cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25625/iypekx/eg1eaa
title: NSDAP-Mitglieder_in_der_MPG-KWG_GSHS.pdf

description: :unav
DOI: 10.25625/iypekx/nwf8nj
title: WM_und_NSDAP-Mitglied_BMS.csv

description: :unav
DOI: 10.25625/iypekx/z5684v
title: 583_Institutionen_-_Max_Planck_Institute_(Dump_für_BioDB-Dossier).tab

description: :unav
DOI: 10.25625/iypekx/7codpj
title: Personen_Gesamtuebersicht.csv

description: :unav
DOI: 10.25625/iypekx/yplgfp
title: Keine_NSDAP-Verbindungen_geb-1880-1927.csv

description: :unav
DOI: 10.25625/iypekx/atckqe
title: Personen_Gesamtuebersicht.xlsx

description: :unav
DOI: 10.25625/iypekx/ek3wy5
title: WM_in_KWG_und_MPG.xlsx

description: :unav
DOI: 10.25625/iypekx/s3yqjs
title: NSDAP-Mitglieder_Zu-Abgaenge_MvA.pdf

description: :unav
DOI: 10.25625/iypekx/7ndqg7
title: 561_Personen_-_Leitungseliten_in_der_MPG.csv

description: :unav
DOI: 10.25625/iypekx/iy3r3b
title: Kontinuität-Diskontinuität_KWG-MPG_MvA.xlsx

description: :unav
DOI: 10.25625/iypekx/tsgcpe
title: wm_mwa_pfm.pdf

description: :unav
DOI: 10.25625/iypekx/ska0zv
title: NSDAP-Organisationen_MPG-KWG.tab

description: :unav
DOI: 10.25625/iypekx/zkvagd
title: 467_Personen_-_Big_Abfrage_II_geburtsorte.tab

description: :unav
DOI: 10.25625/iypekx/qoklkb
title: KWG-MvA_und_NSDAP-Mitglieder_Eintrittsalter_und_-jahr.pdf

description: :unav
DOI: 10.25625/iypekx/l5lsxn
title: wm_mwa_pfm.svg

description: :unav
DOI: 10.25625/iypekx/dlorw8
title: BioDB_Wehrmachts_Beziehungen_Quellen.tab

description: :unav
DOI: 10.25625/iypekx/ztawb3
title: MPG-WM_und_NSDAP-Mitglieder_Eintrittsalter_und_-jahr.pdf

description: :unav
DOI: 10.25625/iypekx/bifoiv
title: wm_mva.pdf

description: :unav
DOI: 10.25625/iypekx/bu6vx3
title: NSDAP-Mitglieder_mit_Eintritt_bevor_20.xlsx

description: :unav
DOI: 10.25625/iypekx/sfpkrc
title: 541_Personen_-_Big_Abfrage_II_mpi.tab

description: :unav
DOI: 10.25625/iypekx/lnhiki
title: 680_alle_WM_MvA_PFM_in_der_KWG_und_MPG.tab

description: :unav
DOI: 10.25625/iypekx/soeidw
title: wm_mva.svg

description: :unav
DOI: 10.25625/iypekx/mirwms
title: NSDAP-Mitglieder_mit_zwei_Eintritten_oder_Austritten.xlsx

description: :unav
DOI: 10.25625/iypekx/vhvbws
title: WM_und_NSDAP-Mitglied_BMS.xlsx

description: :unav
DOI: 10.25625/iypekx/frvgh3
title: 590_Institutionen_-_MPI_und_KWI_mit_Sektion_(Dump_für_BioDB-Dossier).tab

description: :unav
DOI: 10.25625/iypekx/la13u4
title: Wehrmachts_Quellen_Übersicht.csv

description: :unav
DOI: 10.25625/iypekx/g6u1o3
title: Places.tab

description: :unav
DOI: 10.25625/iypekx/naf9z7
title: NSDAP-Verbindungen_MPG-KWG.csv

description: :unav
DOI: 10.25625/iypekx/vzamua
title: fetchCSVfromDatabase.py

description: :unav
DOI: 10.25625/iypekx/pzkbtn
title: gmpg.mplstyle

description: :unav
DOI: 10.25625/iypekx/zk4kf9
title: WM_und_NSDAP-Mitglied_GSHS.csv

