<br>
DOI: 10.6084/m9.figshare.12611384.v1
title: Probabilistic Flow Covering Problem: Data and Results

description: <b>ohio.csv</b><br>Contains tabular network:a) column 1 = origin node id (integer)<br>b) column 2 = destination node id (integer)<br>c) column 3 = minimum number steps between od pair (a 1.0 indicates a direct connection, the network arcs) (integer)<br>d) column 4 = minimum distance between node pair (double)<br>e) column 5 = truck flow in car equivalents (integer)<br><br><b>shpfiles.7z</b><br>Contains arcs/nodes in .shp gis format:a) interstatenodes.shp - network nodesb) interstatearcs.shp - network arcsc) msabndry - boundary for metropolitan statistical areasd) ohiobndy.shp - boundary for the state of Ohio, USA<br>Results.7zContains:a) optimization input files in .lp format, named beta, lambda, alphab) gurobi log files in .txt format, named beta, lambda, alphac) the selected arcs for optimal solutions, named beta, lambda, alphad) WorkspaceGDB.gdb an arcgis geodatabase containing geographic files for the identified solutions.<br>
DOI: 10.25345/c5n51h
title: MassIVE MSV000087099 - Divergent and self-reactive immune responses in the CNS of COVID-19 patients with neurological symptoms
title: COVID-19 patients frequently develop neurological symptoms, but the biological underpinnings of these phenomena are unknown. Through single cell RNA-seq and cytokine analyses of CSF and blood from COVID-19 patients with neurological symptoms, we find compartmentalized, CNS specific T cell activation and B cell responses. All COVID-19 cases had CSF anti-SARS-CoV-2 antibodies whose target epitopes diverged from serum antibodies. In an animal model, we find that intrathecal SARS-CoV-2 antibodies are found only during brain infection, and are not elicited by pulmonary infection. We produced CSF-derived monoclonal antibodies from a COVID-19 patient, and find that these mAbs target both anti-viral and anti-neural antigens including one mAb that reacted to both spike protein and neural tissue. Overall, CSF IgG from 4/6 patients contains anti-neural reactivity. This immune survey reveals evidence of a compartmentalized immune response in the CNS of COVID-19 patients and suggests a role for autoimmunity in neurologic sequelae of COVID-19
DOI: 10.21227/c808-1y96
title: Load forecasting with mixture of experts

description: Submission 28 January
DOI: 10.5281/zenodo.4624878
title: Grid'5000 performance data

description: Collection of performance data on Grid'5000
DOI: 10.5281/zenodo.4586774
title: Dataset for the paper: Managing Systems Evolving in Space and Time: Four Challenges for Maintenance, Evolution and Composition of Variants

description: The dataset contains ground truth variants and their configurations for all scenarios described in the paper: Managing Systems Evolving in Space and Time: Four Challenges for Maintenance, Evolution and Composition of Variants. The ground truth variants for feature location and composition of variants with features is in the folder named by &lt;systemName&gt;_Variants_With_Features.rar The ground truth for feature revision location and composition of variants with feature revisions is in the folder named by &lt;systemName&gt;_Variants_With_Feature_Revisions.rar The additional scenario is in the folder named by LibSSH_Variants_With_Feature_Revisions_ExtraScenario.rar For every scenario use as input variants the variants inside a folder named by inputConfigurations. For every scenario use as ground truth variants of new configurations the variants inside a folder named by newConfigurations. The scenarios for feature location are split according to the number of variants, therefore the first scenario, for example, has the first variant of the file named by Inputconfigurations.csv. The second scenario must have the first variant + the second variant of the file named by Inputconfigurations.csv. The third scenario must have the first three variants of the file named by Inputconfigurations.csv, and so on, according to the Table 1 in the paper. Regarding composing new configurations with features, use the new configurations in the file Newconfigurations.csv or NewConfigurations.txt and as ground truth variants the variants in the folder newConfigurations. The scenarios for feature revision location are split according to the number of Git commits, therefore the first scenario, for example, has all the variants of the first Git commit in the file named by inputConfigurations.csv. The second scenario must have all the variants from the first 5 Git commits in the file named by inputConfigurations.csv, and so on, according to the Table 2 in the paper. Note: For every line of the file inputConfigurations.csv, the first column represents the number of a Git commit, the second column contains the hash of a Git commit and the following columns contain the feature revisions of a configuration. Regarding composing new configurations with feature revisions, use the new configurations in the file newConfigurations.csv and as ground truth variants the variants in the folder newConfigurations.
DOI: 10.5281/zenodo.4586773
title: Dataset for the paper: Managing Systems Evolving in Space and Time: Four Challenges for Maintenance, Evolution and Composition of Variants