description: :unav
DOI: 10.25625/iypekx/juhecn
title: german_municipality_keys.tab

description: :unav
DOI: 10.25625/iypekx/laabj8
title: 464_Personen_-_Big_Abfrage_II_geburtsdaten.tab

description: :unav
DOI: 10.25625/iypekx/qz56p8
title: Wehrmachts_Quellen_Details.csv

description: :unav
DOI: 10.25625/iypekx/qpbhwl
title: wm_unbekannt.xlsx

description: :unav
DOI: 10.25625/iypekx/5dqgzv
title: KWG-WM_und_NSDAP-Mitglieder_Eintrittsalter_und_-jahr.pdf

description: :unav
DOI: 10.25625/iypekx/82oubb
title: NSDAP-Mitglieder_in_der_MPG-KWG.pdf

description: :unav
DOI: 10.25625/iypekx/xtbtwa
title: WM_und_NSDAP-Mitglied_GSHS.xlsx

description: :unav
DOI: 10.25625/iypekx/u4wrn1
title: mva_unbekannt.xlsx

description: :unav
DOI: 10.25625/iypekx/yxzxyu
title: 470_Personen_-_Big_Abfrage_II_mitgliedschaften.tab

description: :unav
DOI: 10.25625/iypekx/rqtpoi
title: MvA_in_KWG_und_MPG.xlsx

description: :unav
DOI: 10.25625/iypekx/hozssl
title: common_plot_tools.py

description: :unav
DOI: 10.25625/iypekx/oyt9xy
title: MPG-MvA_und_NSDAP-Mitglieder_Eintrittsalter_und_-jahr.pdf

description: :unav
DOI: 10.25625/iypekx/1nmsng
title: Difference_1880-1927.xlsx

description: :unav
DOI: 10.25625/iypekx/iixknm
title: BioDB_geb1927.pdf

description: :unav
DOI: 10.25625/iypekx/tajttl
title: NSDAP-Mitglieder_in_der_MPG-KWG_BMS.pdf

description: :unav
DOI: 10.25625/iypekx/9sndjk
title: 463_Big_Abfrage_II_-_BioDB__personen.tab

description: :unav
DOI: 10.25625/iypekx/nn4mig
title: 568_Personen_-_direkte_Cluster-Zuordnung.tab

description: :unav
DOI: 10.25625/iypekx/3l6l4b
title: 567_Personen_-_nsdap_mitglieder.csv

description: :unav
DOI: 10.25625/iypekx/3k2xkj
title: WM_und_NSDAP-Mitglied_CPTS.csv

description: :unav
DOI: 10.25625/iypekx/xn0n6y
title: NSDAP-Mitglieder_Zu-Abgaenge_WM.pdf

description: :unav
DOI: 10.25625/iypekx/ch7k4s
title: NSDAP-Verbindungen_MPG-KWG.xlsx

description: :unav
DOI: 10.25625/iypekx/pphnaq
title: 674_Institutionen_-_Alle_Institutionen_(Dump_für_NS-Dossier).tab

description: :unav
DOI: 10.25625/iypekx/k1kds4
title: 565_Personen_-_nsmitgliedschaftzivil.csv

description: :unav
DOI: 10.25625/iypekx/xyaldx
title: 566_Personen_-_nsmitgliedschaftmilit.csv

description: :unav
DOI: 10.25625/iypekx/hhotvz
title: Kontinuität-Diskontinuität_KWG-MPG_WM.xlsx

description: :unav
DOI: 10.25625/iypekx/vo5mq5
title: NSDAP-Mitglieder_in_der_MPG-KWG_CPTS.pdf

description: :unav
DOI: 10.25625/iypekx/cpgofe
title: 465_Personen_-_Big_Abfrage_II_todesdaten.tab

description: :unav
DOI: 10.25625/iypekx/ffpwsm
title: WM_und_NSDAP-Mitglied_CPTS.xlsx

description: :unav
DOI: 10.25625/iypekx/ojnnom
title: BioDB_geb1927_NSDAP.pdf

description: :unav
DOI: 10.25625/iypekx/j8zpd7
title: NS-Verbindungen_nach_Institution.pdf

description: :unav
DOI: 10.25625/iypekx/jr4hsz
title: NS.ipynb

description: :unav
DOI: 10.25625/iypekx/p7de1d
title: belastung_nach_sektion.ipynb

description: :unav
DOI: 10.25625/iypekx/wi7mko
title: 1928_WM_MvA_PFM.ipynb

description: :unav
DOI: 10.25625/iypekx/ve5cag
title: NS.py

description: :unav
DOI: 10.25625/iypekx
title: Mitgliedschaften der Wissenschaftlichen Mitglieder der Kaiser-Wilhelm-Gesellschaft und der Max-Planck-Gesellschaft in der NSDAP und der SA und der SS

description: Mitgliedschaften der Wissenschaftlichen Mitglieder der Kaiser-Wilhelm-Gesellschaft und der Max-Planck-Gesellschaft in der NSDAP und der SA und der SS
DOI: 10.25380/iastate.24839280
title: Synchrotron diffraction images of San Carlos Olivine deformed in a Rotational Diamond Anvil Cell

description: This data set contains synchrotron diffraction images in TIF format. The images are of San Carlos Olivine minerals compressed and sheared in a newly introduced dynamic rotation diamond anvil cell. The purpose for collecting this data set is trying to induce olivine-spinel phase transformation at room temperature for the first time under severe plastic strain and investigate the strain-induced phase transformation mechanism of olivine. The strain-induced phase transformation of olivine could be the key to solving many enigmas of deep-focus earthquakes.
DOI: 10.5281/zenodo.10032148
title: Demo datasets for the protocol to identify shared transcriptional risks between diseases and compounds predicted to result in mutual benefit

description: We present a computational protocol (https://github.com/ghbore/protocol-cancer-cvd-similarity), implemented as a Snakemake workflow, that was used in previous works (Gao et al., 2022; Baylis et al., 2023). This protocol allows researchers to identify shared transcriptional processes that drive disease and to screen existing compounds for mutual benefit. The protocol also includes a description of the pharmacovigilance study design used to validate the effect of novel compounds using electronic health records, where applicable. This repository bundles the datasets used in previous works as an example to run through the Snakemake workflow. These datasets include the TCGA cancer dataset, the STARNET and BiKE CVD datasets, and other dependent resources.
DOI: 10.5281/zenodo.10032149
title: Demo datasets for the protocol to identify shared transcriptional risks between diseases and compounds predicted to result in mutual benefit

description: We present a computational protocol (https://github.com/ghbore/protocol-cancer-cvd-similarity), implemented as a Snakemake workflow, that was used in previous works (Gao et al., 2022; Baylis et al., 2023). This protocol allows researchers to identify shared transcriptional processes that drive disease and to screen existing compounds for mutual benefit. The protocol also includes a description of the pharmacovigilance study design used to validate the effect of novel compounds using electronic health records, where applicable. This repository bundles the datasets used in previous works as an example to run through the Snakemake workflow. These datasets include the TCGA cancer dataset, the STARNET and BiKE CVD datasets, and other dependent resources.
DOI: 10.5281/zenodo.10417171
title: Demo datasets for the protocol to identify shared transcriptional risks between diseases and compounds predicted to result in mutual benefit

description: We present a computational protocol (https://github.com/ghbore/protocol-cancer-cvd-similarity), implemented as a Snakemake workflow, that was used in previous works (Gao et al., 2022; Baylis et al., 2023). This protocol allows researchers to identify shared transcriptional processes that drive disease and to screen existing compounds for mutual benefit. The protocol also includes a description of the pharmacovigilance study design used to validate the effect of novel compounds using electronic health records, where applicable. This repository bundles the datasets used in previous works as an example to run through the Snakemake workflow. These datasets include the TCGA cancer dataset, the STARNET and BiKE CVD datasets, and other dependent resources.
DOI: 10.6084/m9.figshare.24887991
title: eating disorder and diabetes

description: eating disorder and diabetes
DOI: 10.5281/zenodo.10419913
title: The modeling of cosmogenic Cr isotopes produced in lunar rocks compared with existing calculations and measurements