description: The dataset contains ground truth variants and their configurations for all scenarios described in the paper: Managing Systems Evolving in Space and Time: Four Challenges for Maintenance, Evolution and Composition of Variants. The ground truth variants for feature location and composition of variants with features is in the folder named by &lt;systemName&gt;_Variants_With_Features.rar The ground truth for feature revision location and composition of variants with feature revisions is in the folder named by &lt;systemName&gt;_Variants_With_Feature_Revisions.rar The additional scenario is in the folder named by LibSSH_Variants_With_Feature_Revisions_ExtraScenario.rar For every scenario use as input variants the variants inside a folder named by inputConfigurations. For every scenario use as ground truth variants of new configurations the variants inside a folder named by newConfigurations. The scenarios for feature location are split according to the number of variants, therefore the first scenario, for example, has the first variant of the file named by Inputconfigurations.csv. The second scenario must have the first variant + the second variant of the file named by Inputconfigurations.csv. The third scenario must have the first three variants of the file named by Inputconfigurations.csv, and so on, according to the Table 1 in the paper. Regarding composing new configurations with features, use the new configurations in the file Newconfigurations.csv or NewConfigurations.txt and as ground truth variants the variants in the folder newConfigurations. The scenarios for feature revision location are split according to the number of Git commits, therefore the first scenario, for example, has all the variants of the first Git commit in the file named by inputConfigurations.csv. The second scenario must have all the variants from the first 5 Git commits in the file named by inputConfigurations.csv, and so on, according to the Table 2 in the paper. Note: For every line of the file inputConfigurations.csv, the first column represents the number of a Git commit, the second column contains the hash of a Git commit and the following columns contain the feature revisions of a configuration. Regarding composing new configurations with feature revisions, use the new configurations in the file newConfigurations.csv and as ground truth variants the variants in the folder newConfigurations.
DOI: 10.3886/e119446v1-82241
title: COVID-19 US State Policy Database

description: Database of state policies on closures, shelter-in-place orders, housing protections, changes to Medicaid and SNAP, physical distancing closures, reopening, and more created by researchers at the Boston University School of Public Health. Policies included are state-wide directives or mandates, not guidance or recommendations. In order for a policy to be included, it must apply to the entire state. We are working quickly to go through state government websites to make the policy database as complete and accurate as possible in a rapidly changing policy context. If you use data on a given policy, we encourage you to triangulate based on additional sources of policy data and to review the source documentation to consider the coding decisions that are right for your work. Of course, please email us if you note a discrepancy so we can improve the database for everyone. State policy source documentation can be found at: tinyurl.com/statepolicysources.
description: State policies. Smallest Geographic Unit: State
description: other; Contributors: Megan Cole Brahim, Noa Krawczyk, Carolyn Hoffman, Claire Corkish, Elizabeth Ferrara, Elizabeth Long, Emily Baroni, Faith Contador, Hannah Simon, Morgan Simko, Rachel Scheckman, Sarah Brewer, Sue Kulkarni, Felicia Heykoop, Manish Patel, Aishwarya Vidyasagaran, Andrew Chiao, Cara Safon, Samantha Burkhart, Harry Jung, Victor Lee, Marina MacKinnon, Aarohi Rede, Marie Vearling, Sophie Li, Christina Wang, Tim Levengood, María Jordán P. Emma Chistolini
DOI: 10.5517/ccdc.csd.cc26f8tr
title: CCDC 2038554: Experimental Crystal Structure Determination

description: Related Article: Animesh Ghosh, Dániel Csókás, Maja Budanović, Richard D. Webster, Imre Pápai, Mihaiela C. Stuparu|2021|Chemical Science|12|3977|doi:10.1039/D0SC06730J
DOI: 10.5517/ccdc.csd.cc26fryc
title: CCDC 2039023: Experimental Crystal Structure Determination

description: Related Article: Elishua D. Litle, Lewis C. Wilkins, François P. Gabbaï|2021|Chemical Science|12|3929|doi:10.1039/D0SC05777K
DOI: 10.5517/ccdc.csd.cc26fs0g
title: CCDC 2039025: Experimental Crystal Structure Determination

description: Related Article: Elishua D. Litle, Lewis C. Wilkins, François P. Gabbaï|2021|Chemical Science|12|3929|doi:10.1039/D0SC05777K
DOI: 10.5281/zenodo.4632200
title: Calibration data and analyses

description: This repository contains nearly all the data and notebooks that were used throughout my PhD thesis.
DOI: 10.5517/ccdc.csd.cc26fs3k
title: CCDC 2039028: Experimental Crystal Structure Determination

description: Related Article: Elishua D. Litle, Lewis C. Wilkins, François P. Gabbaï|2021|Chemical Science|12|3929|doi:10.1039/D0SC05777K
DOI: 10.5517/ccdc.csd.cc26fs5m
title: CCDC 2039030: Experimental Crystal Structure Determination

description: Related Article: Elishua D. Litle, Lewis C. Wilkins, François P. Gabbaï|2021|Chemical Science|12|3929|doi:10.1039/D0SC05777K
DOI: 10.5281/zenodo.4628248
title: Calibration data and analyses

description: This repository contains nearly all the data and notebooks that were used throughout my PhD thesis.
DOI: 10.5517/ccdc.csd.cc26fs6n
title: CCDC 2039031: Experimental Crystal Structure Determination

description: Related Article: Elishua D. Litle, Lewis C. Wilkins, François P. Gabbaï|2021|Chemical Science|12|3929|doi:10.1039/D0SC05777K