description: The data in the work 'The modeling of cosmogenic Cr isotopes produced in lunar rocks compared with existing calculations and measurements'
DOI: 10.5281/zenodo.10419914
title: The modeling of cosmogenic Cr isotopes produced in lunar rocks compared with existing calculations and measurements

description: The data in the work 'The modeling of cosmogenic Cr isotopes produced in lunar rocks compared with existing calculations and measurements'
DOI: 10.5281/zenodo.10419934
title: New Evidence on Employee Noncompete, No Poach, and No Hire Agreements in the Franchise Sector

description: This repository holds data, replication files, and a machine learning classifier to detect no poach clauses for "New Evidence on Employee Noncompete, No Poach, and No Hire Agreements in the Franchise Sector," forthcoming at Research in Labor Economics. 

Abstract. This paper presents new evidence on anti-competitive practices in the franchise sector. Drawing from a corpus of Franchise Disclosure Documents (FDDs) filed by 3,716 franchise brands in years 2011- 2023 (partial), I report new information on franchise brands’ use of inter-firm non-solicitation (“no poach”) clauses barring recruitment between firms, no hire clauses barring employment, and franchisor requirements that franchisees use employee non-compete clauses barring workers from joining competitors. Regulatory actions that restricted the enforceability of anti-competitive clauses began to appear in FDDs in 2018. While non-solicitation and no hire clauses have declined in use, the use of non-competes remained stable over time. While prior evidence on anti-competitive practices largely draws from individual complaints, survey data, and limited hand-coded samples, this paper spotlights new methods for finding barriers to worker mobility in large, unstructured text corpora.

The process to create the machine learning classifier from unstructured text is described in "Creating Data from Unstructured Text with Context Rule Assisted Machine Learning (CRAML)" with Stephen Meisenbacher.

Replication materials are released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 

CRAML software is at https://github.com/sjmeis/CRAML_Beta/.

To acccess the text files used, I request you complete a short form. You may also use this form to notify the author of any issues/ questions.  

PDFs uploaded to DocumentCloud are available by searching documentcloud.org: +user:peter-norlander-103369 +access:public .

I am grateful for support from the Economic Security Project Anti-Monopoly Fund; Loyola Rule of Law Institute; Loyola Quinlan School of Business; and Loyola University Chicago. I thank: Stephen Meisenbacher for development of the CRAML software, document collection, and computational assistance; Patricia Tabarani, Chloe Clark, Kayleigh Currier, Zach Nelson, and Damian Orozco for research assistance; Kate Bahn, Michael Lipsitz, Ioana Marinescu, Eric Posner, Todd Sorensen, Evan Starr, Spencer Weber Waller, David Weil, and two anonymous reviewers and the editors for feedback on earlier versions of the manuscript; participants at the 86th Midwest Economic Association Annual Meeting, the 74th Annual Labor and Employment Relations Association, 42nd Annual Strategic Management Society, the 82nd Academy of Management, and at Michigan State University School of Human Resources and Labor Relations. Errors are mine.
DOI: 10.5281/zenodo.10419965
title: New Evidence on Employee Noncompete, No Poach, and No Hire Agreements in the Franchise Sector

description: This repository holds data, replication files, and a machine learning classifier to detect no poach clauses for "New Evidence on Employee Noncompete, No Poach, and No Hire Agreements in the Franchise Sector," forthcoming at Research in Labor Economics. 

Abstract. This paper presents new evidence on anti-competitive practices in the franchise sector. Drawing from a corpus of Franchise Disclosure Documents (FDDs) filed by 3,716 franchise brands in years 2011- 2023 (partial), I report new information on franchise brands’ use of inter-firm non-solicitation (“no poach”) clauses barring recruitment between firms, no hire clauses barring employment, and franchisor requirements that franchisees use employee non-compete clauses barring workers from joining competitors. Regulatory actions that restricted the enforceability of anti-competitive clauses began to appear in FDDs in 2018. While non-solicitation and no hire clauses have declined in use, the use of non-competes remained stable over time. While prior evidence on anti-competitive practices largely draws from individual complaints, survey data, and limited hand-coded samples, this paper spotlights new methods for finding barriers to worker mobility in large, unstructured text corpora.

The process to create the machine learning classifier from unstructured text is described in "Creating Data from Unstructured Text with Context Rule Assisted Machine Learning (CRAML)" with Stephen Meisenbacher.

Replication materials are released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 

CRAML software is at https://github.com/sjmeis/CRAML_Beta/.

To acccess the text files used, I request you complete a short form. You may also use this form to notify the author of any issues/ questions.  

PDFs uploaded to DocumentCloud are available by searching documentcloud.org: +user:peter-norlander-103369 +access:public .

I am grateful for support from the Economic Security Project Anti-Monopoly Fund; Loyola Rule of Law Institute; Loyola Quinlan School of Business; and Loyola University Chicago. I thank: Stephen Meisenbacher for development of the CRAML software, document collection, and computational assistance; Patricia Tabarani, Chloe Clark, Kayleigh Currier, Zach Nelson, and Damian Orozco for research assistance; Kate Bahn, Michael Lipsitz, Ioana Marinescu, Eric Posner, Todd Sorensen, Evan Starr, Spencer Weber Waller, David Weil, and two anonymous reviewers and the editors for feedback on earlier versions of the manuscript; participants at the 86th Midwest Economic Association Annual Meeting, the 74th Annual Labor and Employment Relations Association, 42nd Annual Strategic Management Society, the 82nd Academy of Management, and at Michigan State University School of Human Resources and Labor Relations. Errors are mine.
DOI: 10.5281/zenodo.7454757
title: New Evidence on Employee Noncompete, No Poach, and No Hire Agreements in the Franchise Sector

description: This repository holds data, replication files, and a machine learning classifier to detect no poach clauses for "New Evidence on Employee Noncompete, No Poach, and No Hire Agreements in the Franchise Sector," forthcoming at Research in Labor Economics. 

Abstract. This paper presents new evidence on anti-competitive practices in the franchise sector. Drawing from a corpus of Franchise Disclosure Documents (FDDs) filed by 3,716 franchise brands in years 2011- 2023 (partial), I report new information on franchise brands’ use of inter-firm non-solicitation (“no poach”) clauses barring recruitment between firms, no hire clauses barring employment, and franchisor requirements that franchisees use employee non-compete clauses barring workers from joining competitors. Regulatory actions that restricted the enforceability of anti-competitive clauses began to appear in FDDs in 2018. While non-solicitation and no hire clauses have declined in use, the use of non-competes remained stable over time. While prior evidence on anti-competitive practices largely draws from individual complaints, survey data, and limited hand-coded samples, this paper spotlights new methods for finding barriers to worker mobility in large, unstructured text corpora.

The process to create the machine learning classifier from unstructured text is described in "Creating Data from Unstructured Text with Context Rule Assisted Machine Learning (CRAML)" with Stephen Meisenbacher.

Replication materials are released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 

CRAML software is at https://github.com/sjmeis/CRAML_Beta/.

To acccess the text files used, I request you complete a short form. You may also use this form to notify the author of any issues/ questions.  

PDFs uploaded to DocumentCloud are available by searching documentcloud.org: +user:peter-norlander-103369 +access:public .

I am grateful for support from the Economic Security Project Anti-Monopoly Fund; Loyola Rule of Law Institute; Loyola Quinlan School of Business; and Loyola University Chicago. I thank: Stephen Meisenbacher for development of the CRAML software, document collection, and computational assistance; Patricia Tabarani, Chloe Clark, Kayleigh Currier, Zach Nelson, and Damian Orozco for research assistance; Kate Bahn, Michael Lipsitz, Ioana Marinescu, Eric Posner, Todd Sorensen, Evan Starr, Spencer Weber Waller, David Weil, and two anonymous reviewers and the editors for feedback on earlier versions of the manuscript; participants at the 86th Midwest Economic Association Annual Meeting, the 74th Annual Labor and Employment Relations Association, 42nd Annual Strategic Management Society, the 82nd Academy of Management, and at Michigan State University School of Human Resources and Labor Relations. Errors are mine.
DOI: 10.5281/zenodo.7454758
title: New Evidence on Employee Noncompete, No Poach, and No Hire Agreements in the Franchise Sector

description: This repository holds data, replication files, and a machine learning classifier to detect no poach clauses for "New Evidence on Employee Noncompete, No Poach, and No Hire Agreements in the Franchise Sector," forthcoming at Research in Labor Economics. 

Abstract. This paper presents new evidence on anti-competitive practices in the franchise sector. Drawing from a corpus of Franchise Disclosure Documents (FDDs) filed by 3,716 franchise brands in years 2011- 2023 (partial), I report new information on franchise brands’ use of inter-firm non-solicitation (“no poach”) clauses barring recruitment between firms, no hire clauses barring employment, and franchisor requirements that franchisees use employee non-compete clauses barring workers from joining competitors. Regulatory actions that restricted the enforceability of anti-competitive clauses began to appear in FDDs in 2018. While non-solicitation and no hire clauses have declined in use, the use of non-competes remained stable over time. While prior evidence on anti-competitive practices largely draws from individual complaints, survey data, and limited hand-coded samples, this paper spotlights new methods for finding barriers to worker mobility in large, unstructured text corpora.

The process to create the machine learning classifier from unstructured text is described in "Creating Data from Unstructured Text with Context Rule Assisted Machine Learning (CRAML)" with Stephen Meisenbacher.

Replication materials are released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 

CRAML software is at https://github.com/sjmeis/CRAML_Beta/.

To acccess the text files used, I request you complete a short form. You may also use this form to notify the author of any issues/ questions.  

PDFs uploaded to DocumentCloud are available by searching documentcloud.org: +user:peter-norlander-103369 +access:public .

I am grateful for support from the Economic Security Project Anti-Monopoly Fund; Loyola Rule of Law Institute; Loyola Quinlan School of Business; and Loyola University Chicago. I thank: Stephen Meisenbacher for development of the CRAML software, document collection, and computational assistance; Patricia Tabarani, Chloe Clark, Kayleigh Currier, Zach Nelson, and Damian Orozco for research assistance; Kate Bahn, Michael Lipsitz, Ioana Marinescu, Eric Posner, Todd Sorensen, Evan Starr, Spencer Weber Waller, David Weil, and two anonymous reviewers and the editors for feedback on earlier versions of the manuscript; participants at the 86th Midwest Economic Association Annual Meeting, the 74th Annual Labor and Employment Relations Association, 42nd Annual Strategic Management Society, the 82nd Academy of Management, and at Michigan State University School of Human Resources and Labor Relations. Errors are mine.
DOI: 10.6084/m9.figshare.24888504
title: Supplementary Material for: Cytomorphological assessment in aspirates of ductal carcinoma-in-situ – correlations with histopathologic grade, architectural pattern, and invasion

description: Introduction
Fine-needle aspiration biopsy (FNAB) of the breast is an effective and widely adopted diagnostic technique. Histopathologic grading of ductal carcinoma-in-situ (DCIS) has prognostic significance. In this current study, FNAB of DCIS were reviewed to identify parameters that predict grading, histopathologic architecture, and presence of invasion in DCIS. 

Methods
Aspirates from histopathology-proven cases of DCIS were retrieved and reviewed for cytomorphologic parameters including cellularity, composition, epithelial fragment architecture cellular/nuclear features.

Results
In total 104 aspirates were reviewed. Cytopathologic cellular features – large nuclear size (p = 0.005), prominent nucleoli (p = 0.011), increased nuclear membrane irregularity (p = 0.043), high variation in nuclear size (p = 0.025), and presence of apoptotic figures in epithelial structures (p &lt; 0.001); and background debris (p = 0.033) correlated with a high-grade diagnosis. Cytoplasmic vacuolation (p = 0.034) was seen exclusively in non-high-grade aspirates. Epithelial fragment architecture did not correlate with grading. A predominance (≥ 50%) of solid aggregates and papillary fragments on FNAB correlated with histopathologically solid (p = 0.039, p = 0.005) and papillary (p = 0.029, &lt; p = 0.001) patterns. No parameter showed correlation with invasion. 

Conclusion
FNAB is effective in predicting DCIS grading. Epithelial fragment architecture assessment is limited to papillary or solid-types, and FNAB cannot predict focal invasion in DCIS.